• Journal of Internet Computing and Services
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• v.19 no.1
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• pp.27-35
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• 2018

This paper is a study to classify malicious applications in Android environment. And studying the threat and behavioral analysis of malicious Android applications. In addition, malicious apps classified by machine learning were performed as experiments. Android behavior analysis can use dynamic analysis tools. Through this tool, API Calls, Runtime Log, System Resource, and Network information for the application can be extracted. We redefined the properties extracted for machine learning and evaluated the results of machine learning classification by verifying between the overall features and the main features. The results show that key features have been improved by 1~4% over the full feature set. Especially, SVM classifier improved by 10%. From these results, we found that the application of the key features as a key feature was more effective in the performance of the classification algorithm than in the use of the overall features. It was also identified as important to select meaningful features from the data sets.

• Journal of IKEEE
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• v.24 no.1
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• pp.25-32
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• 2020

In this paper, we propose an image generator for OLED panel test that can compensate for color coordinates and luminance by using panel defect inspection and optical measurement while displaying images on OLED panel. The proposed image generator consists of two processes: the image generation process and the process of compensating color coordinates and luminance using optical measurement. In the image generating process, the panel is set to receive the panel information to drive the panel, and the image is output by adjusting the output setting of the image generator according to the panel information. The output form of the image is configured by digital RGB method. The pattern generation algorithm inside the image generator outputs color and gray image data by transmitting color data to a 24-bit data line based on a synchronization signal according to the resolution of the panel. The process of compensating color coordinates and luminance using optical measurement outputs an image to an OLED panel in an image generator, and compensates for a portion where color coordinates and luminance data measured by an optical module differ from reference data. To evaluate the accuracy of the image generator for the OLED panel test proposed in this paper, Xilinx's Spartan 6 series XC6SLX25-FG484 FPGA was used and the design tool was ISE 14.5. The output of the image generation process was confirmed that the target setting value and the simulation result value for the digital RGB output using the oscilloscope matched. Compensating the color coordinates and luminance using optical measurements showed accuracy within the error rate suggested by the panel manufacturer.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.2
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• pp.821-828
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• 2011

This paper developed the algorithm for improving the performance the auto pilot in the autonomous vehicle system consisting of the Track keeping control, the Automatic steering, and the Automatic mooring control. The automatic steering is the control device that could save the voyage distance and cost of fuel by reducing the unnecessary burden of driving due to the continuous artificial navigation, and avoiding the route deviation. During the step of the ship autonomic navigation control, since the wind power or the tidal force could make the ship deviate from the fixed course, the automatic steering calculates the difference between actual sailing line and the set course to keep the ship sailing in the vicinity of intended course. first, we could get the transfer function for the modeling of ship according to the Nomoto model. Considering the maneuverability, we propose it as linear model with only 4 degree of freedoms to present the heading angle response to the input of rudder angle. In this paper, the model of ship is derived from the simplified Nomoto model. Since the proposed model considers the maximum angle and rudder rate of the ship auto pilot and also designs the Fuzzy controller based on existing PID controller, the performance of the steering machine is well improved.

• The Transactions of the Korea Information Processing Society
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• v.4 no.7
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• pp.1770-1780
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• 1997

Our problem is improvement of image quality because it is inevitable cell loss of image data when traffic congestion occurs. If cells are discarded indiscriminately in transmission of MPEG video data, it occurs severe degradation in quality of service(QOS). In this paper, to solve this problem, we propose two method. The first, we analyze the traffic characteristics of an MPEG encoder and generate high priority and low priority data stream. During network congestion, only the least low priority cells are dropped, and this ensures that the high priority cells are successfully transmitted, which, in turn, guarantees satisfactory QoS. In this case, the prioritization scheme for the encoder assigns components of the data stream to each priority level based on the value of a parameter ${\beta}$. The second, Number of high priority cells are increased when value of ${\beta}$ is large. It occurs the loss of high priority cell in the congestion. To prevent it, this paper is regulated to data stream rate as buffer occupancy with UPC controller. Therefore, encoder's bandwidth can be calculated renegotiation of the encoder and networks. In this paper, the encoder's bandwidth requirements are characterized by a usage parameter control (UPC) set consisting of peak rate, burstness, and sustained rate. An adaptive encoder rate control algorithm at the Networks Interface Card(NIC) computes the necessary UPC parameter to maintain the user specified quality of service. Simulation results are given for a rate-controlled VBR video encoder operating through an ATM network interface which supports dynamic UPC. These results show that dynamic bandwidth renegotiation of prioritized data stream could provided bandwidth saving and significant quality gains which guarantee high priority data stream.

