• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.19 no.2
• /
• pp.187-192
• /
• 2019

Hybrid power storage system with emergency power function for demand management and power outage minimizes the investment cost in the building of buildings and factories requiring emergency power generation facilities, We propose a new business model by developing technology that can secure economical efficiency by reducing power cost at all times. Normally, system power is supplied to load through STS (Static Transfer Switch), and PCS is connected to system in parallel to perform demand management. In order to efficiently operate the electric power through demand forecasting, the EMS issues a charge / discharge command to the ESS as a PMS (Power Management System), and the PMS transmits the command to the PCS controller to operate the system. During the power outage, the STS is rapidly disengaged from the system, and the PCS becomes an independent power supply and can supply constant voltage / constant frequency power to the load side. Therefore, it is possible to secure reliability through verification of actual system linkage and independent operation performance of hybrid ESS, By enabling low-carbon green growth technology to operate in conjunction with an efficient grid, it is possible to improve irregular power quality and contribute to peak load by generating renewable energy through ESS linkage. In addition, the ESS is replacing the frequency follow-up reserve, which is currently under the charge of coal-fired power generation, and thus it is anticipated that the operation cost of the LNG generator with high fuel cost can be reduced.

• Journal of the Korea Society of Computer and Information
• /
• v.26 no.6
• /
• pp.73-80
• /
• 2021

In this paper, we propose a packet processing method capable of handling large-capacity traffic over 20Gbps using multi-core and huge page memory approaches. As ICT technology advances, the global average monthly traffic is expected to reach 396 exabytes by 2022. With the increase in network traffic, cyber threats are also increasing, increasing the importance of traffic analysis. Traffic analyzed as an existing high-cost foreign product simply stores statistical data and visually shows it. Network administrators introduce and analyze many traffic analysis systems to analyze traffic in various sections, but they cannot check the aggregated traffic of the entire network. In addition, since most of the existing equipment is of the 10Gbps class, it cannot handle the increasing traffic every year at a fast speed. In this paper, as a method of processing large-capacity traffic over 20Gbps, the process of processing raw packets without copying from single-core and basic SMA memory approaches to high-performance packet reception, packet detection, and statistics using multi-core and NUMA memory approaches suggest When using the proposed method, it was confirmed that more than 50% of the traffic was processed compared to the existing equipment.

• Science of Emotion and Sensibility
• /
• v.24 no.1
• /
• pp.91-104
• /
• 2021

This study explored users' emotional states identified from the wake-up words -"Hi, KIA!"- using a machine learning algorithm considering the user interface of passenger cars' voice. We targeted four emotional states, namely, excited, angry, desperate, and neutral, and created a total of 12 emotional scenarios in the context of car driving. Nine college students participated and recorded sentences as guided in the visualized scenario. The wake-up words were extracted from whole sentences, resulting in two data sets. We used the soundgen package and svmRadial method of caret package in open source-based R code to collect acoustic features of the recorded voices and performed machine learning-based analysis to determine the predictability of the modeled algorithm. We compared the accuracy of wake-up words (60.19%: 22%~81%) with that of whole sentences (41.51%) for all nine participants in relation to the four emotional categories. Accuracy and sensitivity performance of individual differences were noticeable, while the selected features were relatively constant. This study provides empirical evidence regarding the potential application of the wake-up words in the practice of emotion-driven user experience in communication between users and the artificial intelligence system.

• Journal of the Korean Recycled Construction Resources Institute
• /
• v.9 no.3
• /
• pp.303-310
• /
• 2021

Cracks in bridges are important factors that indicate the condition of bridges and should be monitored periodically. However, a visual inspection conducted by a human expert has problems in cost, time, and reliability. Therefore, in recent years, researches to apply a deep learning model are started to be conducted. Deep learning requires sufficient data on the situations to be predicted, but bridge crack data is relatively difficult to obtain. In particular, it is difficult to collect a large amount of crack data in a specific situation because the shape of bridge cracks may vary depending on the bridge's design, location, and construction method. This study developed a crack detection model that generates and trains insufficient crack data through a Generative Adversarial Network. GAN successfully generated data statistically similar to the given crack data, and accordingly, crack detection was possible with about 3% higher accuracy when using the generated image than when the generated image was not used. This approach is expected to effectively improve the performance of the detection model as it is applied when crack detection on bridges is required, though there is not enough data, also when there is relatively little or much data f or one class.

• Journal of Internet Computing and Services
• /
• v.22 no.4
• /
• pp.1-11
• /
• 2021

As the Internet developed, various and complex cyber attacks began to emerge. Various detection systems were used outside the network to defend against attacks, but systems and studies to detect attackers inside were remarkably rare, causing great problems because they could not detect attackers inside. To solve this problem, studies on the lateral movement detection system that tracks and detects the attacker's movements have begun to emerge. Especially, the method of using the Remote Desktop Protocol (RDP) is simple but shows very good results. Nevertheless, previous studies did not consider the effects and relationships of each logon host itself, and the features presented also provided very low results in some models. There was also a problem that the model could not explain why it predicts that way, which resulted in reliability and robustness problems of the model. To address this problem, this study proposes an interpretable RDP-based lateral movement detection system using page rank algorithm and SHAP(Shapley Additive Explanations). Using page rank algorithms and various statistical techniques, we create features that can be used in various models and we provide explanations for model prediction using SHAP. In this study, we generated features that show higher performance in most models than previous studies and explained them using SHAP.

