• Journal of the Korea Convergence Society
• /
• v.10 no.11
• /
• pp.95-100
• /
• 2019

In this paper, autonomous vehicles of service with existing vehicle accident for the prevention of the vehicle communication technology, self-driving techniques, brakes automatic control technology, artificial intelligence technologies such as well and developed the vehicle accident this occur to death or has been techniques, can prepare various safety cases intended to minimize the injury. In this paper, it is a study to secure safety in autonomous vehicles. This is determined according to spatial factors such as chip signals for general low-power short-range wireless communication and micro road AI. On the other hand, in this paper, the safety of boarding is improved by checking the signal from the electronic chip, up to "recognition of the emotion from residence time in the sensing area" to the biological electronic chip. As a result of demonstrating the reliability of the world countries the world, inducing safety autonomous system of all passengers in terms of safety. Unmanned autonomous vehicle riding and commercialization will lead to AI systems and biochips (Verification), linked IoT on the road in the near future, and the safety technology reliability of the world will be highlighted.

• Journal of Convergence for Information Technology
• /
• v.11 no.10
• /
• pp.45-52
• /
• 2021

In this paper, we proposed an Atrous Residual U-Net (AR-UNet) to improve the segmentation accuracy of semantic segmentation method based on U-Net. The U-Net is mainly used in fields such as medical image analysis, autonomous vehicles, and remote sensing images. The conventional U-Net lacks extracted features due to the small number of convolution layers in the encoder part. The extracted features are essential for classifying object categories, and if they are insufficient, it causes a problem of lowering the segmentation accuracy. Therefore, to improve this problem, we proposed the AR-UNet using residual learning and ASPP in the encoder. Residual learning improves feature extraction ability and is effective in preventing feature loss and vanishing gradient problems caused by continuous convolutions. In addition, ASPP enables additional feature extraction without reducing the resolution of the feature map. Experiments verified the effectiveness of the AR-UNet with Cityscapes dataset. The experimental results showed that the AR-UNet showed improved segmentation results compared to the conventional U-Net. In this way, AR-UNet can contribute to the advancement of many applications where accuracy is important.

• Journal of Biosystems Engineering
• /
• v.43 no.4
• /
• pp.426-439
• /
• 2018

Purpose: To continuously monitor soil and climatic properties, a data acquisition system (DAQ) was developed and tested in plum farms (Gyewol-ri and Haechang-ri, Suncheon, Korea). Methods: The DAQ consisted of a Raspberry-Pi processor, a modem, and an ADC board with multiple sensors (soil moisture content (SEN0193), soil temperature (DS18B20), climatic temperature and humidity (DHT22), and rainfall gauge (TR-525M)). In the laboratory, various tests were conducted to calibrate SEN0193 at different soil moistures, soil temperatures, depths, and bulk densities. For performance comparison of the SEN0193 sensor, two commercial moisture sensors (SMS-BTA and WT-1000B) were tested in the field. The collected field data in Raspberry-Pi were transmitted and stored on a web server database through a commercial communications wireless network. Results: In laboratory tests, it was found that the SEN0193 sensor voltage reading increased significantly with an increase in soil bulk density. A linear calibration equation was developed between voltage and soil moisture content depending on the farm soil bulk density. In field tests, the SEN0193 sensor showed linearity (R = 0.76 and 0.73) between output voltage and moisture content; however, the other two sensors showed no linearity, indicating that site-specific calibration is important for accurate sensing. In the long-term monitoring results, it was observed that the measured climate temperature was almost the same as website information. Soil temperature information was higher than the values measured by DS18B20 during spring and summer. However, the local rainfall measured using TR 525M was significantly different from the values on the website. Conclusion: Based on the test results obtained using the developed monitoring system, it is thought that the measurement of various parameters using one device would be helpful in monitoring plum growth. Field data from the local farm monitoring system can be coupled with website information from the weather station and used more efficiently.

• Food Science of Animal Resources
• /
• v.43 no.6
• /
• pp.1150-1169
• /
• 2023

Edible insects are gaining popularity as a potential future food source because of their high protein content and efficient use of space. Black soldier fly larvae (BSFL) are noteworthy because they can be used as feed for various animals including reptiles, dogs, fish, chickens, and pigs. However, if the edible insect industry is to advance, we should use automation to reduce labor and increase production. Consequently, there is a growing demand for sensing technologies that can automate the evaluation of insect quality. This study used short-wave infrared (SWIR) hyperspectral imaging to predict the proximate composition of dried BSFL, including moisture, crude protein, crude fat, crude fiber, and crude ash content. The larvae were dried at various temperatures and times, and images were captured using an SWIR camera. A partial least-squares regression (PLSR) model was developed to predict the proximate content. The SWIR-based hyperspectral camera accurately predicted the proximate composition of BSFL from the best preprocessing model; moisture, crude protein, crude fat, crude fiber, and crude ash content were predicted with high accuracy, with R² values of 0.89 or more, and root mean square error of prediction values were within 2%. Among preprocessing methods, mean normalization and max normalization methods were effective in proximate prediction models. Therefore, SWIR-based hyperspectral cameras can be used to create automated quality management systems for BSFL.

