• Smart Structures and Systems
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• v.29 no.1
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• pp.195-206
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• 2022

Crack detection is essential for inspection of existing structures and crack segmentation based on deep learning is a significant solution. However, datasets are usually one of the key issues. When building a new dataset for deep learning, laborious and time-consuming annotation of a large number of crack images is an obstacle. The aim of this study is to develop an approach that can automatically select a small portion of the most informative crack images from a large pool in order to annotate them, not to label all crack images. An active learning method with difficulty learning mechanism for crack segmentation tasks is proposed. Experiments are carried out on a crack image dataset of a steel box girder, which contains 500 images of 320×320 size for training, 100 for validation, and 190 for testing. In active learning experiments, the 500 images for training are acted as unlabeled image. The acquisition function in our method is compared with traditional acquisition functions, i.e., Query-By-Committee (QBC), Entropy, and Core-set. Further, comparisons are made on four common segmentation networks: U-Net, DeepLabV3, Feature Pyramid Network (FPN), and PSPNet. The results show that when training occurs with 200 (40%) of the most informative crack images that are selected by our method, the four segmentation networks can achieve 92%-95% of the obtained performance when training takes place with 500 (100%) crack images. The acquisition function in our method shows more accurate measurements of informativeness for unlabeled crack images compared to the four traditional acquisition functions at most active learning stages. Our method can select the most informative images for annotation from many unlabeled crack images automatically and accurately. Additionally, the dataset built after selecting 40% of all crack images can support crack segmentation networks that perform more than 92% when all the images are used.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.199-201
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• 2021

The Internet of Things (IoT) is a technology that can organically connect people and things without time and space constraints by using communication network technology and sensors, and transmit and receive data in real time. The IoT used in all industrial fields has limitations in terms of storage allocation, such as device size, memory capacity, and data transmission performance, so it is important to manage power consumption to effectively utilize the limited battery capacity. In the prior research, there is a problem in that security is deteriorated instead of improving power efficiency by lightening the security algorithm of the encryption module. In this study, we proposes a low-power security architecture that can utilize high-performance security algorithms in the IoT environment. This can provide high security and power efficiency by using relatively complex security modules in low-power environments by executing security modules only when threat detection is required based on inspection results.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.34 no.7
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• pp.929-933
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• 2010

Four-point bending tests were performed to investigate the interfacial adhesion of Cu-Cu bonding fabricated by thermo-compression process for three dimensional packaging. A pair of Cu-coated Si wafers was bonded under a pressure of 15 kN at $350^{\circ}C$ for 1 h, followed by post annealing at $350^{\circ}C$ for 1 h. The bonded wafers were diced into $30\;mm\;{\times}\;3\;mm$ pieces for the test. Each specimen had a $400-{\mu}m$-deep notch along the center. An optical inspection module was installed in the testing apparatus to observe crack initiation at the notch and crack propagation over the weak interface. The tests were performed under a fixed loading speed, and the corresponding load was measured. The measured interfacial adhesion energy of the Cu-to-Cu bonding was $9.75\;J/m^2$, and the delaminated interfaces were analyzed after the test. The surface analysis shows that the delamination occurred in the interface between $SiO_2$ and Ti.

• Research in Plant Disease
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• v.19 no.1
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• pp.36-44
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• 2013

Carnation is considered to be one of the top three cutting flowers in the world, which is a main crop with 21 billion annual volume of manufacture. The four carnation items such as cuttings, seed, plant and unrooted cuttings are imported and exported. Viruses can be easily transmitted during vegetative propagation of carnation. Carnation necrotic fleck virus (CNFV) and Carnation ringspot virus (CRSV) are designated as Korea plant quarantine viruses and inspected. This study was aimed to develop specific primer sets for easy and rapid detection of CNFV and CRSV. Two RT-PCR primer sets were efficiently amplified 288 and 447 bp fragments for CNFV and 503 549 bp fragments for CRSV. Furthermore, developed nested primer sets make possible to high sensitive detection and verification. CNFV nested PCR primer sets all produced band of 147 bp and CRSV nested PCR primer sets did bands of 395 and 347 bp. In addition, plasmid inserted 6 sequences in amplicon were used as a positive control to improve inspection confidence. The successful application of PCR module newly developed in this study will be highly useful for detect of CNFV and CRSV for quarantine inspections.

• Journal of IKEEE
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• v.24 no.1
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• pp.25-32
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• 2020

In this paper, we propose an image generator for OLED panel test that can compensate for color coordinates and luminance by using panel defect inspection and optical measurement while displaying images on OLED panel. The proposed image generator consists of two processes: the image generation process and the process of compensating color coordinates and luminance using optical measurement. In the image generating process, the panel is set to receive the panel information to drive the panel, and the image is output by adjusting the output setting of the image generator according to the panel information. The output form of the image is configured by digital RGB method. The pattern generation algorithm inside the image generator outputs color and gray image data by transmitting color data to a 24-bit data line based on a synchronization signal according to the resolution of the panel. The process of compensating color coordinates and luminance using optical measurement outputs an image to an OLED panel in an image generator, and compensates for a portion where color coordinates and luminance data measured by an optical module differ from reference data. To evaluate the accuracy of the image generator for the OLED panel test proposed in this paper, Xilinx's Spartan 6 series XC6SLX25-FG484 FPGA was used and the design tool was ISE 14.5. The output of the image generation process was confirmed that the target setting value and the simulation result value for the digital RGB output using the oscilloscope matched. Compensating the color coordinates and luminance using optical measurements showed accuracy within the error rate suggested by the panel manufacturer.

