• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.15 no.2
• /
• pp.425-431
• /
• 2011

The harmonic distortion level may be significant in electric propulsion systems, as the main loads usually are variable speed propulsion/thruster drives. Distortion of currents and supply voltage waveforms may lead to: Increased power dissipation(losses) in equipment connected to the network, such as generators, motors, transformers, cables, etc., from the harmonic currents, may cause overheating and deterioration of the insulation, and reduced life time of the equipment. In this paper introduced the canceling method of harmonic currents by a multipulse drive with phase shifting transformer. The simulation results indicated a good speed response to the middle speed range of electric propulsion motor. And also, THD(total harmonic distortion) and torque ripple could be reduced in comparing the 12-pulse drive with 6-pulse drive.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.12 no.8
• /
• pp.1505-1510
• /
• 2008

In large-scale wireless sensor networks, the deployed nodes cannot be replaced or recharged after first deployment. Also, dead nodes maγ lead to the partition of whole networks. While performing data dissemination under a battery power constraint, energy efficiency is a key design factor of routing protocol. As a solution for the efficient data dissemination, in this paper, we propose a protocol namely Hierarchical Data Dissemination (HDD) which provides scalable and efficient data delivery to multiple sources and mobile sinks. HDD uses the facts that sink nodes are central gathering Points and source-centric data forwarding paths are constructed and it is maintained with two-tier communications. The performance of HDD is compared with TTDD about the energy consumption, data delivery time and data success ration. The extensive simulation results show that HDD Routing Protocol outperforms TIDD by more than $1.5{\sim}3times$ on energy consumption.

• Journal of the Korea Society of Computer and Information
• /
• v.26 no.6
• /
• pp.1-8
• /
• 2021

In this paper, we propose reinforced VGG style network structure for low performance embedded system to classify low resolution infrared image. The combination of reinforced VGG style network structure and global average pooling makes lower computational complexity and higher accuracy. The proposed method classify the synthesize image which have 9 class 3,723,328ea images made from OKTAL-SE tool. The reinforced VGG style network structure composed of 4 filters on input and 16 filters on output from max pooling layer shows about 34% lower computational complexity and about 2.4% higher accuracy then the first parameter minimized network structure made for embedded system composed of 8 filters on input and 8 filters on output from max pooling layer. Finally we get 96.1% accuracy model. Additionally we confirmed the about 31% lower inference lead time in ported C code.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.17 no.1
• /
• pp.33-39
• /
• 2007

Finite field multipliers are the basic building blocks in many applications such as error-control coding, cryptography and digital signal processing. Hence, the design of efficient dedicated finite field multiplier architectures can lead to dramatic improvement on the overall system performance. In this paper, a new bit serial structure for a multiplier with low latency in Galois field is presented. To speed up multiplication processing, we divide the product polynomial into several parts and then process them in parallel. The proposed multiplier operates standard basis of $GF(2^m)$ and is faster than bit serial ones but with lower area complexity than bit parallel ones. The most significant feature of the proposed architecture is that a trade-off between hardware complexity and delay time can be achieved.

• Korean Journal of Clinical Laboratory Science
• /
• v.53 no.1
• /
• pp.131-136
• /
• 2021

Duplex sonography is used widely in various medical fields because of its repeatability and low cost. In particular, the carotid duplex sonography is a useful non-invasive test for diagnosing cerebrovascular disease and predicting the prognosis. In clinical practice, it is very important to reduce the test time and improve accuracy. The patient's clinical information must be known in advance to perform carotid duplex sonography quickly and accurately. Despite this, there are often difficulties finding new cervical vascular diseases that are not mentioned in the clinical information. Therefore, knowing a variety of cases can lead to fast and accurate results. In this context, this paper reports three cases of cervical vascular disease discovered unexpectedly during carotid duplex sonography: CASE 1, internal carotid artery occlusion and cerebral arteries branched from the external carotid artery; CASE 2, internal jugular vein thrombosis; CASE 3, microembolism observed in the vertebral artery.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.30 no.6
• /
• pp.1053-1065
• /
• 2020

