• Journal of Korea Multimedia Society
• /
• v.18 no.3
• /
• pp.298-311
• /
• 2015

Vector data represents a map by its coordinate and Raster data represents a map by its pixel. Since these data types have very large data size, data compression procedure is a compulsory process. This paper compare the results from three different methodologies; GIS (Geographic Information System) vector map data compression using DP(Douglas-Peucker) Simplification algorithm, vector data compression based on Bin classification and the combination between two previous methods. The results shows that the combination between the two methods have the best performance among the three tested methods. The proposed method can achieve 4-9% compression ratio while the other methods show a lower performance.

• Korean Journal of Oriental Medicine
• /
• v.10 no.1
• /
• pp.49-61
• /
• 2004

The Standardization of terms in The Pulse studies(脈學) is a need for development of learning. This study, for the correction of existing misused terms in The Pulse studies, we study on modernly and objectively the terms in The Pulse studies. By a focus of ${\ulcorner}$The Pulse Studies of Bin-Ho(瀕湖脈學)${\lrcorner}$, we studies on the new classification of pulse condition. The error of a existing technical books on Pulse studies begin that the classification of pulse condition is not establish a Standardization. For the correction of existing misused terms in The Pulse studies, we study on the pulse condition is expressed objectively a blood vessel that it is a subject of pulse condition. The expression of blood vessel contain a depth of blood vessel, a speed of pulsation, a curve of blood vessel, thickness of blood vessel, a diameter of blood vessel in expand and contract of blood vessel, a interval in expand and contract of blood vessel, a distinctness on a boundary of blood vessel, a speed of blood flow in blood vessel, a volume of blood flow in blood vessel, a condition of blood in blood vessel, a propelling power of blood vessel. These is standard of the new classification of pulse condition.

• Journal of the Semiconductor & Display Technology
• /
• v.22 no.1
• /
• pp.72-77
• /
• 2023

In many manufacturing settings, including the semiconductor industry, products are completed by producing and assembling various components. Sorting out from randomly mixed parts and classification operations takes a lot of time and labor. Recently, many efforts have been made to select and assemble correct parts from mixed parts using robots. Automating the sorting and classification of randomly mixed components is difficult since various objects and the positions and attitudes of robots and cameras in 3D space need to be known. Previously, only objects in specific positions were grasped by robots or people sorting items directly. To enable robots to pick up random objects in 3D space, bin picking technology is required. To realize bin picking technology, it is essential to understand the coordinate system information between the robot, the grasping target object, and the camera. Calibration work to understand the coordinate system information between them is necessary to grasp the object recognized by the camera. It is difficult to restore the depth value of 2D images when 3D restoration is performed, which is necessary for bin picking technology. In this paper, we propose to use depth information of RGB-D camera for Z value in rotation and movement conversion used in calibration. Proceed with camera calibration for accurate coordinate system conversion of objects in 2D images, and proceed with calibration of robot and camera. We proved the effectiveness of the proposed method through accuracy evaluations for camera calibration and calibration between robots and cameras.

• Korean Journal of Remote Sensing
• /
• v.23 no.3
• /
• pp.153-160
• /
• 2007

Forest cover density studies using high resolution satellite data and object oriented classification are limited in India. This article focuses on the potential use of QuickBird satellite data and object oriented classification in forest density mapping. In this study, the high-resolution satellite data was classified based on NDVI/pixel based and object oriented classification methods and results were compared. The QuickBird satellite data was found to be suitable in forest density mapping. Object oriented classification was superior than the NDVI/pixel based classification. The Object oriented classification method classified all the density classes of forest (dense, open, degraded and bare soil) with higher producer and user accuracies and with more kappa statistics value compared to pixel based method. The overall classification accuracy and Kappa statistics values of the object oriented classification were 83.33% and 0.77 respectively, which were higher than the pixel based classification (68%, 0.56 respectively). According to the Z statistics, the results of these two classifications were significantly different at 95% confidence level.

