• Analytical Science and Technology
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• v.30 no.4
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• pp.167-173
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• 2017

In the era of the forth Industrial Revolution, open source code and open source hardware have gained much attention. In particular, 3D printing technology is expanding into the realms of classical science, technology and our daily lives. Relatedly, in the present study, we demonstrate the manufacture of a nano-LC MALDI spotter robot using 3D printing technology. The parts of the spotter robot were either made using a 3D printer or purchased as 3D printer parts from the 3D printer online market, so that anyone can make the robot without a deep knowledge of engineering or electronics, i.e., DIY (do-it-yourself) product. In the nano-LC MALDI spotter, the nano-LC eluent and MALDI matrix were mixed in a T-union and discharged from the capillary outlet. The eluent and matrix mixture could be spotted onto the movable MALDI plate. The MALDI plate was designed to translate in a two-dimensional space (xy plane), which was enabled by the movements of two stepper motors. In the paper, all computer-aided design (CAD) files for the parts and operation software are provided to help the reader manufacture their own spotter robot.

• Journal of the Korean Institute of Intelligent Systems
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• v.26 no.6
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• pp.471-476
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• 2016

In this paper, recognition system for continuous human action is explained by using motion history image and histogram of oriented gradient with spotter model based on depth information, and the spotter model which performs action spotting is proposed to improve recognition performance in the recognition system. The steps of this system are composed of pre-processing, human action and spotter modeling and continuous human action recognition. In pre-processing process, Depth-MHI-HOG is used to extract space-time template-based features after image segmentation, and human action and spotter modeling generates sequence by using the extracted feature. Human action models which are appropriate for each of defined action and a proposed spotter model are created by using these generated sequences and the hidden markov model. Continuous human action recognition performs action spotting to segment meaningful action and meaningless action by the spotter model in continuous action sequence, and continuously recognizes human action comparing probability values of model for meaningful action sequence. Experimental results demonstrate that the proposed model efficiently improves recognition performance in continuous action recognition system.

• Journal of Information Science Theory and Practice
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• v.2 no.1
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• pp.6-21
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• 2014

This paper proposes a novel knowledge extraction system, TAKES (Two-step Approach for Knowledge Extraction System), which integrates advanced techniques from Information Retrieval (IR), Information Extraction (IE), and Natural Language Processing (NLP). In particular, TAKES adopts a novel keyphrase extraction-based query expansion technique to collect promising documents. It also uses a Conditional Random Field-based machine learning technique to extract important biological entities and relations. TAKES is applied to biological knowledge extraction, particularly retrieving promising documents that contain Protein-Protein Interaction (PPI) and extracting PPI pairs. TAKES consists of two major components: DocSpotter, which is used to query and retrieve promising documents for extraction, and a Conditional Random Field (CRF)-based entity extraction component known as FCRF. The present paper investigated research problems addressing the issues with a knowledge extraction system and conducted a series of experiments to test our hypotheses. The findings from the experiments are as follows: First, the author verified, using three different test collections to measure the performance of our query expansion technique, that DocSpotter is robust and highly accurate when compared to Okapi BM25 and SLIPPER. Second, the author verified that our relation extraction algorithm, FCRF, is highly accurate in terms of F-Measure compared to four other competitive extraction algorithms: Support Vector Machine, Maximum Entropy, Single POS HMM, and Rapier.

• Journal of Ecology and Environment
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• v.43 no.4
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• pp.385-389
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• 2019

Knowledge of abundance, or population size, is fundamental in wildlife conservation and management. Camera-trapping, in combination with capture-recapture methods, has been extensively applied to estimate abundance and density of individually identifiable animals due to the advantages of being non-invasive, effective to survey wide-ranging, elusive, or nocturnal species, operating in inhospitable environment, and taking low labor. We assessed the possibility of using coat patterns from images to identify an individual leopard cat (Prionailurus bengalensis), a Class II endangered species in South Korea. We analyzed leopard cat images taken from Digital Single-Lense Relfex camera (high resolution, 18Mpxl) and camera traps (low resolution, 3.1Mpxl) using HotSpotter, an image matching algorithm. HotSpotter accurately top-ranked an image of the same individual leopard cat with the reference leopard cat image 100% by matching facial and ventral parts. This confirms that facial and ventral fur patterns of the Amur leopard cat are good matching points to be used reliably to identify an individual. We anticipate that the study results will be useful to researchers interested in studying behavior or population parameter estimates of Amur leopard cats based on capture-recapture models.

