• Science of Emotion and Sensibility
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• v.26 no.4
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• pp.41-50
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• 2023

This study aims to develop electromyography (EMG) textile electrodes and assess their performance and signal stability by examining variations in layer count and fabric types. We fabricated the electrodes through layering and pressing techniques, focusing on configurations with different layer counts (Layer-0, Layer-1, and Layer-2). Our findings indicate that layer presence significantly influences muscle activation measurements, with enhanced performance correlated with increased layer numbers. Subsequently, we created electrodes from five distinct fabrics (neoprene, spandex cushion, 100% polyester, nylon spandex, and cotton canvas), each maintaining a Layer-2 structure. In performance tests, nylon spandex fabric, particularly heavier variants, outperformed others, while the spandex cushion electrodes showed superior stability in muscle activation signal acquisition. This research elucidates the connection between electrode performance and factors like layer number and electrode-skin contact area. It suggests a novel approach to electrode design, focusing on layer properties and targeted pressure application on specific sensor areas, rather than uniformly increasing sleeve pressure.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.17 no.1
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• pp.25-30
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• 2024

Smart City, a key application area of the AIoT (Artificial Intelligence of Things), provides various services in safety, security, and healthcare sectors through location tracking and location-based services. an IPS (Indoor Positioning System) is required to implement location-based services, and wireless communication technologies such as WiFi, UWB (Ultra-wideband), and BLE (Bluetooth Low Energy) are being applied. BLE, which enables data transmission and reception with low power consumption, can be applied to various IoT devices such as sensors and beacons at a low cost, making it one of the most suitable wireless communication technologies for indoor positioning. BLE utilizes the RSSI (Received Signal Strength Indicator) to estimate the distance, but due to the influence of multipath fading, which causes variations in signal strength, it results in an error of several meters. In this paper, we conducted research on a path loss model that can be applied to BLE IPS for proximity services, and confirmed that optimizing the free space propagation loss coefficient can reduce the distance error between the Tx and Rx devices.

• Smart Structures and Systems
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• v.33 no.2
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• pp.119-131
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• 2024

Drift angle is a significant index for diagnosing post-event structures. A common way to estimate this drift response is by using modal parameters identified under natural excitations. Although the modal parameters of shear structures cannot be identified accurately in the real environment, the identification error has little impact on the estimation when measurements from several floors are used. However, the estimation accuracy falls dramatically when there is only one accelerometer. This paper describes the susceptibility of single sensor identification to modelling error and simulations that preliminarily verified this characteristic. To make a robust evaluation from measurements of one floor of shear structures based on imprecisely identified parameters, a novel scheme is devised to approximately correct the mode shapes with respect to fictitious frequencies generated with a genetic algorithm; in particular, the scheme uses constrained minimization to take both the mathematical aspect and the realistic aspect of the mode shapes into account. The algorithm was validated by using a full-scale shear building. The differences between single-sensor and multiple-sensor estimations were analyzed. It was found that, as the number of accelerometers decreases, the error rises due to insufficient data and becomes very high when there is only one sensor. Moreover, when measurements for only one floor are available, the proposed method yields more precise and appropriate mode shapes, leading to a better estimation on the drift angle of the lower floors compared with a method designed for multiple sensors. As well, it is shown that the reduction in space complexity is offset by increasing the computation complexity.

• Journal of Korea Water Resources Association
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• v.56 no.8
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• pp.497-508
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• 2023

Agricultural reservoirs are crucial structures for water resources monitoring especially in Korea where the resources are seasonally unevenly distributed. Optical and Synthetic Aperture Radar (SAR) satellites, being utilized as tools for monitoring the reservoirs, have unique limitations in that optical sensors are sensitive to weather conditions and SAR sensors are sensitive to noises and multiple scattering over dense vegetations. In this study, we tried to improve water body detection accuracy through optical-SAR data fusion, and quantitatively analyze the complementary effects. We first detected water bodies at Edong, Cheontae reservoir using the Compact Advanced Satellite 500(CAS500), Kompsat-3/3A, and Sentinel-2 derived Normalized Difference Water Index (NDWI), and SAR backscattering coefficient from Sentinel-1 by K-means clustering technique. After that, the improvements in accuracies were analyzed by applying K-means clustering to the 2-D grid space consists of NDWI and SAR. Kompsat-3/3A was found to have the best accuracy (0.98 at both reservoirs), followed by Sentinel-2(0.83 at Edong, 0.97 at Cheontae), Sentinel-1(both 0.93), and CAS500(0.69, 0.78). By applying K-means clustering to the 2-D space at Cheontae reservoir, accuracy of CAS500 was improved around 22%(resulting accuracy: 0.95) with improve in precision (85%) and degradation in recall (14%). Precision of Kompsat-3A (Sentinel-2) was improved 3%(5%), and recall was degraded 4%(7%). More precise water resources monitoring is expected to be possible with developments of high-resolution SAR satellites including CAS500-5, developments of image fusion and water body detection techniques.