• Journal of Bio-Environment Control
• /
• v.23 no.2
• /
• pp.158-166
• /
• 2014

This experiment has investigated suitable methods to improve precision water content monitoring of coconut coir substrates to control irrigation by frequency domain reflectometry(FDR) sensors. Specifically, water content changes and variations were observed at different sensing distances and positions from the irrigation dripper location, and different spaces between the FDR sensors with or without noise filters. Commercial coconut coir substrates containing different ratios of dust and chips(10:0, 7:3, 5:5, 3:7) were used. On the upper side and the side of the substrates, a FDR sensor was used at 5, 10, 20, 30cm distances respectively from the irrigation dripper point, and water content was measured by time after the irrigation. In the glass beads, sensors were installed with or without noise filtering. Closer sensing distance had a higher water content increasing rate, regardless of different coir substrate ratios. There were no differencies of water content increasing rates in 10:0 and 3:7 substrates between the upper side and the side. Whereas, 7:3 and 5:5 substrates showed higher increasing rates on the upper side measurements. Substrates with higher ratios of chip(3:7) had lower increasing rates than others. And, with noise filters, the exatitude of measurement was improved because the variation and deviation were reduced. Therefore, in coconut coir with FDR sensors, an efficient water content measurment to control irrigations can be achieved by installing sensors closer to an irrigation point and upper side of substrates with noise filters.

• Journal of Bio-Environment Control
• /
• v.29 no.2
• /
• pp.141-152
• /
• 2020

The rapid on-site measurement of hydroponic nutrients allows for the more efficient use of crop fertilizers. This paper reports on the development of an embedded on-site system consisting of multiple ion-selective electrodes (ISEs) for the real-time measurement of the concentrations of macronutrients in hydroponic solutions. The system included a combination of PVC ISEs for the detection of NO₃, K, and Ca ions, a cobalt-electrode for the detection of H₂PO₄, a double-junction reference electrode, a solution container, and a sampling system consisting of pumps and valves. An Arduino Due board was used to collect data and to control the volume of the sample. Prior to the measurement of each sample, a two-point normalization method was employed to adjust the sensitivity followed by an offset to minimize potential drift that might occur during continuous measurement. The predictive capabilities of the NO₃ and K ISEs based on PVC membranes were satisfactory, producing results that were in close agreement with the results of standard analyzers (R² = 0.99). Though the Ca ISE fabricated with Ca ionophore II underestimated the Ca concentration by an average of 55%, the strong linear relationship (R² > 0.84) makes it possible for the embedded system to be used in hydroponic NO₃, K, and Ca sensing. The cobalt-rod-based phosphate electrodes exhibited a relatively high error of 24.7±9.26% in the phosphate concentration range of 45 to 155 mg/L compared to standard methods due to inconsistent signal readings between replicates, illustrating the need for further research on the signal conditioning of cobalt electrodes to improve their predictive ability in hydroponic P sensing.

• Journal of the Korean Society of Food Science and Nutrition
• /
• v.44 no.6
• /
• pp.904-911
• /
• 2015

The objective of this study was to investigate the quality characteristics of traditional wine using an radio-frequency identification (RFID) system annexed to a fermenter. In this study, we proposed an RFID-based data transmission scheme for monitoring fermentation of traditional alcoholic beverages. The pH, total acidity, total sugar, soluble sugar, free sugar, alcohol content, and organic acids of were investigated and subjected to fermentation of traditional alcoholic beverages three times. The pH ranged from 7.98, 7.95, and 7.68 at day 0, decreased drastically to 3.31~2.96 at day 2, and then slowly increased to the end point, finally reaching 3.34 at day 20. Acidity tended to increase quickly with time, especially for all samples after day 2. The fermentation environment induced a sudden increase acidity in reactants and indicated a low pH. The total sugars during fermentation quickly decreased to the range of 20.3, 22.43, and 19.2% at day 2, and the slope of reduction steadily decreased to 5.1, 6.1, and 4.8% at day 10. On the other hand, the alcohol content showed the reverse trend as total sugars. The alcohol content also showed the same pattern as total acids, showing the highest alcohol content of 17.3% (v/v) on day 20. In this study on traditional wine fermentation using an RFID system, we showed that pH, soluble sugar, and alcohol content can be adopted as key indicators for quality control and standardization of traditional wine manufacturing.

