• Applied Chemistry for Engineering
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• v.32 no.5
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• pp.562-568
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• 2021

An O/W (oil in water) emulsion, wheat germ oil raw material, was produced by using natural wheat germ oil and composite sugar-ester. The effects of variables such as the hydrophile-lipophile balance (HLB) value, added emulsifier amount, and emulsification time on the average particle size, emulsification viscosity and ESI of O/W wheat germ oil emulsion were investigated. The parameters of the emulsification process produced by the central composite design model of the response surface methodology (CCD-RSM), which is a reaction surface analysis method, were simulated and optimized. The optimum process conditions obtained from this paper for the production of O/W wheat germ oil emulsion were 8.4, 6.4 wt%, 25.4 min for the HLB value, amount of emulsifier, and emulsion time, respectively. The predicted reaction values by CCD-RSM model under the optimum conditions were 206 nm, 8125 cP, and 98.2% for mean droplet size (MDS), viscosity, and ESI, respectively, based on the emulsion after 7 days. The MDS, viscosity and ESI of the emulsion obtained from actual experiments were 209 nm, 7974 cP and 98.7%, respectively. Therefore, it was possible to design an optimization process for evaluating the stability of the emulsion of wheat germ oil raw material by CCD-RSM.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.4
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• pp.591-598
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• 2021

The digital phase-locked loops(DPLL) is a circuit used for phase synchronization and has been generally used in various fields such as communication and circuit fields. State estimators are used to design digital phase-locked loops, and infinite impulse response state estimators such as the well-known Kalman filter have been used. In general, the performance of the infinite impulse response state estimator-based digital phase-locked loop is excellent, but a sudden performance degradation may occur in unexpected situations such as inaccuracy of initial value, model error, and disturbance. In this paper, we propose a minimum variance finite impulse response filter with optimal horizon for designing a new digital phase-locked loop. A numerical method is introduced to obtain the measured value interval length, which is an important parameter of the proposed finite impulse response filter, and to obtain a gain, the covariance matrix of the error is set as a cost function, and a linear matrix inequality is used to minimize it. In order to verify the superiority and robustness of the proposed digital phase-locked loop, a simulation was performed for comparison and analysis with the existing method in a situation where noise information was inaccurate.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.12 no.2
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• pp.135-141
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• 2019

Internet of thing becomes more active, many sensor devices are increasing. Sensors can use network wired network or use mobile communication network. From the viewpoint of the transmission rate, the mobile communication network can be roughly divided into two types of high-speed communication and low-speed communication. In the case of hundreds of millions of sensors in the mobile communication network, resources are wasted to use high-speed communication. Communication is required to reduce the transmission rate and appropriately allocate resources without wasting such resources. As the Internet of Thing has been activated, Narrowband Internet of Thing(NB-IoT), which is one of the low-power technologies in recent mobile communications, is in the spotlight from various companies. Currently, it can be seen that only NB-IoT or other low power consumption communication has the potential to be able to connect to the Internet with rapidly increasing sensor devices. In this paper, we designed and implemented a heater controller using Huawei NB-IoT communication Module, a server that collects controller information, and an application that allows default settings for devices. The main function of this system is to collect temperature and heater status and give it to the server, control the heater from the server, and set parameters for the heater to operate automatically. The system can be applied to places where wired communication is not established, such as road information, smart agriculture, and small reservoirs as well as heaters.

• Applied Chemistry for Engineering
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• v.32 no.3
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• pp.283-289
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• 2021

Adsorption characteristics of carbol fuchsin (CF) dye by coal-based activated carbon (CAC) were investigated using pH, initial concentration, temperature and contact time as adsorption variables. CF dissociates in water to have a cation, NH₂⁺, which is bonded to the negatively charged surface of the activated carbon in the basic region by electrostatic attraction. Under the optimum condition of pH 11, 96.6% of the initial concentration was adsorbed. Isothermal adsorption behavior was analyzed using Langmuir, Freundlich, Temkin and Dubinin-Radushkevich models. Langmuir's equation was the best fit for the experimental results. Therefore, the adsorption mechanism was expected to be adsorbed as a monolayer on the surface of activated carbon with a uniform energy distribution. From the evaluated Langmuir's dimensionless separation coefficients (R_L = 0.503~0.672), it was found that CF can be effectively treated by activated carbon. The adsorption energies determined by Temkin and Dubinin-Radushkevich models were E = 15.31~7.12 J/mol and B = 0.223~0.365 kJ/mol, respectively. Therefore, the adsorption process was physical (E < 20 J/mol, B < 8 kJ/mol). The experimental result of adsorption kinetics fit better the pseudo second order model. In the adsorption reaction of CF dye to CAC, the negative free energy change increased as the temperature increased. It was found that the spontaneity also increased with increasing temperature. The positive enthalpy change (40.09 kJ/mol) indicated an endothermic reaction.

