• International Journal of Contents
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• v.8 no.2
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• pp.1-6
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• 2012

Recently, content-based music information retrieval (MIR) systems for mobile devices have attracted great interest. However, music retrieval systems are greatly affected by background noise when music is recorded in noisy environments. Therefore, we evaluated various pre-processing methods using the Philips method to determine the one that performs most robust music retrieval in such environments. We found that dynamic noise reduction (DNR) is the best pre-processing method for a music retrieval system in noisy environments.

• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.367-369
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• 2005

In this paper, we present a new method for the detection of spiculation on digital mammograms. Traditional methods have defects; sensitive to noise, fixed size processing, and long processing time, however, the proposed method has merits; not sensitive to noise, adaptive size processing, and fast processing time. Experimental results show that the spiculation detection performance of the proposed method is improved much compared to the other methods.

• Food Science of Animal Resources
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• v.43 no.3
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• pp.428-440
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• 2023

Global meat consumption is increasing worldwide, however, supply remains lacking. Several alternative protein sources, such as cultured meat, plant-based protein production, and edible insects, have been proposed to overcome this shortage. Interestingly, edible insects are characterized by superior digestive and absorptive qualities that make them the ideal replacement for traditional protein production. This study aims to further the processing ability of insect protein by investigating the effects of various pre-treatment methods, such as blanching (HB), roasting (HR), and superheated steam (HS), on the nutritional properties and physicochemical characteristics of proteins extracted from Hermetia illucens larvae. The drying rate, pH value, color analysis, amino and fatty acid profile, as well as bulk density, shear force, and rehydration ratios of the above pre-treatment methods, were explored. HS was found to have the highest drying rate and pH value analysis showed that HB and HS samples have significantly higher values compared to the other modalities. Raw edible insects had the highest value in the sum of essential amino acid (EAA) and EAA index when compared to EAAs. HB and HS showed significantly lower bulk density results, and HS showed the highest shear force and the highest value in rehydration ratio, regardless of immersion time. Therefore, taking the above results together, it was found that blanching and superheated steam blanching pre-treatment were the most effective methods to improve the processing properties of H. illucens after hot-air drying.

• IEMEK Journal of Embedded Systems and Applications
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• v.11 no.5
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• pp.277-285
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• 2016

Moving object detection system has been an emerging research field in various advanced driver assistance systems (ADAS) and surveillance system. In this paper, we propose two optical flow based moving object detection methods at dynamic scenes. Both proposed methods consist of three successive steps; pre-processing, foreground segmentation, and post-processing steps. Two proposed methods have the same pre-processing and post-processing steps, but different foreground segmentation step. Pre-processing calculates mainly optical flow map of which each pixel has the amplitude of motion vector. Dense optical flows are estimated by using Farneback technique, and the amplitude of the motion normalized into the range from 0 to 255 is assigned to each pixel of optical flow map. In the foreground segmentation step, moving object and background are classified by using the optical flow map. Here, we proposed two algorithms. One is Gaussian mixture model (GMM) based background subtraction, which is applied on optical map. Another is adaptive thresholding based foreground segmentation, which classifies each pixel into object and background by updating threshold value column by column. Through the simulations, we show that both optical flow based methods can achieve good enough object detection performances in dynamic scenes.

• Journal of the Korea Institute of Military Science and Technology
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• v.13 no.5
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• pp.875-885
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• 2010

Recently, the role of Ubiquitous Sensor Network(USN) has been considered to be essential for supporting the near future Network Centric Warfare(NCW) and Tactical Information Communication Network(TICN). In this paper, we explore a set of data storage methods(external storage, local storage and data storage) and query processing methods in WSN. In particular, we focus on analyzing a novel data structure for supporting the local storage method, named the partial ordered tree(POT). The main idea behind POT is that sensor readings are usually correlated with the physical spatial domain. With the help of POT, only a small portion of sensor nodes participate in query processing tasks, and thus network lifetime is greatly increased. Through a series of simulation experiments, we demonstrate that the POT based local storage method clearly outperforms the existing data storage methods in terms of the energy-efficiency, which directly affects the network lifetime, for processing exact match queries, range queries and top-k queries.

• International Journal of Contents
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• v.12 no.1
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• pp.60-64
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• 2016

As new applications of cloud data management system (CDMS) such as online games, cooperation edit, social network, and so on, are increasing, transaction processing capabilities for CDMS are required. Several transaction processing methods for cloud data management system (CDMS) have been proposed. However, existing transaction processing methods have some problems. Some of them provide limited transaction processing capabilities. Some of them are hard to be integrated with existing CDMSs. In this paper, we proposed a new concurrency control method to support transaction processing capability for CDMS to solve these problems. The proposed method was designed and implemented based on Spark, an in-memory distributed processing framework. It uses RDD (Resilient Distributed Dataset) model to provide fault tolerant to data in the main memory. In our proposed method, database stored in CDMS is loaded to main memory managed by Spark. The loaded data set is then transformed to RDD. In addition, we proposed a multi-version concurrency control method through immutable characteristics of RDD. Finally, we performed experiments to show the feasibility of the proposed method.

