• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.11
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• pp.2603-2608
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• 2015

Mushroom is a high in protein, low calorie food and has dietary fiber, vitamins, iron and minerals such as zinc. It is called that mushroom is one of the biggest concerns for healthy foods. When we make the artificial cultivation of mushroom, one of the greatest influence element is temperature. In this regard, farmers passively measure temperatures in the greenhouse as inaccurate way such as by the naked eyes. In this paper, we constructed a display system in order to improve the efficiency of manual management of temperature based on the influence of temperature on the mushroom. In related to the methods of mushroom cultivation, the recent technology apply the new technology such as sensors and IT convergence services. And then cultivating mushroom is managed effectively. In this paper, we implement an automatic display system for sensing data. By using this function, farmers could effectively manage environment needed to be grown mushroom, and anticipate the improvement of sales by increasing quality of mushrooms as well.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.15 no.2
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• pp.50-62
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• 2016

With programmable applications that utilize sensors, such as global positioning systems and accelerometers, smartphones provide an unprecedented opportunity to collect behavioral data in an unobtrusive and cost-effective manner. This paper assesses the relative accuracy and reliability of the Future Mobility Sensing (FMS), a smartphone-based prompted-recall travel survey. We compared the data extracted from FMS with the data collected from the Korea Passenger Trip Survey (PTS), a traditional self-reported, paper-based travel survey. In total, 46 undergraduate students completed the PTS for seven consecutive days, while also carrying their smartphones with the activated FMS applications for the same time span. After completing the PTS, the participants validated their FMS data on the web-based prompted recall surveys. We then matched the validated FMS data with the PTS-based records. The FMS turns out to be superior in detecting short trips, which are usually under-reported in self-reported travel surveys. The reported PTS travel times are longer than for the FMS, suggesting that participants tend to overestimate their travel time in the PTS. This study contributes to the ongoing development of smartphone-based travel behavior data collecting methods.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.12 no.4
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• pp.366-372
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• 2019

In this paper, we fabricated a capacitive humidity sensor with an IDT(Interdigitated) electrode using commercial polyimide containing fluorine, and its properties were measured and analyzed. First, in order to analyze the composition of commercial polyimide, EDS analysis was performed after patterning process on a silicon wafer. The area of the humidity sensor was $1.56{\times}1.66mm^2$, and the width of the electrode and the gap between the electrodes were $3{\mu}m$ each. The number of electrodes was 166 and the length of the electrode was 1.294mm for the sensitivity of the sensor. The fabricated sensor showed that the sensitivity was 24 fF/%RH, linearity <${\pm}2.5%RH$ and hysteresis <${\pm}4%RH$. As a result of measuring the capacitance value according to the frequency change, the capacitance vlaue decreased with increasing frequency. Capacitance deviations with 10kHz and 100kHz were measured as 0.3pF on average.

• The Journal of the Convergence on Culture Technology
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• v.5 no.2
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• pp.247-255
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• 2019

This study was conducted to explore the correlation between the personality types and learning styles and academic achievement in first year nursing students. 144 students were sampled in first year nursing sstudents. Data were collected from March 6 to May 18 2018. Results analysis was performed using SPSS win version of frequency, mean and standard deviation, t-test, ANOVA, Chi-square test, Pearson correlation. The personality types of nursing students in this study were 58.3% of Extrovert type, 41.7% of Introvert type, 33.3% of Sensing, 66.7% of Intuition type, 22.2% of Thinking, 77.8% of Feeling, 50.7% of Judging type, and 49.3% of Perceiving type. The average academic achievement of female students was 3.51 points, higher than the average of 3.17 for male students (t=-3.277, p<.001). The average academic achievement of Introvert type was found to be higher than the average of Extrovert type (t=3.541, p<.001). Learning styles by personality type showed a statistically significant difference between the judging type and the other personality types (${\chi}^2=18.409$, p<.001). There was a significant amount of correlation between gender and TF index (r=.209, p<.05), gender and academic achievement (r=.265, p<.01), JP index and learning styles (r=.262, p<.01, EI index and academic achievement (r=.285, p<.01). The development and utilization of teaching-learning methods suitable for individuals will be required based on the results of personality types, learning styles and academic achievement in first year nursing students identified through this study.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.2
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• pp.115-121
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• 2022

As city environments have recently become crowded, there are many obstacles that interfere with the walking of the visually impaired on pedestrian roads. Typical examples include ballads, parking breakers and standing signs, which usually do not get in the way, but blind people may be injured by collisions. To solve such a problem, many solutions have been proposed, but they are limited in applied in practical environments due to the several restrictions such as outside use only, inaccurate obstacle sensing and requirement of special devices. In this paper, we propose a new method to automatically detect obstacles while walking on the pedestrian roads and warn the collision risk in advance by using only sensors embedded in typical mobile phones. The proposed method supports the walking of the visually impaired by notifying the type of obstacles appearing in front of them as well as the distance remaining from the obstacles. To accomplish this goal, we utilized an object recognition technology applying the latest deep learning algorithms in order to identify the obstacles appeared in real-time videos. In addition, we also calculate the distance to the obstacles using the number of steps and the pedestrian's stride. Compared to the existing walking support technologies for the visually impaired, our proposed method ensures efficient and safe walking with only simple devices regardless of the places.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.33 no.2
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• pp.267-280
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• 2023

