• Korean Chemical Engineering Research
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• v.59 no.2
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• pp.191-199
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• 2021

A distillation column, which is a main facility of the chemical process, separates the desired product from a mixture by using the difference of boiling points. The distillation process requires the optimization and the prediction of operation because it consumes much energy. The target process of this study is difficult to operate efficiently because the composition of feed flow is not steady according to the supplier. To deal with this problem, we could develop a data-driven model to predict operating conditions. However, data preprocessing is essential to improve the predictive performance of the model because the raw data contains outlier and noise. In this study, after optimizing the predictive model based long-short term memory (LSTM) and Random forest (RF), we used a low-pass filter and one-class support vector machine for data preprocessing and compared predictive performance according to the method and range of the preprocessing. The performance of the predictive model and the effect of the preprocessing is compared by using R2 and RMSE. In the case of LSTM, R2 increased from 0.791 to 0.977 by 23.5%, and RMSE decreased from 0.132 to 0.029 by 78.0%. In the case of RF, R2 increased from 0.767 to 0.938 by 22.3%, and RMSE decreased from 0.140 to 0.050 by 64.3%.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.6
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• pp.460-468
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• 2021

This study investigated the enhanced combustion efficiency of an "air-cooled combustion system" with single F.D. fan, and performed a numerical analysis for the operation and design conditions to increase the combustion efficiency. The combustion efficiency in an actual combustor was compared before and after the structure modification. Numerical analysis for application of a single fan revealed the difficulty of forming a turbulence for circular combustion conditions. This is because the supply ratio of combustion air supplied into 2 flow paths becomes irregular in the combustion furnace due to a change in friction force and pressure in each flow path. Subsequently, two methods of supplying air into the combustion furnace were analyzed numerically to obtain the optimal combustion conditions of an air-cooled combustion system. The first method involved injecting the preheated combustion air after a 180~360 degree rotation from the outer wall, whereas in the second method, the combustion air was injected into the combustion furnace in a tangential direction after primary heat exchange outside the combustion furnace, by applying a rotatable vane structure in the combustion furnace. Results reveal that application of a single F.D. fan to the air injection into a rotatable combustion furnace is desirable for optimization of the combustion conditions for applying a duct structure having a dual cooling wall for the cooling of the outer wall of the combustion furnace, and for maintaining perfect mixing in the combustion furnace. We therefore confirmed enhanced combustion efficiency by comparing the actual combustion efficiency before and after structure modification.

• Journal of the Korea Convergence Society
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• v.12 no.8
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• pp.31-37
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• 2021

As the size of buildings increases due to urbanization due to the development of industry, the need to purify the air and maintain a comfortable indoor environment is also increasing. With the development of monitoring technology for refrigeration systems, it has become possible to manage the amount of electricity consumed in buildings. In particular, refrigeration systems account for about 40% of power consumption in commercial buildings. Therefore, in order to develop the refrigeration system failure diagnosis algorithm in this study, the purpose of this study was to understand the structure of the refrigeration system, collect and analyze data generated during the operation of the refrigeration system, and quickly detect and classify failure situations with various types and severity . In particular, in order to improve the classification accuracy of failure types that are difficult to classify, a three-step diagnosis and classification algorithm was developed and proposed. A model based on SVM and LGBM was presented as a classification model suitable for each stage after a number of experiments and hyper-parameter optimization process. In this study, the characteristics affecting failure were preserved as much as possible, and all failure types, including refrigerant-related failures, which had been difficult in previous studies, were derived with excellent results.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.50 no.3
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• pp.181-192
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• 2022

Active vibration control system(AVCS) for helicopter enables to control the vibration generated from the main rotor and has the superb vibration reduction performance with low weight compared passive vibration reduction device. In this paper, FxLMS algorithm-based vibration control software of the light civil helicopter tansmits the control command calculated using the signals of the tachometer and accelerometers to the circular force generator(CFG) is developed and verified. According to the RTCA DO-178C/DO-331, the vibration control software is developed through the model based design technique, and real-time operation performance is evaluated in PILS(processor in-the loop simulation) and HILS(hardware in-the loop simulation) environments. In particular, the reliability of the software is improved through the LDRA-based verification coverage in the PIL environments. In order to AVCS to light civil helicopter(LCH), the dynamic response characteristic model is obtained through the ground/flight tests. AVCS configuration which exhibits the optimal performance is determined using system optimization analysis and flight test and obtain STC certification.

• Journal of Navigation and Port Research
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• v.46 no.3
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• pp.280-288
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• 2022

This study examined the diagnostics of abnormalities and faults of equipment, whose rotational speed changes even during regular operation. The purpose of this study was to suggest a procedure that can properly apply machine learning to the time series data, comprising non-stationary characteristics as the rotational speed changes. Anomaly and fault diagnosis was performed using machine learning: k-Nearest Neighbor (k-NN), Support Vector Machine (SVM), and Random Forest. To compare the diagnostic accuracy, an autoencoder was used for anomaly detection and a convolution based Conv1D was additionally used for fault diagnosis. Feature vectors comprising statistical and frequency attributes were extracted, and normalization & dimensional reduction were applied to the extracted feature vectors. Changes in the diagnostic accuracy of machine learning according to feature selection, normalization, and dimensional reduction are explained. The hyperparameter optimization process and the layered structure are also described for each algorithm. Finally, results show that machine learning can accurately diagnose the failure of a variable-rotation machine under the appropriate feature treatment, although the convolution algorithms have been widely applied to the considered problem.