• Journal of the Korea Convergence Society
• /
• v.12 no.1
• /
• pp.99-104
• /
• 2021

In this paper, we propose health risk management using feature extraction and cluster analysis considering time flow. The proposed method proceeds in three steps. The first is the pre-processing and feature extraction step. It collects user's lifelog using a wearable device, removes incomplete data, errors, noise, and contradictory data, and processes missing values. Then, for feature extraction, important variables are selected through principal component analysis, and data similar to the relationship between the data are classified through correlation coefficient and covariance. In order to analyze the features extracted from the lifelog, dynamic clustering is performed through the K-means algorithm in consideration of the passage of time. The new data is clustered through the similarity distance measurement method based on the increment of the sum of squared errors. Next is to extract information about the cluster by considering the passage of time. Therefore, using the health decision-making system through feature clusters, risks able to managed through factors such as physical characteristics, lifestyle habits, disease status, health care event occurrence risk, and predictability. The performance evaluation compares the proposed method using Precision, Recall, and F-measure with the fuzzy and kernel-based clustering. As a result of the evaluation, the proposed method is excellently evaluated. Therefore, through the proposed method, it is possible to accurately predict and appropriately manage the user's potential health risk by using the similarity with the patient.

• Composites Research
• /
• v.34 no.1
• /
• pp.8-15
• /
• 2021

In the shipbuilding and marine industry, as a technology for reducing the weight of parts to reduce energy and improve operational efficiency of ships is required, a method of applying fibers-reinforced composites which is high-strength lightweight materials, as part materials can be considered. In this study, the possibility of applying fibers-reinforced composites to the pipe support clamps was evaluated to reduce the weight of LNGC. The fibers-reinforced composites were manufactured using carbon fibers and glass fibers as reinforcing fibers. Through the computer simulation program, the properties of the reinforcing materials and the matrix materials of the composites were inversely calculated, and the performance prediction was performed according to the change in the properties of each fiber lamination pattern. In addition, the structural analysis of the clamps according to the thickness of the composites was performed through the finite element analysis program. As a result of the study, it was confirmed that attention is needed in selecting the thickness when applying the fibers-reinforced composites of the clamp for weight reduction. It is considered that it will be easy to change the shape of the structure and change the structure for weight reduction in future supplementary design.

• Journal of the Computational Structural Engineering Institute of Korea
• /
• v.33 no.6
• /
• pp.383-390
• /
• 2020

Since the dynamic behaviors of liquid storage tanks on flexible soil are significantly influenced by the fluid-structure-soil interaction (FSSI), its effects must be rigorously considered for accurate earthquake analysis and seismic design of the storage system. In this study, dynamic analysis is performed for a rectangular liquid storage tank on flexible soil, and its dynamic characteristics are examined by rigorously considering the effects of FSSI. The hydrodynamic force and the interaction force between the structure and soil are evaluated using the finite-element approach. In the evaluations, mid-point integrated finite elements and viscous dampers are considered for energy radiation into the infinite soil. The effective earthquake force is then obtained from free-field analysis. It is thus demonstrated that the earthquake responses of the rectangular liquid storage tank on flexible soil are significantly influenced by the FSSI.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.11 no.5
• /
• pp.17-30
• /
• 2001

In this paper, we implemented a scalar multiplier needed at an elliptic curve cryptosystem over standard basis in $GF(2^{163})$. The scalar multiplier consists of a radix-16 finite field serial multiplier and a finite field inverter with some control logics. The main contribution is to develop a new fast finite field inverter, which made it possible to avoid time consuming iterations of finite field multiplication. We used an algorithmic transformation technique to obtain a data-independent computational structure of the Extended Euclid GCD algorithm. The finite field multiplier and inverter shown in this paper have regular structure so that they can be easily extended to larger word size. Moreover they can achieve 100% throughput using the pipelining. Our new scalar multiplier is synthesized using Hyundai Electronics 0.6$\mu\textrm{m}$ CMOS library, and maximum operating frequency is estimated about 140MHz. The resulting data processing performance is 64Kbps, that is it takes 2.53ms to process a 163-bit data frame. We assure that this performance is enough to be used for digital signature, encryption & decryption and key exchange in real time embedded-processor environments.

• Journal of the Korea institute for structural maintenance and inspection
• /
• v.25 no.1
• /
• pp.75-84
• /
• 2021

In this study, to evaluate deterioration propagation life and deterioration curve of the shop painted and field re-painted steel bridges, accelerated corrosion tests were carried out on 4 types of heavy-duty painting systems with different surface treatments. The surface treatments prior to painting were examined by hand tool(SSPC SP-2), power tool(SP-3,) or blast cleaning(SP-10) considering shop painting and field re-painting. The paint deterioration curves for each painting system and surface treatment were evaluated based on corrosion propagation from the initial paint defects. From the test results, the paint deterioration life of shop painted and field re-painted system was evaluated and compared by using corrosivity categories and durability performance evaluation of structural steel. The deterioration propagation life of shop and field paint was estimated in 18 to 21 years and 5.3 to 8.0 years with atmospheric corrosion category C4.