• Journal of the Institute of Convergence Signal Processing
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• v.18 no.2
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• pp.55-61
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• 2017

In radio astronomy, high-capacity HDDs are being used to save huge amounts of HDDs in order to record the observational data. For VLBI observations, observational speeds increase and huge amounts of observational data must be stored as they expand to broadband. As the HDD is frequently used, the number of failures occurred, and then it takes a lot of time to recover it. In addition, if a failed HDD is continuously used, observational data loss occurs. And it costs a lot of money to buy a new HDD. In this study, we developed the integrity verification system of the Serial ATA HDD using FirmOS. The FirmOS is an OS that has been developed to function exclusively for specific purposes on a system having a general server board and CPU. The developed system performs the process of writing and reading specific patterns of data in a physical area of the SATA HDD based on a FirmOS. In addition, we introduced a method to investigate the integrity of HDD integrity by comparing it with the stored pattern data from the HDD controller. Using the developed system, it was easy to determine whether the disk pack used in VLBI observations has error or not, and it is very useful to improve the observation efficiency. This paper introduces the detail for the design, configuration, testing, etc. of the SATA HDD integrity verification system developed.

• Journal of Biosystems Engineering
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• v.29 no.4
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• pp.341-346
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• 2004

Recent livestock people concern not only increase of production, but also superior quality of animal-breeding environment. So far, the optimization of the breeding and air environment has been focused on the production increase. In the very near future, the optimization will be emphasized on the environment for the animal welfare and health. Especially, cattle farming demands the precision livestock farming and special attention has to be given to the management of feeding, animal health and fertility. The management of individual animal is the first step for precision livestock farming and animal welfare, and recognizing each individual is important for that. Though electronic identification of a cattle such as RFID(Radio Frequency Identification) has many advantages, RFID implementations practically involve several problems such as the reading speed and distance. In that sense, computer vision might be more effective than RFID for the identification of an individual animal. The researches on the identification of cattle via image processing were mostly performed with the cows having black-white patterns of the Holstein. But, the native Korean and Japanese cattle do not have any definite pattern on the body. The purpose of this research is to identify the Japanese black cattle that does not have a body pattern using computer vision technology and neural network algorithm. Twelve heads of Japanese black cattle have been tested to verify the proposed scheme. The values of input parameters were specified and then computed using the face images of cattle. The images of cattle faces were trained using associate neural network algorithm, and the algorithm was verified by the face images that were transformed using brightness, distortion, and noise factors. As a result, there was difference due to transform ratio of the brightness, distortion, and noise. And, the proposed algorithm could identify 100% in the range from -3 to +3 degrees of the brightness, from -2 to +4 degrees of the distortion, and from 0% to 60% of the noise transformed images. It is concluded that our system can not be applied in real time recognition of the moving cows, but can be used for the cattle being at a standstill.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.40 no.1
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• pp.29-36
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• 2004

This study deals with an analysis on the sinking resistance for the model purse seine, in the case of different netting material and sinkers. The experiment was carried out using rune simplified model seines of knotless nettings. Dimension of model seines 420cm for corkline and 85cm for seine depth, three groups of models rigged 25, 45 and 60g with the same weighted sinkers in water were used. These were named PP-25, PA-25, PES-25, PP-45, PA-45, PES-45, PP-60, PA-60 and PES-60 seine. The densitie($\rho$) of netting materials were 0.91g/cm$cm^3$, 1.14g/cm$cm^3$ and 1.38g/cm$m^3$. Experiments carried out in the observation channel in a flume tank under still water conditions. Sinking motion was recorded by the one set of TV-camera for VTR, and reading coordinate carried out by the video digitization system. Differential equations were derived from the conservation of momenta of the model purse seines and used to determine the sinking speeds of the depths of leadline and the other portions of the seines. An analysis carried out by simultaneous differential equations for numerical method by sub-routine Runge-Kutta-Gill The results obtained were as follows : 1. Average sinking speed of leadline for the model seines rigged 60g with the same weighted sinkers in water was fastest for 12.2cm/sec of PES seine, followed by 11.4cm/sec of PA and 10.7cm/sec of PP seines. 2. The coefficient of resistance for netting of seine was estimated to be $K_D=0.09(\frac{\rho}{\rho_w})^4$ 3. The coefficient of resistance for netting bundle of seine was estimated to be $C_R=0.91(\frac{\rho}{\rho_w})$ 4. In all seines, the calculated depths of leadline closely agreed with the measured ones, each 25g, 45g, 60g of weighted sinkers were put into formulas meas.=1.04cal., meas.=0.99cal. and meas.=0.98 cal.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.57-78
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• 2020

With the development of the Internet, consumers have had an opportunity to check product information easily through E-Commerce. Product reviews used in the process of purchasing goods are based on user experience, allowing consumers to engage as producers of information as well as refer to information. This can be a way to increase the efficiency of purchasing decisions from the perspective of consumers, and from the seller's point of view, it can help develop products and strengthen their competitiveness. However, it takes a lot of time and effort to understand the overall assessment and assessment dimensions of the products that I think are important in reading the vast amount of product reviews offered by E-Commerce for the products consumers want to compare. This is because product reviews are unstructured information and it is difficult to read sentiment of reviews and assessment dimension immediately. For example, consumers who want to purchase a laptop would like to check the assessment of comparative products at each dimension, such as performance, weight, delivery, speed, and design. Therefore, in this paper, we would like to propose a method to automatically generate multi-dimensional product assessment scores in product reviews that we would like to compare. The methods presented in this study consist largely of two phases. One is the pre-preparation phase and the second is the individual product scoring phase. In the pre-preparation phase, a dimensioned classification model and a sentiment analysis model are created based on a review of the large category product group review. By combining word embedding and association analysis, the dimensioned classification model complements the limitation that word embedding methods for finding relevance between dimensions and words in existing studies see only the distance of words in sentences. Sentiment analysis models generate CNN models by organizing learning data tagged with positives and negatives on a phrase unit for accurate polarity detection. Through this, the individual product scoring phase applies the models pre-prepared for the phrase unit review. Multi-dimensional assessment scores can be obtained by aggregating them by assessment dimension according to the proportion of reviews organized like this, which are grouped among those that are judged to describe a specific dimension for each phrase. In the experiment of this paper, approximately 260,000 reviews of the large category product group are collected to form a dimensioned classification model and a sentiment analysis model. In addition, reviews of the laptops of S and L companies selling at E-Commerce are collected and used as experimental data, respectively. The dimensioned classification model classified individual product reviews broken down into phrases into six assessment dimensions and combined the existing word embedding method with an association analysis indicating frequency between words and dimensions. As a result of combining word embedding and association analysis, the accuracy of the model increased by 13.7%. The sentiment analysis models could be seen to closely analyze the assessment when they were taught in a phrase unit rather than in sentences. As a result, it was confirmed that the accuracy was 29.4% higher than the sentence-based model. Through this study, both sellers and consumers can expect efficient decision making in purchasing and product development, given that they can make multi-dimensional comparisons of products. In addition, text reviews, which are unstructured data, were transformed into objective values such as frequency and morpheme, and they were analysed together using word embedding and association analysis to improve the objectivity aspects of more precise multi-dimensional analysis and research. This will be an attractive analysis model in terms of not only enabling more effective service deployment during the evolving E-Commerce market and fierce competition, but also satisfying both customers.