With the recent development of hardware performance and artificial intelligence technology, sophisticated fake videos that are difficult to distinguish with the human's eye are increasing. Face synthesis technology using artificial intelligence is called Deepfake, and anyone with a little programming skill and deep learning knowledge can produce sophisticated fake videos using Deepfake. A number of indiscriminate fake videos has been increased significantly, which may lead to problems such as privacy violations, fake news and fraud. Therefore, it is necessary to detect fake video clips that cannot be discriminated by a human eyes. Thus, in this paper, we propose a deep-fake detection model applied with Bidirectional Convolution LSTM and Attention Module. Unlike LSTM, which considers only the forward sequential procedure, the model proposed in this paper uses the reverse order procedure. The Attention Module is used with a Convolutional neural network model to use the characteristics of each frame for extraction. Experiments have shown that the model proposed has 93.5% accuracy and AUC is up to 50% higher than the results of pre-existing studies.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.24 no.2
• /
• pp.192-197
• /
• 2020

Multivariate processes, such as large scale power plants or chemical processes are operated in very hazardous environment, which can lead to significant human and material losses if a fault occurs. On-line monitoring technology, therefore, is essential to detect system faults. In this paper, the ICA-based fault detection method is conducted using three different multivariate process data. Fault detection procedure based on ICA is divided into off-line and on-line processes. The off-line process determines a threshold for fault detection by using the obtained dataset when the system is normal. And the on-line process computes statistics of query vectors measured in real-time. The fault is detected by comparing computed statistics and previously defined threshold. For comparison, the PCA-based fault detection method is also implemented in this paper. Experimental results show that the ICA-based fault detection method detects the system faults earlier and better than the PCA-based method.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.24 no.10
• /
• pp.1348-1354
• /
• 2020

The purpose of this study is to discover the occupational identity by examining the narrative of the life of a TVET teacher using the project method in programming fields. Teacher S, a participant in the study, started as a part-time lecturer at university and has been teaching programs for the past 20 years. As a result, six kinds of occupational identities of a programming teacher who teach by project method were found. S experienced pride and pride as a teacher through the project method. The project method served as a motive for continuous self-development to lead S to an expert. His experience as a positive teacher made him wish to maintain this life and live as a teacher as long as his health permits, even after retirement. This study has significance in exploring the structure of the occupational identity of a TVET teacher using the project method in programming fields, which have not been studied yet.

• The Journal of Information Systems
• /
• v.30 no.4
• /
• pp.21-42
• /
• 2021

Purpose In order for companies to continue to grow, they should properly manage human resources, which are the core of corporate competitiveness. Employee turnover means the loss of talent in the workforce. When an employee voluntarily leaves his or her company, it will lose hiring and training cost and lead to the withdrawal of key personnel and new costs to train a new employee. From an employee's viewpoint, moving to another company is also risky because it can be time consuming and costly. Therefore, in order to reduce the social and economic costs caused by employee turnover, it is necessary to accurately predict employee turnover intention, identify the factors affecting employee turnover, and manage them appropriately in the company. Design/methodology/approach Prior studies have mainly used logistic regression and decision trees, which have explanatory power but poor predictive accuracy. In order to develop a more accurate prediction model, XGBoost is proposed as the classification technique. Then, to compensate for the lack of explainability, SHAP, one of the XAI techniques, is applied. As a result, the prediction accuracy of the proposed model is improved compared to the conventional methods such as LOGIT and Decision Trees. By applying SHAP to the proposed model, the factors affecting the overall employee turnover intention as well as a specific sample's turnover intention are identified. Findings Experimental results show that the prediction accuracy of XGBoost is superior to that of logistic regression and decision trees. Using SHAP, we find that jobseeking, annuity, eng_test, comm_temp, seti_dev, seti_money, equl_ablt, and sati_safe significantly affect overall employee turnover intention. In addition, it is confirmed that the factors affecting an individual's turnover intention are more diverse. Our research findings imply that companies should adopt a personalized approach for each employee in order to effectively prevent his or her turnover.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.26 no.1
• /
• pp.15-23
• /
• 2022

Among the learning methods for Continuous Learning environments, "Learning without Forgetting" has fixed regularization strengths, which can lead to poor performance in environments where various data are received. We suggest a way to set weights variable by identifying the features of the data we want to learn. We applied weights adaptively using correlation and complexity. Scenarios with various data are used for evaluation and experiments showed accuracy increases by up to 5% in the new task and up to 11% in the previous task. In addition, it was found that the adaptive weight value obtained by the algorithm proposed in this paper, approached the optimal weight value calculated manually by repeated experiments for each experimental scenario. The correlation coefficient value is 0.739, and overall average task accuracy increased. It can be seen that the method of this paper sets an appropriate lambda value every time a new task is learned, and derives the optimal result value in various scenarios.