• The Korean Journal of Applied Statistics
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• v.26 no.2
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• pp.267-280
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• 2013

College tuition is a significant economic, social, and political issue in Korea. We conduct a Bayesian analysis of a hierarchical model to address the factors related to college tuition based on a survey data collected by Statistics Korea. A binary response variable is selected depending on if more than 70% of tuition costs are supported by parents, and a hierarchical Probit model is constructed with areas as groups. A set of explanatory variables is selected from a factor analysis of available variables in the survey. A Markov chain Monte Carlo algorithm is used to estimate parameters. From the analysis results, income and stress are significantly related to college tuition support from parents. Parents with high income tend to support children's college tuition and students with parents' financial support tend to be mentally less stressed; subsequently, this shows that the economic status of parents significantly affects the mental health of college students. Gender, a healthy life style, and college satisfaction are not significant factors. Comparing areas in terms of the degrees of correlation between stress/income and tuition support from parents, students in Kangwon-do are the most mentally stressed when parents' support is limited; in addition, the positive correlation between parents support and income is stronger in big cities compared to provincial areas.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.32 no.4_1
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• pp.311-318
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• 2014

The data of buildings and structures take over large portions of the mapping database with large numbers. Furthermore, those shapes and attributes of building data continuously change over time. Due to those factors, the efficient methodology of updating database for following the most recent data become necessarily. This study has purposed on extracting needed data, which has been changed, by using overlaying analysis of new and old dataset, during updating processes. Following to procedures, we firstly searched for matching pairs of objects from each dataset, and defined the classification algorithm for building updating cases by comparing; those of shape updating cases are divided into 8 cases, while those of attribute updating cases are divided into 4 cases. Also, two updated dataset are set to be automatically saved. For the study, we selected few guidelines; the layer of digital topographic map 1:5000 for the targeted updating data, the building layer of Korea Address Information System map for the reference data, as well as build-up areas in Gwanak-gu, Seoul for the test area. The result of study updated 82.1% in shape and 34.5% in attribute building objects among all.

• Journal of the Korea Society of Computer and Information
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• v.24 no.10
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• pp.79-89
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• 2019

While most research focuses on the k-nearest neighbors (kNN) queries in the database community, an important type of proximity queries called k-farthest neighbors (kFN) queries has not received much attention. This paper addresses the problem of finding the k-farthest neighbors in road networks. Given a positive integer k, a query object q, and a set of data points P, a kFN query returns k data objects farthest from the query object q. Little attention has been paid to processing kFN queries in road networks. The challenge of processing kFN queries in road networks is reducing the number of network distance computations, which is the most prominent difference between a road network and a Euclidean space. In this study, we propose an efficient algorithm called FANS for k-FArthest Neighbor Search in road networks. We present a shared computation strategy to avoid redundant computation of the distances between a query object and data objects. We also present effective pruning techniques based on the maximum distance from a query object to data segments. Finally, we demonstrate the efficiency and scalability of our proposed solution with extensive experiments using real-world roadmaps.

