• Journal of IKEEE
• /
• v.23 no.3
• /
• pp.883-892
• /
• 2019

This study is the method which is adapted to control ankle joint movement for resolving the problem of gait imbalance in intervals where gait environments are changed and slope walking, as applying terrain-adaptive technique to intelligent above-knee prosthesis. In this development of above-knee prosthesis, to classify the gait modes is essential. For distinguishing the stance phases and the swing phase depending on roads, a machine learning which combines decision tree and random forest from knee angle data and inertial sensor data, is proposed and adapted. By using this method, the ankle movement state of the prosthesis is controlled. This study verifies whether the problem is resolved through butterfly diagram.

• Journal of agriculture & life science
• /
• v.53 no.5
• /
• pp.37-49
• /
• 2019

Environmental conditions are important in increasing the fruit sugar content and productivity of the new cultivar Autumn Sense of Actinidia arguta. We analyzed various soil properties at experimental sites in South Korea. A Pearson's correlation analysis was performed between the soil properties and sugar content or productivity of Autumn Sense. Further, a decision tree was used to determine the optimal soil conditions. The difference in the fruit size, sugar content, and productivity of Autumn Sense across sites was significant, confirming the effects of soil properties. The decision tree analysis showed that a soil C/N ratio of over 11.49 predicted a sugar content of more than 7°Bx at harvest time, and soil electrical capacity below 131.83 µS/cm predicted productivity more than 50 kg/vine at harvest time. Our results present the soil conditions required to increase the sugar content or productivity of Autumn Sense, a new A. arguta cultivar in South Korea.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.21 no.12
• /
• pp.411-416
• /
• 2020

Field investigations of forests are carried out by writing measured data by hand, and it is a hassle to reorganize the results after a field survey. In this study, a method using object modeling and augmented reality (AR) was applied in a test forest to increase the efficiency of a field investigations. Using a 3D laser scanner, data on were acquired 387 trees within an area of 1 ha at the study site. The coordinates, height, and diameter were calculated through object extraction and modeling of a tree. The proposed can reduce the time required to acquire data in the field and can be used as basic data for building related systems. In addition, the modeling results of trees and a survey using GNSS and AR techniques can be used check coordinates, labor, and attribute information, such as the chest height diameter of the trees being surveyed in the field. The shortcomings of the survey method could be improved. In the future, the method could greatly improve the efficiency of tree surveys and monitoring by reducing the manpower and time required for field surveys.

• Journal of the Institute of Electronics Engineers of Korea CI
• /
• v.48 no.2
• /
• pp.16-27
• /
• 2011

Embryo is a very early stage of the development of multicellular organism such as animals and plants. It is an important research target for studying ontogeny because the fundamental body system of multicellular organism is determined during an embryo state. Researchers in the developmental biology have a large volume of embryo image databases for studying embryos and they frequently search for an embryo image efficiently from those databases. Thus, it is crucial to organize databases for their efficient search. Hierarchical clustering methods have been widely used for database organization. However, most of previous algorithms tend to produce a highly skewed tree as a result of clustering because they do not simultaneously consider both the size of a cluster and the number of objects within the cluster. The skewed tree requires much time to be traversed in users' search process. In this paper, we propose a method that effectively organizes a large volume of embryo image data in a balanced tree structure. We first represent embryo image data as a similarity-based graph. Next, we identify clusters by performing a graph partitioning algorithm repeatedly. We check constantly the size of a cluster and the number of objects, and partition clusters whose size is too large or whose number of objects is too high, which prevents clusters from growing too large or having too many objects. We show the superiority of the proposed method by extensive experiments. Moreover, we implement the visualization tool to help users quickly and easily navigate the embryo image database.

• KIPS Transactions on Software and Data Engineering
• /
• v.5 no.12
• /
• pp.653-662
• /
• 2016

This paper describes a test case generation algorithm for Simulink/Stateflow models based on the Rapidly exploring Random Tree (RRT) algorithm that has been successfully applied to path finding. An important factor influencing the performance of the RRT algorithm is the metric used for calculating the distance between the nodes in the RRT space. Since a test case for a Simulink/Stateflow (SL/SF) model is an input sequence to check a specific condition (called a test target in this paper) at a specific status of the model, it is necessary to drive the model to the status before checking the condition. A status maps to a node of the RRT. It is usually necessary to check various conditions at a specific status. For example, when the specific status represents an SL/SF model state from which multiple transitions are made, we must check multiple conditions to measure the transition coverage. We propose a unique distance calculation metric, based on the observation that the test targets are gathered around some specific status such as an SL/SF state, named key nodes in this paper. The proposed metric increases the probability that an RRT is extended from key nodes by imposing penalties to non-key nodes. A test case generation algorithm utilizing the proposed metric is proposed. Three models of Electrical Control Units (ECUs) embedded in a commercial vehicle are used for the performance evaluation. The performances are evaluated in terms of penalties and compared with those of the algorithm using a typical RRT algorithm.