• Korean Journal of Remote Sensing
• /
• v.39 no.5_1
• /
• pp.521-535
• /
• 2023

When converting rice fields into fields,sorghum (sorghum bicolor L. Moench) has excellent moisture resistance, enabling stable production along with soybeans. Therefore, it is a crop that is expected to improve the self-sufficiency rate of domestic food crops and solve the rice supply-demand imbalance problem. However, there is a lack of fundamental statistics,such as cultivation fields required for estimating yields, due to the traditional survey method, which takes a long time even with a large manpower. In this study, U-Net was applied to RGB images based on unmanned aerial vehicle to confirm the possibility of non-destructive segmentation of sorghum cultivation fields. RGB images were acquired on July 28, August 13, and August 25, 2022. On each image acquisition date, datasets were divided into 6,000 training datasets and 1,000 validation datasets with a size of 512 × 512 images. Classification models were developed based on three classes consisting of Sorghum fields(sorghum), rice and soybean fields(others), and non-agricultural fields(background), and two classes consisting of sorghum and non-sorghum (others+background). The classification accuracy of sorghum cultivation fields was higher than 0.91 in the three class-based models at all acquisition dates, but learning confusion occurred in the other classes in the August dataset. In contrast, the two-class-based model showed an accuracy of 0.95 or better in all classes, with stable learning on the August dataset. As a result, two class-based models in August will be advantageous for calculating the cultivation fields of sorghum.

• Korean Journal of Remote Sensing
• /
• v.39 no.5_1
• /
• pp.669-681
• /
• 2023

Studies have tried to apply remote sensing technology, a non-destructive survey method, instead of the existing destructive survey, which requires relatively large labor input and a long time to estimate chlorophyll content, which is an important indicator for evaluating the growth of fruit trees. This study was conducted to non-destructively evaluate the chlorophyll content of pear tree leaves using unmanned aerial vehicle-based hyperspectral imagery for two years(2021, 2022). The reflectance of the single bands of the pear tree canopy extracted through image processing was band rationed to minimize unstable radiation effects depending on time changes. The estimation (calibration and validation) models were developed using machine learning algorithms of elastic-net, k-nearest neighbors(KNN), and support vector machine with band ratios as input variables. By comparing the performance of estimation models based on full band ratios, key band ratios that are advantageous for reducing computational costs and improving reproducibility were selected. As a result, for all machine learning models, when calibration of coefficient of determination (R²)≥0.67, root mean squared error (RMSE)≤1.22 ㎍/cm², relative error (RE)≤17.9% and validation of R²≥0.56, RMSE≤1.41 ㎍/cm², RE≤20.7% using full band ratios were compared, four key band ratios were selected. There was relatively no significant difference in validation performance between machine learning models. Therefore, the KNN model with the highest calibration performance was used as the standard, and its key band ratios were 710/714, 718/722, 754/758, and 758/762 nm. The performance of calibration showed R²=0.80, RMSE=0.94 ㎍/cm², RE=13.9%, and validation showed R²=0.57, RMSE=1.40 ㎍/cm², RE=20.5%. Although the performance results based on validation were not sufficient to estimate the chlorophyll content of pear tree leaves, it is meaningful that key band ratios were selected as a standard for future research. To improve estimation performance, it is necessary to continuously secure additional datasets and improve the estimation model by reproducing it in actual orchards. In future research, it is necessary to continuously secure additional datasets to improve estimation performance, verify the reliability of the selected key band ratios, and upgrade the estimation model to be reproducible in actual orchards.

• Proceedings of the Korean Vacuum Society Conference
• /
• 2016.02a
• /
• pp.268-268
• /
• 2016

Chemical sensors have attracted much attention due to their various applications such as agriculture product, cosmetic and pharmaceutical components and clinical control. A conventional chemical and biological sensor is consists of fluorescent dye, optical light sources, and photodetector to quantify the extent of concentration. Such complicated system leads to rising cost and slow response time. Until now, the most contemporary thin film transistors (TFTs) are used in the field of flat panel display technology for switching device. Some papers have reported that an interesting alternative to flat panel display technology is chemical sensor technology. Recent advances in chemical detection study for using TFTs, benefits from overwhelming progress made in organic thin film transistors (OTFTs) electronic, have been studied alternative to current optical detection system. However numerous problems still remain especially the long-term stability and lack of reliability. On the other hand, the utilization of metal oxide transistor technology in chemical sensors is substantially promising owing to many advantages such as outstanding electrical performance, flexible device, and transparency. The top-gate structure transistor indicated long-term atmosphere stability and reliability because insulator layer is deposited on the top of semiconductor layer, as an effective mechanical and chemical protection. We report on the fabrication of InGaZnO TFTs with silver nanowire as the top gate electrode for the aim of chemical materials detection by monitoring change of electrical properties. We demonstrated that the improved sensitivity characteristics are related to the employment of a unique combination of nano materials. The silver nanowire top-gate InGaZnO TFTs used in this study features the following advantages: i) high sensitivity, ii) long-term stability in atmosphere and buffer solution iii) no necessary additional electrode and iv) simple fabrication process by spray.