• Food Science of Animal Resources
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• v.38 no.5
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• pp.970-980
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• 2018

We evaluated the effects of pre-cooking methods on the quality of reheated marinated pork loin. Frozen marinated pork loins cooked using various methods (boiling, grilling, pan frying, infrared cooking, and superheated steam cooking) were reheated in a microwave, and their pH, color, cooking loss, re-heating loss, total loss, thiobarbituric acid reactive substance (TBARS) value, sensory properties, and shear force were determined. Although all parameters varied with different cooking methods, lightness values and TBARS values showed the tendency to decrease and increase, respectively, after reheating. Superheated steam-cooked samples showed the lowest values of cooking loss, total loss, TBARS value, and shear force (p<0.05) and the highest lightness, redness, and yellowssness values and juiciness, chewiness, and overall acceptability scores (p<0.05). These results show that pre-cooking with superheated steam maintains the quality characteristics of marinated pork loin upon reheating. Therefore, pre-cooking with superheated steam may be beneficial for the commercial distribution of frozen cooked marinated pork loin.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.150-150
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• 2018

The Numerical Weather Prediction (NWP) models provide information for weather forecasts. The highly nonlinear and complex interactions in the atmosphere are simplified in meteorological models through approximations and parameterization. Therefore, the simplifications may lead to biases and errors in model results. Although the models have improved over time, the biased outputs of these models are still a matter of concern in meteorological and hydrological studies. Thus, bias removal is an essential step prior to using outputs of atmospheric models. The main idea of statistical bias correction methods is to develop a statistical relationship between modeled and observed variables over the same historical period. The Model Output Statistics (MOS) would be desirable to better match the real time forecast data with observation records. Statistical post-processing methods relate model outputs to the observed values at the sites of interest. In this study three methods are used to remove the possible biases of the real-time outputs of the Weather Research and Forecast (WRF) model in Imjin basin (North and South Korea). The post-processing techniques include the Linear Regression (LR), Linear Scaling (LS) and Power Scaling (PS) methods. The MOS techniques used in this study include three main steps: preprocessing of the historical data in training set, development of the equations, and application of the equations for the validation set. The expected results show the accuracy improvement of the real-time forecast data before and after bias correction. The comparison of the different methods will clarify the best method for the purpose of the forecast skill enhancement in a real-time case study.

• Food Science of Animal Resources
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• v.34 no.5
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• pp.597-603
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• 2014

In this study, the physicochemical and sensory quality characteristics due to the influence of various thawing methods on electro-magnetic and air blast frozen pork were examined. The packaged pork samples, which were frozen by air blast freezing at $-45^{\circ}C$ or electro-magnetic freezing at $-55^{\circ}C$, were thawed using 4 different methods: refrigeration ($4{\pm}1^{\circ}C$), room temperature (RT, $25^{\circ}C$), cold water ($15^{\circ}C$), and microwave (2450 MHz). Analyses were carried out to determine the drip and cooking loss, water holding capacity (WHC), moisture content and sensory evaluation. Frozen pork thawed in a microwave indicated relatively less thawing loss (0.63-1.24%) than the other thawing methods (0.68-1.38%). The cooking loss after electro-magnetic freezing indicated 37.4% by microwave thawing, compared with 32.9% by refrigeration, 36.5% by RT, and 37.2% by cold water in ham. The thawing of samples frozen by electro-magnetic freezing showed no significant differences between the methods used, while the moisture content was higher in belly thawed by microwave (62.0%) after electro-magnetic freezing than refrigeration (54.8%), RT (61.3%), and cold water (61.1%). The highest overall acceptability was shown for microwave thawing after electro-magnetic freezing but there were no significant differences compared to that of the other samples.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.12
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• pp.4420-4438
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• 2021

Image dehazing is an ill-posed problem which is far from being solved. Traditional image dehazing methods often yield mediocre effects and possess substandard processing speed, while modern deep learning methods perform best only in certain datasets. The haze removal effect when processed by said methods is unsatisfactory, meaning the generalization performance fails to meet the requirements. Concurrently, due to the limited processing speed, most dehazing algorithms cannot be employed in the industry. To alleviate said problems, a lightweight fast dehazing network based on a multiple scale-patch framework (MSP) is proposed in the present paper. Firstly, the multi-scale structure is employed as the backbone network and the multi-patch structure as the supplementary network. Dehazing through a single network causes problems, such as loss of object details and color in some image areas, the multi-patch structure was employed for MSP as an information supplement. In the algorithm image processing module, the image is segmented up and down for processed separately. Secondly, MSP generates a clear dehazing effect and significant robustness when targeting real-world homogeneous and nonhomogeneous hazy maps and different datasets. Compared with existing dehazing methods, MSP demonstrated a fast inference speed and the feasibility of real-time processing. The overall size and model parameters of the entire dehazing model are 20.75M and 6.8M, and the processing time for the single image is 0.026s. Experiments on NTIRE 2018 and NTIRE 2020 demonstrate that MSP can achieve superior performance among the state-of-the-art methods, such as PSNR, SSIM, LPIPS, and individual subjective evaluation.