• MALSORI
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• no.62
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• pp.133-147
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• 2007

In this paper, we introduce a phone vector discrete HMM(PVDHMM) that decodes a phone sequence string, and demonstrates the applicability to spoken document retrieval. The PVDHMM treats a phone recognizer or large vocabulary continuous speech recognizer (LVCSR) as a vector quantizer whose codebook size is equal to the size of its phone set. We apply the PVDHMM to decode the phone sequence strings and compare the outputs with those of a continuous speech recognizer(CSR). Also we carry out spoken document retrieval experiment through PVDHMM word spotter on the phone sequence strings which are generated by phone recognizer or LVCSR and compare its results with those of retrieval through the phone-based vector space model.

• Proceedings of the IEEK Conference
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• 2002.06b
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• pp.245-248
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• 2002

In this paper, the influence of Si surface damage on the NiSi formation has been characterized. The silicon surface is damaged using ion beam type spotter. Then, the effect of H2 anneal and TiN capping layer on the damaged has also been analyzed. The sheet resistance of NiSi formed on damaged Si increased rapidly as the damaging time increases while thermal stability of damaged NiSi was stabler than the undamaged one. In the case when H\ulcorner anneal and TiN capping layer were applied together, the characteristics of NiSi shows a little improvement of the sheet resistance.

• Journal of Korea Multimedia Society
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• v.11 no.6
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• pp.807-815
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• 2008

A simple efficient method of spotting and recognizing hand gestures in video is presented using a network of hidden Markov models and dynamic programming search algorithm. The description starts from designing a set of isolated trajectory models which are stochastic and robust enough to characterize highly variable patterns like human motion, handwriting, and speech. Those models are interconnected to form a single big network termed a spotting network or a spotter that models a continuous stream of gestures and non-gestures as well. The inference over the model is based on dynamic programming. The proposed model is highly efficient and can readily be extended to a variety of recurrent pattern recognition tasks. The test result without any engineering has shown the potential for practical application. At the end of the paper we add some related experimental result that has been obtained using a different model - dynamic Bayesian network - which is also a type of stochastic model.

• Progress in Medical Physics
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• v.18 no.1
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• pp.35-41
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• 2007

In this work practical considerations of a pulsed arterial spin labeling MRI are presented to reliable multi-slice perfusion measurements In the human brain. Three parameters were considered in this study. First, In order to improve slice profile and Inversion efficiency of a labeling pulse a high power Inversion pulse of adiabatic hyperbolic secant was designed. A $900^{\circ}$ rotation of the flip angle was provided to make a good slice profile and excellent Inversion efficiency. Second, to minimize contributions of a residual magnetization be4ween Interleaved scans of control and labeling we tested three different conditions which were applied 1) only saturation pulses, 2) only spotter gradients, and 3) combinations of saturation pulses and spotter gradients Applications of bo4h saturation pulses and spoiler gradients minimized the residual magnetization. Finally, to find a minimum gap between a tagged plane and an imaging plane we tested signal changes of the subtracted image between control and labeled Images with varying the gap. The optimum gap was about 20mm. In conclusion, In order to obtain high quality of perfusion Images In human brain It Is Important to use optimum parameters. Before routinely using In clinical studios, we recommend to make optimizations of sequence parameters.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.41 no.4
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• pp.441-447
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• 2021

Construction sites are complex and dangerous because heavy equipment and workers perform various operations simultaneously within limited working areas. Solitary works of heavy equipment in complex job sites can cause fatal accidents, and thus they should interact with spotters and obtain information about surrounding environments during operations. Recently, many computer vision technologies have been developed to automatically monitor construction equipment and detect their interactions with other resources. However, previous methods did not take into account the interactions between equipment and spotters, which is crucial for identifying solitary works of heavy equipment. To address the drawback, this research develops a computer vision-based solitary work detection model that considers interactive operations between heavy equipment and spotters. To validate the proposed model, the research team performed experiments using image data collected from actual construction sites. The results showed that the model was able to detect workers and equipment with 83.4 % accuracy, classify workers and spotters with 84.2 % accuracy, and analyze the equipment-to-spotter interactions with 95.1 % accuracy. The findings of this study can be used to automate manual operation monitoring of heavy equipment and reduce the time and costs required for on-site safety management.

• Proceedings of the KIEE Conference
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• 1994.11a
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• pp.228-230
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• 1994

A Spotter ion Plating(SIP) system with a r. f. coil electrode and the Facing Target Sputter(FTS) source was designed for high-quality thin film formation. The rf discharge was combined with DC facing target sputtering in order to enhance ionization degree of a sputtered atoms. The discharge voltage-discharge characteristics curves of a FTS source could be characterized by the fern of $I{\propto}V^n$ with n in the range of $8{\sim}12$. The energy of ions incident on the substrate depended on the sheath potential of DC biased substrate. The mean impact ion energy increased with negative bias voltage and rf power. The adhesive force of the TiN film formed was in the range of $30{\sim}50N$, and markedly influenced by substrate bias voltage.