• Molecules and Cells
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• v.43 no.6
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• pp.530-538
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• 2020

The Gustatory system enables animals to detect toxic bitter chemicals, which is critical for insects to survive food induced toxicity. Cucurbitacin is widely present in plants such as cucumber and gourds that acts as an anti-herbivore chemical and an insecticide. Cucurbitacin has a harmful effect on insect larvae as well. Although various beneficial effects of cucurbitacin such as alleviating hyperglycemia have also been documented, it is not clear what kinds of molecular sensors are required to detect cucurbitacin in nature. Cucurbitacin B, a major bitter component of bitter melon, was applied to induce action potentials from sensilla of a mouth part of the fly, labellum. Here we identify that only Gr33a is required for activating bitter-sensing gustatory receptor neurons by cucurbitacin B among available 26 Grs, 23 Irs, 11 Trp mutants, and 26 Gr-RNAi lines. We further investigated the difference between control and Gr33a mutant by analyzing binary food choice assay. We also measured toxic effect of Cucurbitacin B over 0.01 mM range. Our findings uncover the molecular sensor of cucurbitacin B in Drosophila melanogaster. We propose that the discarded shell of Cucurbitaceae can be developed to make a new insecticide.

• Journal of Sensor Science and Technology
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• v.13 no.6
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• pp.405-410
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• 2004

Compensation wires for Pt/Pd thermocouple was manufactured using Cu/Ni alloys. Their thermoelectric voltage has been tested from room temperature to about $150^{\circ}C$. Alloys of $Cu_{95.5}Ni_{4.5}$ and $Cu_{89.5}Ni_{10.5}$ introduced only small emf differences to Pt/Pd thermocouples, indicating a real possibility of industrial use. Above $1000^{\circ}C$, the temperature difference was expected to he small as ${\pm}0.5^{\circ}C$, and the difference would be minimized by adjusting the Ni content with a small amount.

• Journal of Powder Materials
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• v.9 no.4
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• pp.267-272
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• 2002

The production of micro components is one of the leading technologies in the fields of information and communiation, medical and biotechnology, and micro sensor and micro actuator system. Microfabrication (micromachining) techniques such as X-ray lithography, electroforming, micromolding and excimer laser ablation are used for the production of micro components out of silicon, polymer and a limited number of pure metals or binary alloys. However, since the first development of microfabrication technologies there have been demands for the cost-effective replication in large scale series as well as the extended range of available material. One such promising process is micro powder injection molding (PIM), which inherits the advantages of the conventional PIM technology, such as low production cost, shape complexity, applicability to many materials, applicability to many materials, and good tolerance. This paper reports on a fundamental investigation of the application of W-Cu powder to micro metal injection molding (MIM), especially in view of achieving a good filling and a safe removal of a micro mold conducted in the experiment. It is absolutely legitimate and meaningful, at the present state of the technique, to continue developing the micro MIM towards production processes for micro components.

• Journal of Communications and Networks
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• v.14 no.5
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• pp.471-480
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• 2012

Reliable detection of primary user activity increases the opportunity to access temporarily unused bands and prevents harmful interference to the primary system. By extracting a global decision from local sensing results, cooperative sensing achieves high reliability against multipath fading. For the effective combining of sensing results, which is generalized by a likelihood ratio test, the fusion center should learn some parameters, such as the probabilities of primary transmission, false alarm, and detection at the local sensors. During the training period in supervised learning, the on/off log of primary transmission serves as the output label of decision statistics from the local sensor. In this paper, we extend unsupervised learning techniques with an expectation maximization algorithm for cooperative spectrum sensing, which does not require an external primary transmission log. Local sensors report binary hard decisions to the fusion center and adjust their operating points to enhance learning performance. Increasing the number of sensors, the joint-expectation step makes a confident classification on the primary transmission as in the supervised learning. Thereby, the proposed scheme provides accurate parameter estimates and a fast convergence rate even in low signal-to-noise ratio regimes, where the primary signal is dominated by the noise at the local sensors.

• Journal of the Korean Society of Industry Convergence
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• v.23 no.6_2
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• pp.1025-1032
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• 2020

In this paper, we propose a method to classify road weather conditions into rain, fog, and sun using a SVM (Support Vector Machine) classifier after extracting weather features from images acquired in real time using an optical sensor installed on a roadside post. A multi-dimensional weather feature vector consisting of factors such as image sharpeness, image entropy, Michelson contrast, MSCN (Mean Subtraction and Contrast Normalization), dark channel prior, image colorfulness, and local binary pattern as global features of weather-related images was extracted from road images, and then a road weather classifier was created by performing machine learning on 700 sun images, 2,000 rain images, and 1,000 fog images. Finally, the classification performance was tested for 140 sun images, 510 rain images, and 240 fog images. Overall classification performance is assessed to be applicable in real road services and can be enhanced further with optimization along with year-round data collection and training.