• Journal of Korean Society of Forest Science
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• v.111 no.2
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• pp.287-301
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• 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 Internet Computing and Services
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• v.23 no.4
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• pp.45-55
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• 2022

The size of the cryptocurrency market is growing. For example, market capitalization of bitcoin exceeded 500 trillion won. Accordingly, many studies have been conducted to predict the price of cryptocurrency, and most of them have similar methodology of predicting stock prices. However, unlike stock price predictions, machine learning become best model in cryptocurrency price predictions, conceptually cryptocurrency has no passive income from ownership, and statistically, cryptocurrency has at least three times higher liquidity than stocks. Thats why we argue that a methodology different from stock price prediction should be applied to cryptocurrency price prediction studies. We propose Reverse Walk-forward Validation (RWFV), which modifies Walk-forward Validation (WFV). Unlike WFV, RWFV measures accuracy for Validation by pinning the Validation dataset directly in front of the Test dataset in time series, and gradually increasing the size of the Training dataset in front of it in time series. Train data were cut according to the size of the Train dataset with the highest accuracy among all measured Validation accuracy, and then combined with Validation data to measure the accuracy of the Test data. Logistic regression analysis and Support Vector Machine (SVM) were used as the analysis model, and various algorithms and parameters such as L1, L2, rbf, and poly were applied for the reliability of our proposed RWFV. As a result, it was confirmed that all analysis models showed improved accuracy compared to existing studies, and on average, the accuracy increased by 1.23%p. This is a significant improvement in accuracy, given that most of the accuracy of cryptocurrency price prediction remains between 50% and 60% through previous studies.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.4
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• pp.49-57
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• 2022

The government is making great efforts to prevent crimes such as missing children by using public CCTVs. However, there is a shortage of operating manpower, weakening of concentration due to long-term concentration, and difficulty in tracking. In addition, applying real-time object search, re-identification, and tracking through a deep learning algorithm showed a phenomenon of increased parameters and insufficient memory for speed reduction due to complex network analysis. In this paper, we designed the network to improve speed and save memory through the application of Yolo v4, which can recognize real-time objects, and the application of Batch and TensorRT technology. In this thesis, based on the research on these advanced algorithms, OSNet re-ranking and K-reciprocal nearest neighbor for re-identification, Jaccard distance dissimilarity measurement algorithm for correlation, etc. are developed and used in the solution of CCTV national safety identification and tracking system. As a result, we propose a solution that can track objects by recognizing and re-identification objects in real-time within situation of a Korean public multi-CCTV environment through a set of algorithm combinations.

• The Journal of Engineering Geology
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• v.32 no.4
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• pp.597-610
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• 2022

Knowledge of the failure behavior of friction materials considering their intermediate principal stress is related to an understanding of situations where these materials might be used: for example, the stability of deep-seated boreholes and fault slip analysis. This study designed equipment for physically implementing true triaxial compression and used it to assess specimens of plaster, a friction material. The material's mechanical behaviors are discussed based on the results. The applicability of the 3D failure criteria are also reviewed. The tested specimens were molded cuboids of width, length, and height 52, 52, and 104 mm, respectively. A total of 24 true triaxial compression tests were performed under various combinations of 𝜎₃ and 𝜎₂ conditions. Conventional uniaxial and triaxial compression tests were employed to estimate the mechanical properties of the plaster for use as parameters for 3D failure criteria. Examining the stress-strain relations of the plaster materials showed that a large difference between the intermediate principal stress and the minimum principal stress indicated strong brittle behavior. The mechanical behavior of the plaster used here reflects the change of intermediate principal stress. Nonlinear multiple regression analysis on the test data in the principal space showed that the modified Wiebols-Cook failure criterion and the modified Lade failure criterion were the most suitable 3D failure criteria for the tested plaster.

• Smart Media Journal
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• v.11 no.10
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• pp.65-75
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• 2022

Deep learning is used as a creative tool that could overcome the limitations of existing analysis models and generate various types of results such as text, image, and music. In this paper, we propose a method necessary to preprocess audio data using the Niko's MIDI Pack sound source file as a data set and to generate music using Bi-LSTM. Based on the generated root note, the hidden layers are composed of multi-layers to create a new note suitable for the musical composition, and an attention mechanism is applied to the output gate of the decoder to apply the weight of the factors that affect the data input from the encoder. Setting variables such as loss function and optimization method are applied as parameters for improving the LSTM model. The proposed model is a multi-channel Bi-LSTM with attention that applies notes pitch generated from separating treble clef and bass clef, length of notes, rests, length of rests, and chords to improve the efficiency and prediction of MIDI deep learning process. The results of the learning generate a sound that matches the development of music scale distinct from noise, and we are aiming to contribute to generating a harmonistic stable music.

• Journal of Korea Society of Industrial Information Systems
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• v.29 no.3
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• pp.1-12
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• 2024

In this paper, we fine-tuned KoAlpaca, a large-scale Korean language model, and implemented a blog text generation system utilizing it. Blogs on social media platforms are widely used as a marketing tool for businesses. We constructed training data of positive reviews through emotion analysis and refinement of collected sponsored blog texts and applied QLoRA for the lightweight training of KoAlpaca. QLoRA is a fine-tuning approach that significantly reduces the memory usage required for training, with experiments in an environment with a parameter size of 12.8B showing up to a 58.8% decrease in memory usage compared to LoRA. To evaluate the generative performance of the fine-tuned model, texts generated from 100 inputs not included in the training data produced on average more than twice the number of words compared to the pre-trained model, with texts of positive sentiment also appearing more than twice as often. In a survey conducted for qualitative evaluation of generative performance, responses indicated that the fine-tuned model's generated outputs were more relevant to the given topics on average 77.5% of the time. This demonstrates that the positive review generation language model for sponsored content in this paper can enhance the efficiency of time management for content creation and ensure consistent marketing effects. However, to reduce the generation of content that deviates from the category of positive reviews due to elements of the pre-trained model, we plan to proceed with fine-tuning using the augmentation of training data.