• Journal of IKEEE
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• v.26 no.4
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• pp.736-741
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• 2022

Lattice-based cryptography is the most practical post-quantum cryptography because it enjoys strong worst-case security, relatively efficient implementation, and simplicity. Ring learning with errors (R-LWE) is a public key encryption (PKE) method of lattice-based encryption (LBC), and the most important operation of R-LWE is the modular polynomial multiplication of rings. This paper proposes a method for optimizing modular multipliers based on approximate computing (AC) technology, targeting the medium-security parameter set of the R-LWE cryptosystem. First, as a simple way to implement complex logic, LUT is used to omit some of the approximate multiplication operations, and the 2's complement method is used to calculate the number of bits whose value is 1 when converting the value of the input data to binary. We propose a total of two methods to reduce the number of required adders by minimizing them. The proposed LUT-based modular multiplier reduced both speed and area by 9% compared to the existing R-LWE modular multiplier, and the modular multiplier using the 2's complement method reduced the area by 40% and improved the speed by 2%. appear. Finally, the area of the optimized modular multiplier with both of these methods applied was reduced by up to 43% compared to the previous one, and the speed was reduced by up to 10%.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.1
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• pp.45-52
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• 2023

The power generation method using expensive diesel has operation problems such as high cost diesel generator and a lack of reserved power due to increase of power demand in some islands, requiring expansion of power generation facilities. To solve this problems, it is necessary to improve the efficiency of power generation facilities through an ORC(Organic Rankin Cycle) system application that uses waste heat as a heat source. Therefore, localized application technology of price competitive and highly reliable ORC power generation system is needed, and optimization technology of generators is having great effect, so this study performed two generator designs to get a high-efficiency generator with an optimized 30kW output. The comparison of simulation data for two designed models showed that a generator with SPM factor of 46.2% had an efficiency of 92.1% and a power ouput of about 23.2kW based on 12,000rpm, a generator with SPM factor of 44.46%, had a power output of 27.9kW and efficiency of 93.6% based on above rpm. For the verification of improved design model with SPM factor of 44.46%, the prototype test system with 110kW motor dynamometer was installed and got to the efficiency of 92.08% with conditions of the rated capacity 25kW at 12,000rpm, the test results of prototype generator showed the validity of generator design.

• Journal of Korea Water Resources Association
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• v.55 no.spc1
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• pp.1211-1222
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• 2022

Abnormal condition inevitably occurs during operation of water distribution system (WDS) and requires the isolation of certain areas using isolation valves. In general, the determination of the optimal location of isolation valves considered minimization of hydraulic failures as isolation of certain areas causes a change in hydraulic states (e.g., flow direction, velocity, pressure, etc.). Water quality failure can also be induced by changes in hydraulics, which have not been considered for isolation valve system design. Therefore, this study proposes a new isolation valve system design methodology to prevent unexpected water quality failure events. The new methodology considers flow direction change ratio (FDCR), which accounts for flow direction changes after isolation of the area, as a constraint while reliability is used as the objective function. The optimal design model has been applied to a synthetic grid network and the results are compared with the traditional design approach. Results show that considering FDCR can eliminate flow direction changes while average pressure and coefficient of variation of pressure, velocity, and hydraulic geodesic index (HGI) outperform compared to the traditional design approach. The proposed methodology is expected to be a useful approach to minimizing unexpected consequences by traditional design approaches.

• Nuclear Engineering and Technology
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• v.55 no.1
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• pp.339-352
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• 2023

Prediction of the time-related traits of pebble flow inside pebble-bed HTGRs is of great significance for reactor operation and design. In this work, an image-driven approach with the aid of a convolutional neural network (CNN) is proposed to predict the remaining time of initially loaded pebbles and the time interval of paired flow images of the pebble bed. Two types of strategies are put forward: one is adding FC layers to the classic classification CNN models and using regression training, and the other is CNN-based deep expectation (DEX) by regarding the time prediction as a deep classification task followed by softmax expected value refinements. The current dataset is obtained from the discrete element method (DEM) simulations. Results show that the CNN-aided models generally make satisfactory predictions on the remaining time with the determination coefficient larger than 0.99. Among these models, the VGG19+DEX performs the best and its CumScore (proportion of test set with prediction error within 0.5s) can reach 0.939. Besides, the remaining time of additional test sets and new cases can also be well predicted, indicating good generalization ability of the model. In the task of predicting the time interval of image pairs, the VGG19+DEX model has also generated satisfactory results. Particularly, the trained model, with promising generalization ability, has demonstrated great potential in accurately and instantaneously predicting the traits of interest, without the need for additional computational intensive DEM simulations. Nevertheless, the issues of data diversity and model optimization need to be improved to achieve the full potential of the CNN-aided prediction tool.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.1D
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• pp.17-23
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• 2009

Due to the rapid increase of vehicles on the street, Korean society is facing worsening traffic congestions and air pollutions. Also, the oil price pickup has led to increasing need for the use of public transportation. In particular, transfering among public transportation may be a main factor for riders who are commuting for a long distance journey. In order to ensure such connectivity, transfer stations have been actively built in Korea. However, it would be necessary to shift those vehicles, from cars to public transportations by enhancing the users' satisfaction with public transportation through strategies for minimizing the users' waiting cost by building an efficient connective system between transportation modes as well as the preparation of aforementioned transfer stations. Therefore, this study aimed to develop an algorithm for minimizing transferring passengers' waiting costs based on service intervals of linked buses within the transfer facilities. In order to adjust the service interval, we calculated the total costs, involving the wait cost of transfer passengers and bus operation costs, and produced an allocation interval, that would minimize the costs. We selected a KTX departing from Seoul station, and a No. 6014 bus route in Gwangmyeong city where it starts from the Gwangmyeong station in order to for verifying the model. Then, the transfer passengers' total waitting cost was reduced equivalent to the maximum of 212 minutes, and it revealed that the model performed very effectively.