With development of computing and communications technologies, IoT environments based on high-speed networks have been extending rapidly. Especially, from home to an office or a factory, applications of IoT devices with sensing environment and performing computations are increasing. Unfortunately, IoT devices which have limited hardware resources can be vulnerable to cyber attacks. Hence, there is a concern that an IoT botnet can give rise to information leakage as a national cyber security crisis arising from abuse as a malicious waypoint or propagation through connected networks. In order to response in advance from unknown cyber threats in IoT networks, in this paper, We firstly define four types of We firstly define four types of characteristics by analyzing darknet traffic accessed from an IoT botnet. Using the characteristic, a suspicious IP address is filtered quickly. Secondly, the filtered address is identified by Cyber Threat Intelligence (CTI) or Open Source INTelligence (OSINT) in terms of an unknown suspicious host. The identified IP address is finally fingerprinted to determine whether the IP is a malicious host or not. To verify a validation of the proposed method, we apply to a Darknet on real-world SOC. As a result, about 1,000 hosts who are detected and blocked preemptively by the proposed method are confirmed as real IoT botnets.

• Journal of the Korean Geotechnical Society
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• v.25 no.6
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• pp.17-29
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• 2009

There has been an increase in the investigation of deep sea sediments with a consequent increase in the amount of energy required to undertake these investigations. The geotechnical characteristics of Ulleung Basin sediment are explored by using depressurized specimens following methane production tests carried out on pressured core samples obtained at 2,100 m water depth and 110 m below sea floor. Geotechnical index tests, X-ray diffraction, and scanning electron microscope are conducted to identify the geotechnical index parameters, clay mineralogy, chemical composition, and microstructure of the sediments. Compressibility, and elastic and electromagnetic wave parameters are investigated for two samples by using a multi sensing instrumented oedometer cell. The strength chatracteristics are obtained by the direct shear tests. The dominant clay minerals are mostly kaolinite, illite, chlorite, and calcite. The SEM shows a well-developed flocculated structure of the microfossil. Void ratio, electrical resistivity, real permittivity, conductivity, and shear wave velocity show bi-linear behavior with the effective vertical stress: as the vertical effective stress increases. The friction angle obtained by the direct shear test is about $21^{\circ}$, which is similar to the value observed in the Ulleung Basin sediments. This study shows that the understanding of the behavior acting on the diatomaceous marine sediment is important because it often maintains the useful energy resources such as gas hydrate and so will be the new engineering field in the next generation.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.40 no.1
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• pp.15-22
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• 2022

In order to improve the accuracy of the deep learning object detection technique, the effect of magnification rate conditions and seasonal factors on detection accuracy in aerial photographs and drone images was analyzed through experiments. Among the deep learning object detection techniques, Mask R-CNN, which shows fast learning speed and high accuracy, was used to detect the vehicle to be detected in pixel units. Through Seoul's aerial photo service, learning images were captured at different screen magnifications, and the accuracy was analyzed by learning each. According to the experimental results, the higher the magnification level, the higher the mAP average to 60%, 67%, and 75%. When the magnification rates of train and test data of the data set were alternately arranged, low magnification data was arranged as train data, and high magnification data was arranged as test data, showing a difference of more than 20% compared to the opposite case. And in the case of drone images with a seasonal difference with a time difference of 4 months, the results of learning the image data at the same period showed high accuracy with an average of 93%, confirming that seasonal differences also affect learning.

• Journal of Intelligence and Information Systems
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• v.29 no.2
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• pp.23-34
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• 2023

Wearable technology for military applications has received considerable attention as a means of personal status check and monitoring. Among many, an implementation to recognize specific motion states of a human is promising in that allows active management of troops by immediately collecting the operational status and movement status of individual soldiers. In this study, as an extension of military wearable application research, a new ankle wearable device is proposed that can glean the information of a soldier on the battlefield on which action he/she takes in which environment. Presuming a virtual situation, the soldier's upper limbs are easily exposed to uncertainties about circumstances. Therefore, a sensing module is attached to the ankle of the soldier that may always interact with the ground. The obtained data comprises 3-axis accelerations and 3-axis rotational velocities, which cannot be interpreted by hand-made algorithms. In this study, to discern the behavioral characteristics of a human using these dynamic data, a data-driven model is introduced; four features extracted from sliced data (minimum, maximum, mean, and standard deviation) are utilized as an input of the model to learn and classify eight primary military movements (Sitting, Standing, Walking, Running, Ascending, Descending, Low Crawl, and High Crawl). As a result, the proposed device could recognize a movement status of a solider with 95.16% accuracy in an arbitrary test situation. This research is meaningful since an effective way of motion recognition has been introduced that can be furtherly extended to various military applications by incorporating wearable technology and artificial intelligence.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.16 no.6
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• pp.478-488
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• 2023

This paper investigates the naval application of laser communication as a potential replacement for traditional acoustic wave communication in underwater environments. We developed a laser transceiver using Arduino and MATLAB, conducting a water tank experiment to validate communication feasibility across diverse underwater conditions. In the first experiment, when transmitting data through a laser, the desired message was converted into data and transmitted, received, and confirmed to be converted into the correct message. In the second experiment, the operation of communication in underwater situations was confirmed, and in the third experiment, the intensity of light was measured using the CDS illuminance sensor module and the limits of laser communication were measured and confirmed in various underwater situations. Additionally, MATLAB code was employed to gather data on salinity, water temperature, and water depth for calculating turbidity. Optimal wavelength values (532nm, 633nm, 785nm, 1064nm) corresponding to calculated turbidity levels (5, 20, 55, 180) were determined and presented. The study then focuses on analyzing potential applications in naval tactical communication, remote sensing, and underwater drone control. Finally, we propose measures for overcoming current technological limitations and enhancing performance.