• Resources Recycling
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• v.29 no.4
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• pp.15-30
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• 2020

Recently, it is increasing a amount of installed solar-cell rapidly, and end-of-life photovoltaic(ELP) modules are generated in according to the reduction of cell efficiency largely. Recycling of ELP modules are begun at an advanced nation already, but there are bring about environmental contamination and resource recovery problems owing to not treated ELP modules because of economic cost completely. First of all, there were researched basic study for treatment conditions of used solar cell inspection, dismantling of aluminum frame, crushing / grinding & separation of tempered glass, removal of back sheet & EVA film, leaching & precipitation recovery of valuable metals and treatment of waste water. Therefore, we establish optimum conditions through carried out of designed apparatus, installation of equipment, test operation & trouble shooting in scale of 1ton/day pilot plant test. Following to economic review, it does have the economic efficiency until to the case of tempered glass recovery, but does not have the economic value in case of total processes until to recover the valuable metals. However, there are guaranteed economic value if we are gained a large amount of the expenses through EPR supported system. It was confirmed the commercialized possibility of ELP modules recycling if there were established on the collecting ELP modules, reusing criteria, economical technology, enactment of directives and enforcement of EPR supported system efficiently.

• Korean journal of applied entomology
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• v.51 no.3
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• pp.235-243
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• 2012

Population dynamics of the American serpentine leafminer, Liriomyza trifolii (Burgess), were observed and modeled in order to compare the effects of air and tomato leaf temperatures inside a greenhouse using DYMEX model builder and simulator (pre-programed module based simulation programs developed by CSIRO, Australia). The DYMEX model simulator consisted of a series of modules with the parameters of temperature dependent development and oviposition models of L. trifolii were incorporated from pre-published data. Leaf surface temperatures of cherry tomato leaves (cv. 'Koko') were monitored according to three tomato plant positions (top, > 1.8 m above the ground level; middle, 0.9 - 1.2 m; bottom, 0.3 - 0.5 m) using an infrared temperature gun. Air temperature was monitored at the same three positions using a self-contained temperature logger. Data sets for the observed air temperature and average leaf surface temperatures were collected (top and bottom surfaces), and incorporated into the DYMEX simulator in order to compare the effects of air and leaf surface temperature on the population dynamics of L. trifolii. The initial population consisted of 50 eggs, which were laid by five female L. trifolii in early June. The number of L. trifolii larvae was counted by visual inspection of the tomato plants in order to verify the performance of DYMEX simulation. The egg, pupa, and adult stage of L. trifolii could not be counted due to its infeasible of visual inspection. A significant positive correlation between the observed and the predicted numbers of larvae was found when the leaf surface temperatures were incorporated into the DYMEX simulation (r = 0.97, p < 0.01), but no significant positive correlation was observed with air temperatures(r = 0.40, p = 0.18). This study demonstrated that the population dynamics of L. trifolii was affected greatly by the leaf temperatures, though to little discernible degree by the air temperatures, and thus the leaf surface temperature should be for a consideration in the management of L. trifolii within cherry tomato greenhouses.

• Horticultural Science & Technology
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• v.29 no.5
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• pp.420-432
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• 2011

Population dynamics of greenhouse whitefly, Trialeurodes vaporariorum (Westwood), were modeled and simulated to compare the temperature effects of air and tomato leaf inside greenhouse using DYMEX model simulator (pre-programed module based simulation program developed by CSIRO, Australia). The DYMEX model simulator consisted of temperature dependent development and oviposition modules. The normalized cumulative frequency distributions of the developmental period for immature and oviposition frequency rate and survival rate for adult of greenhouse whitefly were fitted to two-parameter Weibull function. Leaf temperature on reversed side of cherry tomato leafs (Lycopersicon esculentum cv. Koko) was monitored according to three tomato plant positions (top, > 1.6 m above the ground level; middle, 0.9 - 1.2 m; bottom, 0.3 - 0.5 m) using an infrared temperature gun. Air temperature was monitored at same three positions using a Hobo self-contained temperature logger. The leaf temperatures from three plant positions were described as a function of the air temperatures with 3-parameter exponential and sigmoidal models. Data sets of observed air temperature and predicted leaf temperatures were prepared, and incorporated into the DYMEX simulator to compare the effects of air and leaf temperature on population dynamics of greenhouse whitefly. The number of greenhouse whitefly immatures was counted by visual inspection in three tomato plant positions to verify the performance of DYMEX simulation in cherry tomato greenhouse where air and leaf temperatures were monitored. The egg stage of greenhouse whitefly was not counted due to its small size. A significant positive correlation between the observed and the predicted numbers of immature and adults were found when the leaf temperatures were incorporated into DYMEX simulation, but no significant correlation was observed with the air temperatures. This study demonstrated that the population dynamics of greenhouse whitefly was affected greatly by the leaf temperatures, rather than air temperatures, and thus the leaf surface temperature should be considered for management of greenhouse whitefly in cherry tomato grown in greenhouses.