• International Journal of Computer Science & Network Security
• /
• v.21 no.3
• /
• pp.103-111
• /
• 2021

Obesity affects individuals of all gender and ages worldwide; consequently, several studies have performed great works to define factors causing it. This study develops an effective method to trace obesity levels based on supervised data mining techniques such as Random Forest and Multi-Layer Perception (MLP), so as to tackle this universal epidemic. Notably, the dataset was from countries like Mexico, Peru, and Colombia in the 14- 61year age group, with varying eating habits and physical conditions. The data includes 2111 instances and 17 attributes labelled using NObesity, which facilitates categorization of data using Overweight Levels l I and II, Insufficient Weight, Normal Weight, as well as Obesity Type I to III. This study found that the highest accuracy was achieved by Random Forest algorithm in comparison to the MLP algorithm, with an overall classification rate of 96.7%.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.46 no.2
• /
• pp.1-12
• /
• 2023

To make semiconductor chips, a number of complex semiconductor manufacturing processes are required. Semiconductor chips that have undergone complex processes are subjected to EDS(Electrical Die Sorting) tests to check product quality, and a wafer bin map reflecting the information about the normal and defective chips is created. Defective chips found in the wafer bin map form various patterns, which are called defective patterns, and the defective patterns are a very important clue in determining the cause of defects in the process and design of semiconductors. Therefore, it is desired to automatically and quickly detect defective patterns in the field, and various methods have been proposed to detect defective patterns. Existing methods have considered simple, complex, and new defect patterns, but they had the disadvantage of being unable to provide field engineers the evidence of classification results through deep learning. It is necessary to supplement this and provide detailed information on the size, location, and patterns of the defects. In this paper, we propose an anomaly detection framework that can be explained through FCDD(Fully Convolutional Data Description) trained only with normal data to provide field engineers with details such as detection results of abnormal defect patterns, defect size, and location of defect patterns on wafer bin map. The results are analyzed using open dataset, providing prominent results of the proposed anomaly detection framework.

• International Journal of Computer Science & Network Security
• /
• v.23 no.4
• /
• pp.95-102
• /
• 2023

The emergence of Internet of Things (IoT) into our daily lives has grown rapidly. It's been integrated to our homes, cars, and cities, increasing the intelligence of devices involved in communications. Enormous amount of data is exchanged over smart devices through the internet, which raises security concerns in regards of privacy evasion. This paper is focused on the forensics and intrusion detection on one of the most common protocols in IoT environments, especially smart home environments, which is the Message Queuing Telemetry Transport (MQTT) protocol. The paper covers general IoT infrastructure, MQTT protocol and attacks conducted on it, and multiple network forensics frameworks in smart homes. Furthermore, a machine learning model is developed and tested to detect several types of attacks in an IoT network. A forensics tool (MQTTracker) is proposed to contribute to the investigation of MQTT protocol in order to provide a safer technological future in the warmth of people's homes. The MQTT-IOT-IDS2020 dataset is used to train the machine learning model. In addition, different attack detection algorithms are compared to ensure the suitable algorithm is chosen to perform accurate classification of attacks within MQTT traffic.

• Journal of Korean Society of Rural Planning
• /
• v.13 no.1 s.34
• /
• pp.41-50
• /
• 2007

Recently, it is demanded to study about landscape. Landscape is an environment factor for improving life, a social resource for establishing image and identity of the area and also a tourist resource for earning profit. With this importance, landscape planning is being performed by local governments. However, classification for a rule about landscape planning is not prepared yet. For this purpose, classification of landscape is definitely required. Therefore, this article focuses on presenting method of classification Natural and Rural Landscape. We used Arcview 3.2 to draw watershed of the site, and calculated the percent of landform. We also took a picture which explain the landscape and made a survey of classifying the landscape. Due to this study, we were able to frame an algorithm of the landscape classification. This will contribute to classify the landscape type. This study needs more specific researches because it was supposed to target the entire Gyeonggi-Do however it only covered several regions. Through this results, it would be expected to develop the map of landscape character.

• Korean Journal of Materials Research
• /
• v.16 no.10
• /
• pp.633-638
• /
• 2006

Silica powders with spherical shape and narrow size distribution were prepared by large-scale ultrasonic spray pyrolysis applying the droplet classification apparatus. On the other hand, silica powders prepared by large-scale ultrasonic spray pyrolysis without droplet classification apparatus had broad size distribution. Droplet classification apparatus used in this paper applied the principles of cyclone and dispersion plate with small holes. The droplets formed from the ultrasonic spray generator applying the droplet classification apparatus had narrow size distribution. The droplets with fine and large sizes were eliminated by droplet classification apparatus. The optimum flow rate of the carrier gas and diameter of the hole of the dispersion plate were studied to reduce the size distribution of the silica powders prepared by large-scale ultrasonic spray pyrolysis. The size distribution of the silica powders prepared by large-scale ultrasonic spray pyrolysis at the optimum preparation conditions was 0.76.

• Proceedings of the Korean Society for Quality Management Conference
• /
• 2006.04a
• /
• pp.513-522
• /
• 2006