• Journal of Broadcast Engineering
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• v.24 no.5
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• pp.713-725
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• 2019

Automatic License Plate Recognition (ALPR) is a technology required for many applications such as Intelligent Transportation Systems and Video Surveillance Systems. Most of the studies have studied were about the detection and recognition of license plates on cars, and there is very little about detecting and recognizing license plates on motorbikes. In the case of a car, the license plate is located at the front or rear center of the vehicle and is a straight or slightly sloped license plate. Also, the background of the license plate is mainly monochromatic, and license plate detection and recognition process is less complicated. However since the motorbike is parked by using a kickstand, it is inclined at various angles when parked, so the process of recognizing characters on the motorbike license plate is more complicated. In this paper, we have developed a 2-stage YOLOv2 algorithm to detect the area of a license plate after detection of a motorbike area in order to improve the recognition accuracy of license plate for motorbike data set parked at various angles. In order to increase the detection rate, the size and number of the anchor boxes were adjusted according to the characteristics of the motorbike and license plate. Image warping algorithms were applied after detecting tilted license plates. As a result of simulating the license plate character recognition process, the proposed method had the recognition rate of license plate of 80.23% compared to the recognition rate of the conventional method(YOLOv2 without image warping) of 47.74%. Therefore, the proposed method can increase the recognition of tilted motorbike license plate character by using the adjustment of anchor boxes and the image warping which fit the motorbike license plate.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.26 no.4
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• pp.307-326
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• 2021

Real-time sea level observations from tide gauges include missing and erroneous values. Classification as abnormal values can be done for the latter by the quality control procedure. Although the 3𝜎 (three standard deviations) rule has been applied in general to eliminate them, it is difficult to apply it to the sea-level data where extreme values can exist due to weather events, etc., or where erroneous values can exist even within the 3𝜎 range. An artificial intelligence model set designed in this study consists of non-annotated recurrent neural networks and ensemble techniques that do not require pre-labeling of the abnormal values. The developed model can identify an erroneous value less than 20 minutes of tide gauge recording an abnormal sea level. The validated model well separates normal and abnormal values during normal times and weather events. It was also confirmed that abnormal values can be detected even in the period of years when the sea level data have not been used for training. The artificial neural network algorithm utilized in this study is not limited to the coastal sea level, and hence it can be extended to the detection model of erroneous values in various oceanic and atmospheric data.

• Proceedings of the Korean Society of Crop Science Conference
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• 2022.10a
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• pp.169-169
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• 2022

Over the past 100 years, the global average temperature has risen by 0.75 ℃. The Korean Peninsula has risen by 1.8 ℃, more than twice the global average. According to the RCP 8.5 scenario, the CO₂ concentration in 2100 will be 940 ppm, about twice as high as current. The National Institute of Crop Science(NICS) is using the SPAR (Soil-Plant Atmosphere Research) facility that can precisely control the environment, such as temperature, humidity, and CO₂. A Python-based colony photosynthesis algorithm has been developed, and the carbon and nitrogen absorption rate of rice is evaluated by setting climate change conditions. In this experiment, Oryza Sativa cv. Shindongjin were planted at the SPAR facility on June 10 and cultivated according to the standard cultivation method. The temperature and CO₂ settings are high temperature and high CO₂ (current temperature+4.7℃ temperature+4.7℃·CO₂ 800ppm), high temperature single condition (current temperature+4.7℃·CO₂ 400ppm) according to the RCP8.5 scenario, Current climate is set as (current temperature·CO₂400ppm). For colony photosynthesis measurement, a LI-820 CO₂ sensor was installed in each chamber for setting the CO₂ concentration and for measuring photosynthesis, respectively. The colony photosynthetic rate in the booting stage was greatest in a high temperature and CO₂ environment, and the higher the nitrogen fertilization level, the higher the colony photosynthetic rate tends to be. The amount of photosynthesis tended to decrease under high temperature. In the high temperature and high CO₂ environment, seed yields, the number of an ear, and 1000 seed weights tended to decrease compared to the current climate. The number of an ear also decreased under the high temperature. But yield tended to increase a little bit under the high temperature and high CO₂ condition than under the high temperature. In addition, In addition to this study, it seems necessary to comprehensively consider the relationship between colony photosynthetic ability, metabolite reaction, and rice yield according to climate change.