• Journal of the Korean Society for Precision Engineering
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• v.24 no.10
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• pp.64-74
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• 2007

Because of the size and environment in the shipbuilding process, the portable type robot is required for the automatic seam tracking. For this reason, the structure of laser sensor should be considered in the initial design step and the coordinate transformation between welding robot and laser sensor, which is joint finder, must be identified exactly and the real time tracking algorithm based on these consideration could be developed. In this research, laser displacement sensor in which its structure is laser beam's vertical projection, is developed to recognize the location of weld joint. In practical applications, however, images of weld joints are often degraded because of the surface specularity or spatter. To overcome the problem, the constrained joint finding algorithm is proposed. In the approach of coordinate conversion rule for the visual feedback control among welding torch, robot body and laser sensor is applied by the same reference point method. In the real time seam tracking algorithms we propose constrained sampling method which uses look ahead distance. The RLS(Recursive Least Square) filter is applied to obtain the smooth tracking path from the sensitive edge data. From the experimental results, we could see the possibility that the developed laser sensor with proposed processing algorithm and real time seam tracking method can be used as a welding under the shipbuilding condition.

• Journal of the Korean Institute of Telematics and Electronics
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• v.19 no.3
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• pp.5-12
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• 1982

Through the use of energy ratio of special bandwidths of basic vowels, recognition of Korean basic spoken digit is performed in logical combination with a zero-crossing rate and an energy parameter. In the experiments for recognition of the digits, the speech signal of spoken digits is filtered by a lowpass filter of which the cutoff frequency is 10KHz, and then sampled at 20KHz of sampling rate, In the speech signal processing, we used four FIR digital filters, and the order of filter lengths is 61, 120, 25, 25respectively. The filters are designed by using Remetz exchange algorithm.[13],[14] As a result, the recognition rate of 92% for the three speakers is obstained.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.11
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• pp.1562-1569
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• 2018

Sensor readings always have a certain degree of randomness and fuzziness due to its intrinsic property, other electronic devices in the circuitry, wires and the rapidly changing environment. In an electrical control system, such readings will bring instability in the system and other undesired events especially if the signal hovers around the threshold. This paper proposes a Gaussian-based statistical approach in stabilizing the output through sampling the sensor data and automatic tuning the threshold to the range of multiple standard deviations. It takes advantage of the Central limit theorem and its properties assuming that a large number of sensor data samples will eventually converge to a Gaussian distribution. Experimental results demonstrate the effectiveness of the proposed algorithm in completely stabilizing the outputs over known filtering algorithms like Exponential smoothing and Kalman Filter.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.43 no.6
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• pp.735-739
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• 2010

This study investigated a method for monitoring fishes immigrating to upper streams from the sea in relation to water level with elapsed time, and measured fish behavior patterns and swimming speed in a fishing boat gateway using an acoustic camera system. This method was employed due to difficulties, linked to high turbidity, of using only underwater optical systems for monitoring fish migrating to brackish water. Results showed that fish length distribution showed high correlation between haul sampling and an automatic counting algorithm supported by the DIDSON software program. These results will help to maximize the effects of fish run management by increasing understanding of the amount of major fish species migrating in relation to durable water levels.

• Korean Journal of Remote Sensing
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• v.34 no.1
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• pp.45-74
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• 2018

Landslide is a natural hazard that threats lives and properties in many areas around the world. Landslides are difficult to recognize, particularly in rainforest regions. Thus, an accurate, detailed, and updated inventory map is required for landslide susceptibility, hazard, and risk analyses. The inconsistency in the results obtained using different features selection techniques in the literature has highlighted the importance of evaluating these techniques. Thus, in this study, six techniques of features selection were evaluated. Very-high-resolution LiDAR point clouds and orthophotos were acquired simultaneously in a rainforest area of Cameron Highlands, Malaysia by airborne laser scanning (LiDAR). A fuzzy-based segmentation parameter (FbSP optimizer) was used to optimize the segmentation parameters. Training samples were evaluated using a stratified random sampling method and set to 70% training samples. Two machine-learning algorithms, namely, Support Vector Machine (SVM) and Random Forest (RF), were used to evaluate the performance of each features selection algorithm. The overall accuracies of the SVM and RF models revealed that three of the six algorithms exhibited higher ranks in landslide detection. Results indicated that the classification accuracies of the RF classifier were higher than the SVM classifier using either all features or only the optimal features. The proposed techniques performed well in detecting the landslides in a rainforest area of Malaysia, and these techniques can be easily extended to similar regions.