• Journal of Sensor Science and Technology
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• v.30 no.1
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• pp.20-24
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• 2021

The electrocardiogram (ECG) is one of the most extensively employed signals used to diagnose and predict cardiovascular diseases (CVDs). In recent years, several deep learning (DL) models have been proposed to improve detection accuracy. Among these, deep neural networks (DNNs) are the most popular, wherein the features are extracted automatically. Despite the increment in classification accuracy, DL models require exorbitant computational resources and power. This causes the mapping of DNNs to be slow; in addition, the mapping is challenging for a wearable device. Embedded systems have constrained power and memory resources. Therefore full-precision DNNs are not easily deployable on devices. To make the neural network faster and more power-efficient, spiking neural networks (SNNs) have been introduced for fewer operations and less complex hardware resources. However, the conventional SNN has low accuracy and high computational cost. Therefore, this paper proposes a new binarized SNN which modifies the synaptic weights of SNN constraining it to be binary (+1 and -1). In the simulation results, this paper compares the DL models and SNNs and evaluates which model is optimal for ECG classification. Although there is a slight compromise in accuracy, the latter proves to be energy-efficient.

• Journal of Korea Multimedia Society
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• v.13 no.9
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• pp.1337-1347
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• 2010

Fingerprint recognition is a biometric technology to identify individual by using fingerprint features such ridges and valleys. Most fingerprint systems perform the recognition based on minutiae points after acquiring a fingerprint image from contact type sensor. They have an advantage of acquiring a clear image of uniform size by touching finger on the sensor. However, they have the problems of the image quality can be reduced in case of severely dry or wet finger due to the variations of touching pressure and latent fingerprint on the sensor. To solve these problems, the contactless capturing devices for a fingerprint image was introduced in previous works. However, the accuracy of detecting minutiae points and recognition performance are reduced due to the degradation of image quality by the illumination variation. So, this paper proposes a new LDP-based fingerprint recognition method. It can effectively extract fingerprint patterns of iterative ridges and valleys. After producing histograms of the binary codes which are extracted by the LDP method, chi square distance between the enrolled and input feature histograms is calculated. The calculated chi square distance is used as the score of fingerprint recognition. As the experimental results, the EER of the proposed approach is reduced by 0.521% in comparison with that of the previous LBP-based fingerprint recognition approach.

• Journal of Internet Computing and Services
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• v.17 no.6
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• pp.53-59
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• 2016

Recently, huge stream data have been generated in real time from various applications such as wireless sensor networks, Internet of Things services, and social network services. For this reason, to develop an efficient method have become one of significant issues in order to discover useful information from such data by processing and analyzing them and employing the information for better decision making. Since stream data are generated continuously and rapidly, there is a need to deal with them through the minimum access. In addition, an appropriate method is required to analyze stream data in resource limited environments where fast processing with low power consumption is necessary. To address this issue, the sliding window model has been proposed and researched. Meanwhile, one of data mining techniques for finding meaningful information from huge data, pattern mining extracts such information in pattern forms. Frequency-based traditional pattern mining can process only binary databases and treats items in the databases with the same importance. As a result, frequent pattern mining has a disadvantage that cannot reflect characteristics of real databases although it has played an essential role in the data mining field. From this aspect, high utility pattern mining has suggested for discovering more meaningful information from non-binary databases with the consideration of the characteristics and relative importance of items. General high utility pattern mining methods for static databases, however, are not suitable for handling stream data. To address this issue, sliding window based high utility pattern mining has been proposed for finding significant information from stream data in resource limited environments by considering their characteristics and processing them efficiently. In this paper, we conduct various experiments with datasets for performance evaluation of sliding window based high utility pattern mining algorithms and analyze experimental results, through which we study their characteristics and direction of improvement.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.44 no.2
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• pp.263-270
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• 2024

As unmanned aerial vehicles(UAVs) and sensors have been developed in a variety of ways, it has become possible to update information on the ground faster than existing aerial photography or remote sensing. However, acquisition and input of ground control points(GCPs) UAV photogrammetry takes a lot of time, and geometric distortion occurs if measurement and input of GCPs are incorrect. In this study, RGB-based orthophotos were generated to reduce GCPs measurment and input time, and comparison and evaluation were performed by applying feature point algorithms to target orthophotos from various sensors. Four feature point extraction algorithms were applied to the two study sites, and as a result, speeded up robust features(SURF) was the best in terms of the ratio of matching pairs to feature points. When compared overall, the accelerated-KAZE(AKAZE) method extracted the most feature points and matching pairs, and the binary robust invariant scalable keypoints(BRISK) method extracted the fewest feature points and matching pairs. Through these results, it was confirmed that the AKAZE method is superior when performing geometric correction of the objective orthophoto for each sensor.

• Journal of the Korean Society for Precision Engineering
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• v.30 no.9
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• pp.901-908
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• 2013

The introduction of industrial intelligent robot using vision sensor has been interested in automated factory. 2D and 3D vision sensors have used to recognize object and to estimate object pose, which is for packaging parts onto a complete whole. But it is not trivial task due to illumination and various types of objects. Object image has distorted due to illumination that has caused low reliability in recognition. In this paper, recognition method of complex shape object has been proposed. An accurate object region has detected from combined binary image, which has achieved using DoG filter and local adaptive binarization. The object has recognized using neural network, which is trained with sub-divided object class according to object type and rotation angle. Predefined shape model of object and maximal slope have used to estimate the pose of object. The performance has evaluated on ETRI database and recognition rate of 96% has obtained.