• Journal of Drive and Control
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• v.13 no.1
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• pp.1-9
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• 2016

Nowadays automatic transmission equipped vehicles prevail in construction and agricultural equipment due to their convenience in driving and operation. Though domestic vehicle manufacturers install imported electronic controlled transmissions at present, overseas products will be replaced by domestic ones in the near future owing to development efforts over the past 10 years. For passenger cars, there are many kinds of shift control algorithms that enhance the shift quality such as feedback and learning control. However, since shift control technologies for heavy duty vehicles are not highly developed, it is possible to improve the shift quality with an organized control method. A feedback control algorithm for neutral-into-gear shift, which is enabled during the inertia phase for the master clutch slip speed to track the slip speed reference, is proposed based on the power transmission structure of TH100. The performance of the feedback shift control is verified by a vehicle test which is implemented with firmware embedded TCU. As the master clutch engages along the predetermined speed trajectory, it can be concluded that the shift quality can be managed by a shift time control parameter. By extending the proposed feedback algorithm for neutral-into-gear shift to gear change and shuttle shift, it is expected that the quality of the shift can be improved.

• Journal of the Korea Institute of Building Construction
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• v.21 no.5
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• pp.397-408
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• 2021

The construction industry has the highest occupational accidents/injuries and has experienced the most fatalities among entire industries. Korean government installed surveillance camera systems at construction sites to reduce occupational accident rates. Construction safety managers are monitoring potential hazards at the sites through surveillance system; however, the human capability of monitoring surveillance system with their own eyes has critical issues. A long-time monitoring surveillance system causes high physical fatigue and has limitations in grasping all accidents in real-time. Therefore, this study aims to build a deep learning-based safety monitoring system that can obtain information on the recognition, location, identification of workers and heavy equipment in the construction sites by applying multiple object tracking with instance segmentation. To evaluate the system's performance, we utilized the Microsoft common objects in context and the multiple object tracking challenge metrics. These results prove that it is optimal for efficiently automating monitoring surveillance system task at construction sites.

• Journal of Digital Convergence
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• v.19 no.12
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• pp.51-62
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• 2021

This study developed the factors capable of systematic/comprehensive evaluation of the task performance in order to strengthen the performance management of the professional career personnel invitation program (PCPIP). To this end, a performance evaluation framework was developed by analyzing existing project evaluation studies based on boundary theory and Kirkpatrick's four-level evaluation model. Afterwords, through two Delphi surveys, evaluation factors that can measure performance in terms of individual and invitation institutions of PCP were derived and validated. With this procedure, five evaluation factors were finally selected: adaptability, connectivity, clarity, compatibility, and expandability. This study has implications suggesting a performance evaluation factors capable of hybrid quantitative/qualitative evaluation for the performance management of PCPIP operated by National Research Foundation of Korea Research since 1994.

• KIPS Transactions on Computer and Communication Systems
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• v.8 no.6
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• pp.151-158
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• 2019

FLUSH+RELOAD attack exposes the most serious security threat among cache side channel attacks due to its high resolution and low noise. This attack is exploited by a variety of malicious programs that attempt to leak sensitive information. In order to prevent such information leakage, it is necessary to detect FLUSH+RELOAD attack in real time. In this paper, we propose a novel run-time detection technique for FLUSH+RELOAD attack by utilizing PCM (Performance Counter Monitor) of processors. For this, we conducted four kinds of experiments to observe the variation of each counter value of PCM during the execution of the attack. As a result, we found that it is possible to detect the attack by exploiting three kinds of important factors. Then, we constructed a detection algorithm based on the experimental results. Our algorithm utilizes machine learning techniques including a logistic regression and ANN(Artificial Neural Network) to learn from different execution environments. Evaluation shows that the algorithm successfully detects all kinds of attacks with relatively low false rate.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.1
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• pp.1-8
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• 2019

Among the methods for extracting the most appropriate information from a large amount of log data, there is a method using inductive inference. In this paper, we use SVM (Support Vector Machine), which is an excellent classification method for inductive inference, in order to determine the ranking of intrusion logs in digital forensic analysis. For this purpose, the logs of the training log set are classified into intrusion logs and normal logs. The associated words are extracted from each classified set to generate a related word dictionary, and each log is expressed as a vector based on the generated dictionary. Next, the logs are learned using the SVM. We classify test logs into normal logs and intrusion logs by using the log set extracted through learning. Finally, the recommendation orders of intrusion logs are determined to recommend intrusion logs to the forensic analyst.