• The KIPS Transactions:PartB
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• v.17B no.1
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• pp.47-54
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• 2010

Old archived film shows two major defects: line scratch and blobs. In this paper, we present a design and implementation of an automatic video restoration system for line scratches observed in archived film. We use autoregressive (AR) image model because we can make stochastic and specifically autoregressive image generation process with our PAST-PRESENT model and Sampling Pattern. We designed locality maximizing scanning pattern, which can generate nearly stationary time-like series of pixels, which is a strong requirement for a stochastic series to be autoregressive. The sampled pixel series undergoes filtering and model fitting using Durbin-Levinson algorithm before interpolation process. We designed three-stage film restoration system, which includes (1) film acquisition from VHS tapes, (2) simple line scratch detection and restoration, and (3) manual blob identification and sophisticated inpainting scheme. We implemented film acquisition and simple inpainting scheme on Texas Instruments DSP board TMS320DM642 EVM, and implemented our AR inpainting scheme on PC for sophisticated restoration. We experimented our scheme with two old Korean films: "Viva Freedom" and "Robot Tae-Kwon-V", and the experimental results show that our scheme improves Bertalmio's scheme for subjective quality (MOS), objective quality (PSNR), and especially restoration ratio (RR), which reflects how much similar to the manual inpainting results.

• Korean Journal of Remote Sensing
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• v.34 no.6_2
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• pp.1203-1213
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• 2018

In Arctic and Antarctic ocean, remote sensing is the most effective observation for environmental changes due to the inaccessibility of the regions. Even though satellite, UAV (Unmanned Aerial Vehical) are well known remote sensing platforms, and research vessel also used for automatic measurement on the regions, varied environment of Polar regions require time series and wide coverage of data. Especially, in high latitude, apply an optical satellite remote sensing is not easy due to low sun altitude. In this paper, we introduce an operation of hyper-spectrometer (HyperSAS/Satlantic inc.) which is mounted on Ice Breaker Research Vessel ARAON of Korea Polar Research Institute since 2010, to acquire an above water reflectance atomatically through every research cruise on Arctic and Antarctic ocean and transit both regions. In addition to, auxiliary data for the remotely acquired data, in situ water sampling were also obtained. The above water reflectance and in situ water sampling data are continuously acquired since 2010 will contribute to improve an Ocean Color algorithm in the high latitude and help to understand ocean reflectances over from high latitude through low latitude. Preliminary result from above water reflectance showed characteristics of Arctic ocean and Antarctic Ocean and used to develop algorithms for estimating various ocean factors such as chlorophyll and suspended sediment.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.1-21
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• 2020

With the rapid development of artificial intelligence technology, various techniques have been developed to extract meaningful information from unstructured text data which constitutes a large portion of big data. Over the past decades, text mining technologies have been utilized in various industries for practical applications. In the field of business intelligence, it has been employed to discover new market and/or technology opportunities and support rational decision making of business participants. The market information such as market size, market growth rate, and market share is essential for setting companies' business strategies. There has been a continuous demand in various fields for specific product level-market information. However, the information has been generally provided at industry level or broad categories based on classification standards, making it difficult to obtain specific and proper information. In this regard, we propose a new methodology that can estimate the market sizes of product groups at more detailed levels than that of previously offered. We applied Word2Vec algorithm, a neural network based semantic word embedding model, to enable automatic market size estimation from individual companies' product information in a bottom-up manner. The overall process is as follows: First, the data related to product information is collected, refined, and restructured into suitable form for applying Word2Vec model. Next, the preprocessed data is embedded into vector space by Word2Vec and then the product groups are derived by extracting similar products names based on cosine similarity calculation. Finally, the sales data on the extracted products is summated to estimate the market size of the product groups. As an experimental data, text data of product names from Statistics Korea's microdata (345,103 cases) were mapped in multidimensional vector space by Word2Vec training. We performed parameters optimization for training and then applied vector dimension of 300 and window size of 15 as optimized parameters for further experiments. We employed index words of Korean Standard Industry Classification (KSIC) as a product name dataset to more efficiently cluster product groups. The product names which are similar to KSIC indexes were extracted based on cosine similarity. The market size of extracted products as one product category was calculated from individual companies' sales data. The market sizes of 11,654 specific product lines were automatically estimated by the proposed model. For the performance verification, the results were compared with actual market size of some items. The Pearson's correlation coefficient was 0.513. Our approach has several advantages differing from the previous studies. First, text mining and machine learning techniques were applied for the first time on market size estimation, overcoming the limitations of traditional sampling based- or multiple assumption required-methods. In addition, the level of market category can be easily and efficiently adjusted according to the purpose of information use by changing cosine similarity threshold. Furthermore, it has a high potential of practical applications since it can resolve unmet needs for detailed market size information in public and private sectors. Specifically, it can be utilized in technology evaluation and technology commercialization support program conducted by governmental institutions, as well as business strategies consulting and market analysis report publishing by private firms. The limitation of our study is that the presented model needs to be improved in terms of accuracy and reliability. The semantic-based word embedding module can be advanced by giving a proper order in the preprocessed dataset or by combining another algorithm such as Jaccard similarity with Word2Vec. Also, the methods of product group clustering can be changed to other types of unsupervised machine learning algorithm. Our group is currently working on subsequent studies and we expect that it can further improve the performance of the conceptually proposed basic model in this study.