• Journal of Mushroom
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• v.17 no.4
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• pp.197-204
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• 2019

In this study, smart farm technology was used by farmers cultivating 'CHIKUMASSHU T-011' in order to develop an optimal growth model for the precision cultivation of bottle-grown winter mushroom and the results of the same are mentioned herein. Farmers participating in the experiment used 60 ㎡ of bed area with 4 rows and 13 columns of shelf shape, 20 horsepower refrigerator, 100T of sandwich panel for insulation, 6 ultrasonic humidifiers, 12 kW of heating, and 20,000 bottles of Flammulina velutipes mushroom spores. The temperature, humidity, and carbon dioxide concentrations, which directly affect the growth of the mushroom, were collected and analyzed from the environmental sensors installed at the winter mushroom cultivation area. The initial temperature was found to be 14.5℃, which was maintained at 14℃ to 15℃ until the 10th day. In the restriction phase, the initial temperature was 4℃ and was maintained between 2℃ and 3℃ until the 15th day, while during the growth phase, it was maintained between 7.5℃ to 9.5℃. Analysis of the humidity data revealed initial humidity to be 100%, which varied between 88% to 98% during primordia formation period. The humidity remained between 77% to 96% until the 15th day, in the restriction phase and between 75% to 83% during the growth phase. The initial carbon dioxide concentration was 3,500 ppm and varied between 3,500 ppm to 6,000 ppm during primordia formation period and was maintained at 6,000 ppm until the 15th day. During the growth phase, the carbon dioxide concentration was found to be over 6,000 ppm. Fruiting body characteristics of 'CHIKUMASSHU T-011' cultivated in the farmhouse were as follows: Pileus diameter of 7.5 mm and thickness of 4.1 mm, stipe thickness of 3.3 mm, and length of 154.2 mm. The number of valid fruiting bodies was 1,048 unit per 1,400 mL bottle, and the individual weight was 0.71 g per unit. The yield of fruiting bodies was 402.8 g per 1,400 mL bottle.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.7
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• pp.1342-1354
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• 2016

As Internet, and hardware technology are in rapid process, using rate and penetration rate of Internet of Things are increasing. Internet of Things is the physical objects with network which embedded with electronics, software, sensors, and network. Smart Home-kit to operate refrigerators, washing machines, light bulbs, and such internet of things by a smartphone has been realized. However, it is difficult to use a good quality of software based on IoT. It is because that the study related to quality evaluation of software based on IoT is deficient compared with increase amount of IoT devices. Software based on IoT includes mobility, transportability, real time accessibility and hardware characteristics. Therefore, it is necessary to have differentiated quality standards and quality model. Software quality evaluation model for IoT is proposed to satisfy these needs. Evaluation model is mapped by characteristics of IoT software based on ISO/IEC 25000's quality characteristics. Scenario based studies were applied to quality model for verification.

• Journal of the Institute of Electronics and Information Engineers
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• v.54 no.4
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• pp.50-58
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• 2017

Recently, some methods of converging heterogeneous fire sensor data have been proposed for effective fire detection, but the rule-based methods have low adaptability and accuracy, and the fuzzy inference methods suffer from detection speed and accuracy by lack of consideration for images. In addition, a few image-based deep learning methods were researched, but it was too difficult to rapidly recognize the fire event in absence of cameras or out of scope of a camera in practical situations. In this paper, we propose a novel fire detection system combining a deep learning algorithm based on CNN and fuzzy inference engine based on heterogeneous fire sensor data including temperature, humidity, gas, and smoke density. we show it is possible for the proposed system to rapidly detect fire by utilizing images and to decide fire in a reliable way by utilizing multi-sensor data. Also, we apply distributed computing architecture to fire detection algorithm in order to avoid concentration of computing power on a server and to enhance scalability as a result. Finally, we prove the performance of the system through two experiments by means of NIST's fire dynamics simulator in both cases of an explosively spreading fire and a gradually growing fire.