• The Journal of the Korean dental association
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• v.56 no.11
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• pp.631-640
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• 2018

Nowadays computer-guided "flapless" surgery for implant placement using templates is gaining popularity among clinicians and patients. The advantages of this surgical protocol are its minimally invasive nature, accuracy of implant placement, predictability, less post-surgical discomfort and reduced time required for definitive rehabilitation. Aim of this study is to describe the digital implant protocol, thanks to which is now possible to do a mini-invasive static guided implant surgery. This is possible thanks to a procedure named surface mapping based on the matching between numerous points on the surface of patient's dental casts and the corresponding anatomical surface points in the CBCT data. With some critical points and needing an adequate learning curve, this protocol allows to select the ideal implant position in depth, inclination and mesio-distal distance between natural teeth and or other implants enabling a very safe and predictable rehabilitation compared with conventional surgery. It represents a good tool for the best compromise between anatomy, function and aesthetic, able to guarantee better results in all clinical situations.

• The Journal of Korean Institute of Information Technology
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• v.17 no.12
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• pp.21-28
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• 2019

Side effects of drugs mean harmful and unintended effects resulting from drugs used to prevent, diagnose, or treat diseases. These side effects can lead to patients' death and are the main causes of drug developmental failures. Thus, various methods have been tried to identify side effects. These can be divided into biological and systems biology approaches. In this study, we use systems biology approach and focus on using various phenotypic information in addition to the chemical structure and target proteins. First, we collect datasets that are used in this study, and calculate similarities individually. Second, we generate a set of features using the similarities for each drug-side effect pair. Finally, we confirm the results by AUC(Area Under the ROC Curve), and showed the significance of this study through a comparison experiment.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.12
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• pp.672-678
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• 2020

Ultra-high-speed images that accurately depict the minute movements of objects have become common as low-cost and high-performance cameras that can film at high speeds have emerged. In this paper, the proposed method removes unexpected noise contained in images after input at high speed, and then extracts an area of interest that can represent personal information, such as skin areas, from the image in which noise has been removed. In this paper, noise generated by abnormal electrical signals is removed by applying bilateral filters. A color model created through pre-learning is then used to extract the area of interest that represents the personal information contained within the image. Experimental results show that the introduced algorithms remove noise from high-speed images and then extract the area of interest robustly. The approach presented in this paper is expected to be useful in various applications related to computer vision, such as image preprocessing, noise elimination, tracking and monitoring of target areas, etc.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.1
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• pp.22-27
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• 2020

Recently, as the number of households raising pets increases due to the increase of single households, there is a need for a system for monitoring the status or behavior of pets. There are regional limitations in the monitoring of pets using domestic CCTVs, which requires a large number of CCTVs or restricts the behavior of pets. In this paper, we propose a mobile system for detecting and tracking cats using deep learning to solve the regional limitations of pet monitoring. We use YOLO (You Look Only Once), an object detection neural network model, to learn the characteristics of pets and apply them to Raspberry Pi to track objects detected in an image. We have designed a mobile monitoring system that connects Raspberry Pi and a laptop via wireless LAN and can check the movement and condition of cats in real time.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.7
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• pp.942-948
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• 2020

In this paper, we propose a clustered algorithm that possible more efficient user distinction within clustering using context-aware attribute information. In typically, the data provided to classify interrelationships within cluster information in the process of clustering data will be as a degrade factor if new or newly processing information is treated as contaminated information in comparative information. In this paper, we have developed a clustering algorithm that can extract user's recognition information to solve this problem in using K-means algorithm. The proposed algorithm analyzes the user's clustering attributed parameters from user clusters using accumulated information and clustering according to their attributes. The results of the simulation with the proposed algorithm showed that the user management system was more adaptable in terms of classifying and maintaining multiple users in clusters.