• Journal of IKEEE
• /
• v.18 no.4
• /
• pp.485-494
• /
• 2014

Traffic Sign Recognition(TSR) is an important element in an Advanced Driver Assistance System(ADAS). However, many studies related to TSR approaches only in normal daytime environment because a sign's unique color doesn't appear in poor environment such as night time, snow, rain or fog. In this paper, we propose a new TSR algorithm based on machine learning for daytime as well as poor environment. In poor environment, traditional methods which use RGB color region doesn't show good performance. So we extracted sign characteristics using HoG extraction, and detected signs using a Support Vector Machine(SVM). The detected sign is recognized by a decision tree based on 25 reference points in a Normalized RGB system. The detection rate of the proposed system is 96.4% and the recognition rate is 94% when applied in poor environment. The testing was performed on an Intel i5 processor at 3.4 GHz using Full HD resolution images. As a result, the proposed algorithm shows that machine learning based detection and recognition methods can efficiently be used for TSR algorithm even in poor driving environment.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
• /
• v.14 no.6
• /
• pp.35-42
• /
• 2000

In this paper, AE(Acoustic Emission) signals detected from the growth of the electrical tree in an epoxy resin under ac high-voltage application were analysed to diagnose the insulation deterioration of cast-resin power transformer. Frequency spectra of AE signals generated from the magnetizing and the load currents in the actual operating cast-resin power transformer of 500[kVA] under distribution system of 22.9[kV] were also analysed to distinguish the AE signals due to void discharges from the magnetic circuit noises in the core of the transformer. As the experimental results, we could distinguish the AE signals whether those signals were caused due to the void discharges or due to the magnetic circuit noises by analyzing the frequency spectrum of AE signals. The frequency spectra of AE signals generated from the cast-resin power transformer in operation due to both the magnetizing and the load currents appeared in the range of 40-120[kHz], but the frequency band of AE signals emitted from the void discharges in an epoxy resin sample was about 50[kHz] to 230[kHz].

• Journal of IKEEE
• /
• v.15 no.3
• /
• pp.227-234
• /
• 2011

The Interest in robotics has been steadily increasing in recent times both in Korea as well as abroad. Research on robots for new and diverse fields is ongoing. This study discusses the current research and development on robot actuator, which are used to control the joints of robots, and focuses on developing more efficient technology for joint control, as compared with the current technologies. It also aims to find means to apply the abovementioned technology to diverse industrial fields. We found that easy and effective control of actuators could be achieved by using ZigBee sensor networks, which were widely being used on wireless communications. Throughout the experiments it is proved that the developed wireless actuator could be used for easy control of various robot joints. This technology can be effectively applied to develop two-legged robots that will be able to walk like human, or even quadruped and hexapod robots. It can also be applied to motors used in industry. In this study, we develop an extremely minimized ZigBee sensor network module that can be used to control various servo motors with low power consumption even if it is long distances. We realized effective wireless control by optimizing the ZigBee antenna, and were able to quickly check the status of relevant Tree node through mutual communication between the servo motors composing the ZigBee sensor network and the main server control modules. The developed Servo Motor with ZigBee sensor network modules can be applied in both robotics as well as for home or factory automation.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
• /
• v.35 no.5
• /
• pp.459-465
• /
• 2022

This paper reports a method to use a wireless sensor network deployed in the field to real-time monitor soil moisture, warning when the moisture level reaches a specific value, and wirelessly controlling an additional device (LED or water supply system, etc.). In addition, we report all processes related to wireless irrigation system, including field deployment of sensors, real-time monitoring using a smartphone, data calibration, and control of additional devices deployed in the field by smartphone. A commercially available open-source Internet of Things (IoT) platform, NodeMCU, was used, which was combined with a 9V battery, LED and soil humidity sensor to be integrated into a portable prototype. The IoT-based soil humidity sensor prototype deployed in the field was installed next to a tree for on-site demonstration for the measurement of soil humidity in real-time for about 30 hours, and the measured data was successfully transmitted to a smartphone via Wifi. The measurement data were automatically transmitted via e-mail in the form of a text file, stored on the web, followed by analyses and calibrations. The user can check the humidity of the soil real-time through a personal smartphone. When the humidity of a soil reached a specific value, an additional device, an LED device, placed in the field was successfully controlled through the smartphone. This LED can be easily replaced by other electronic devices such as water supplies, which can also be controlled by smartphones. These results show that farmers can not only monitor the condition of the field real-time through a sensor monitoring system manufactured simply at a low cost but also control additional devices such as irrigation facilities from a distance, thereby reducing unnecessary energy consumption and helping improve agricultural productivity.

• Korean Journal of Environment and Ecology
• /
• v.12 no.2
• /
• pp.131-137
• /
• 1998

This study was performed to investigate tree injury with air pollution and acid precipitation in industrial area and rural area. This study analyzed the ion properties of pollutant precipitated in the forest of Ulsan ＆ Onsan area and correlation between S $O_2$concentration in air and the degree of forest decline. pH of industrial area was lower than that of rural area and electrical conductivity and pH had a negative correlation(r=-.7861$^{**}$). Correlation of cation and anion(especially S $O_{4}$$^{2-}$, N $O_{3}$$^{[-10]}$ ) in precipitation and S $O_2$in air was higher in industrial area. In seasonal change, winter and spring were higher. In the analysis of correlation between forest decline and variables of precipitation properties, correlation coefficient was higher by following order: S $O_{4}$$^{2-}$>pH>EC>N $O_{3}$$^{[-10]}$ >S $O_{2}$$^{2-}$>C $l^{[-10]}$ . Regression formula by computation was Y = 5.1007-0.7811 $X_2$(pH) +0.0253 $X_{5}$ (S $O_{4}$$^{2-}$) +0.0275 $X_{6}$ (N $O_{3}$$^{[-10]}$ ). In considering the result of this study, it was predicted that air pollution and acid rain would affect soil acidification and forest decline continuously.y.