• Journal of Sensor Science and Technology
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• v.29 no.2
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• pp.93-99
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• 2020

A colorimetric paper sensor was used to detect volatile nitrogen-containing compounds emitted from spoiled salmon filets to determine their freshness. The sensing mechanism was based on acid-base reactions between acidic pH-indicating dyes and basic volatile ammonia and amines. A sensing layer was simply fabricated by drop-casting a dye solution of bromocresol green (BCG) on a polyvinylidene fluoride substrate, and its color-change response was enhanced by optimizing the amounts of additive chemicals, such as polyethylene glycol, p-toluene sulfonic acid, and graphene oxide in the dye solution. To avoid the adverse effects of water vapor, both faces of the sensing layer were enclosed by using a polyethylene terephthalate film and a gas-permeable microporous polytetrafluoroethylene sheet, respectively. When exposed to basic gas analytes, the paper-like sensor distinctly exhibited a color change from initially yellow, then to green, and finally to blue due to the deprotonation of BCG via the Brønsted acid-base reaction. The use of ammonia analyte as a test gas confirmed that the sensing performance of the optimized sensor was reversible and excellent (detection time of < 15 min, sensitive naked-eye detection at 0.25 ppm, good selectivity to common volatile organic gases, and good stability against thermal stress). Finally, the coloration intensity of the sensor was quantified as a function of the storage time of the salmon filet at 28℃ to evaluate its usefulness in monitoring of the food freshness with the measurement of the total viable count (TVC) of microorganisms in the food. The TVC value increased from 3.2 × 10⁵ to 3.1 × 10⁹ cfu/g in 28 h and then became stable, whereas the sensor response abruptly changed in the first 8 h and slightly increased thereafter. This result suggests that the colorimetric response could be used as an indicator for evaluating the degree of decay of salmon induced by microorganisms.

• KIPS Transactions on Software and Data Engineering
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• v.4 no.3
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• pp.99-110
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• 2015

Internet-of-Things(IoT) is the computing paradigm converged with different technologies, where diverse devices are connected via the wireless network, acquire environmental information from their equipped sensors, and actuated. IoT applications typically provide smart services to users by interacting with multiple devices connected to the network and are designed by integrating multiple technologies such as sensor network, communication technologies, and software engineering. Moreover, since the concept of IoT has been introduced recently, most of the researches are in the beginning step, which is too early to be practically applied. Due to these facts, developing IoT application results in unconventional technical challenges which have not been observed in typical software applications. And, it is not straightforward to apply conventional project guidelines to IoT application development projects. Hence, there can be many difficulties to successfully complete the projects. Therefore, for successful completion of the projects, we analyze technical challenges occurring in all phases of the project lifecycle, i.e. project preparation stage and development stage. And, we propose the effective solutions to overcome the issues. To verify identified issues and presented solutions, we present the result of applying the solutions to an IoT application development. Through the case study, we evaluate how reasonable the unconventional technical issues are generated and analyze effectiveness of applying the solutions to the application.

• KIPS Transactions on Computer and Communication Systems
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• v.5 no.10
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• pp.347-352
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• 2016

Recently, IoT technology has been applied in various field. In particular, the technology focuses on analysing large amount of data that has been gathered from the environmental sensors, to provide valuable information. This technique has been actively researched in the agro-industrial sector. Many researches are underway in the monitoring and control for growth crop environment in agro-industrial. Normally, the average weather data is provided by the manual agro-control method but the value may differ due to the different region's weather and environment that may cause problem in the disease and insect-pest prevention. In order to develop a suitable integrated system for fruit tree, all the necessary information is obtained from the Jeollanam-do province, which has the high production rate in the Korea. In this paper, we propose an integrated support platform for the growing crops, to minimize the damage caused due to the weather disaster through image analysis, forecasting models, by using the micro-climate weather information collection and CCTV. The fruit tree damage caused by the weather disaster are controlled by utilizing various IoT technology by maintaining the growth environment, which helps in the disease and insect-pest prevention and also helps farmers to improve the expected production.