• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.5
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• pp.1421-1444
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• 2012

Mobile devices such as Android devices are emerging as a convenient client computing device with mobility and context-sensing capability. However, the computing power and hardware resource of the devices are limited due to their small form-factor. Consequently, large-scaled applications could not be deployed on these devices. Nonetheless, if the large-scaled applications are deployed and executed on the devices, high performance of the applications cannot be guaranteed. To remedy the limitation in terms of performance, it is inevitable to let some heavy-weight functionality executed on the server side and let a client application invoke the functionality in the server. To realize this kind of mobile applications, we adopt well-defined architecture design principles; being thin-client, being layered with Model-View-Controller (MVC), and being balanced between client side and server side. By adopting the principles, we propose a unique, ideal and practical architecture for mobile applications, called balanced MVC architecture. By considering the principles, key design considerations of realizing balanced MVC architecture lie in functionality partitioning. Hence, we define key criteria of determining the degree of performance. And, we define a method to design a balanced MVC architecture which embodies functionality partitioning for high performance, and a simulation-based evaluation method of balanced MVC architectures.

• Journal of the Korean Institute of Telematics and Electronics
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• v.24 no.2
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• pp.338-346
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• 1987

This paper describes a method for verifying whether a given geometrical layout correcdtly reflects the original logic level description. The logic description extracted from layout data was directly compadred with the original logic diagram generated at logic level design stage where the logic diagram is represented as a weighted multi-place graph. The comparison is based on graph isomorphism and error messages(error categories and locations)are invoked if any difference is found between the two logic descriptions. An efficient partitioning algorithm which consists of two steps, candidate selection and equal weight partitioning procedure, enables the entire verification process to occur in O(n log n) time.

• Korean Chemical Engineering Research
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• v.45 no.2
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• pp.133-142
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• 2007

As the environmental pollution caused by excessive uses of chemical fertilizers and pesticides is aggravated, organic farming using pasture and livestock manure is gaining an increased necessity. The application rate of the organic farming materials to the field is determined as a function of crops and soil types, weather and cultivation surroundings. When livestock manure is used for organic farming materials, the volatilization of ammonia from field-spread animal manure is a major source of atmospheric pollution and leads to a significant reduction in the fertilizer value of the manure. Therefore, an ammonia emission model should be presented to reduce the ammonia emission and to know appropriate application rate of manure. In this study, the ammonia emission rate from field-applied pig manure is predicted using an artificial neural network (ANN) method, where the Michaelis-Menten equation is employed for the ammonia emission rate model. Two model parameters (total loss of ammonia emission rate and time to reach the half of the total emission rate) of the model are predicted using a feedforward-backpropagation ANN on the basis of the ALFAM (Ammonia Loss from Field-applied Animal Manure) database in Europe. The relative importance among 15 input variables influencing ammonia loss is identified using the weight partitioning method. As a result, the ammonia emission is influenced mush by the weather and the manure state.

• Asian-Australasian Journal of Animal Sciences
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• v.29 no.12
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• pp.1756-1760
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• 2016

Estimation of feed intake (FI) for individual animals within a pen is needed in situations where more than one animal share a feeder during feeding trials. A partitioning method (PM) was previously published as a model to estimate the individual FI (IFI). Briefly, the IFI of a pig within the pen was calculated by partitioning IFI into IFI for maintenance ($IFI_m$) and IFI for growth. In the PM, $IFI_m$ is determined based on the metabolic body weight (BW), which is calculated using the coefficient of 106 and exponent of 0.75. Two simulation studies were conducted to test the hypothesis that the use of different coefficients and exponents for metabolic BW to calculate $IFI_m$ improves the accuracy of the estimates of IFI for pigs, and that PM is applied to pigs fed in group-housing systems. The accuracy of prediction represented by difference between actual and estimated IFI was compared using PM, ratio (RM), or averaging method (AM). In simulation studies 1 and 2, the PM estimated IFI better than the AM and RM during most of the periods (p<0.05). The use of 0.60 as the exponent and the coefficient of 197 to calculate metabolic BW did not improve the accuracy of the IFI estimates in both simulation studies 1 and 2. The results imply that the use of $197kcal{\times}kg\;BW^{0.60}$ as metabolizable energy for maintenance in PM does not improve the accuracy of IFI estimations compared with the use of $106kcal{\times}kg\;BW^{0.75}$ and that the PM estimates the IFI of pigs with greater accuracy compared with the averaging or ratio methods in group-housing systems.

• Journal of the Korea Society of Computer and Information
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• v.27 no.4
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• pp.27-36
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• 2022

In this paper, to improve the conditions of the fish processing site, we propose a method to predict the cutting point of fish according to the target weight using AI machine vision. The proposed method performs image-based preprocessing by first photographing the top and front views of the input fish. Then, RANSAC(RANdom SAmple Consensus) is used to extract the fish contour line, and then 3D external information of the fish is obtained using 3D modeling. Next, machine learning is performed on the extracted three-dimensional feature information and measured weight information to generate a neural network model. Subsequently, the fish is cut at the cutting point predicted by the proposed technique, and then the weight of the cut piece is measured. We compared the measured weight with the target weight and evaluated the performance using evaluation methods such as MAE(Mean Absolute Error) and MRE(Mean Relative Error). The obtained results indicate that an average error rate of less than 3% was achieved in comparison to the target weight. The proposed technique is expected to contribute greatly to the development of the fishery industry in the future by being linked to the automation system.

• Journal of KIISE:Software and Applications
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• v.30 no.3_4
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• pp.316-325
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• 2003

A user preference prediction method using an exiting collaborative filtering technique has used the nearest-neighborhood method based on the user preference about items and has sought the user's similarity from the Pearson correlation coefficient. Therefore, it does not reflect any contents about items and also solve the problem of the sparsity. This study suggests the preference prediction system using the similarity weight granted Bayesian estimated value and the associative user clustering to complement problems of an exiting collaborative preference prediction method. This method suggested in this paper groups the user according to the Genre by using Association Rule Hypergraph Partitioning Algorithm and the new user is classified into one of these Genres by Naive Bayes classifier to slove the problem of sparsity in the collaborative filtering system. Besides, for get the similarity between users belonged to the classified genre and new users, this study allows the different estimated value to item which user vote through Naive Bayes learning. If the preference with estimated value is applied to the exiting Pearson correlation coefficient, it is able to promote the precision of the prediction by reducing the error of the prediction because of missing value. To estimate the performance of suggested method, the suggested method is compared with existing collaborative filtering techniques. As a result, the proposed method is efficient for improving the accuracy of prediction through solving problems of existing collaborative filtering techniques.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2005.10a
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• pp.57-78
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• 2005

The vehicle routing problem (VRP) is to determine a set of feasible vehicle routes, one for each vehicle, such that each customer is visited exactly once and the total distance travelled by the vehicles is minimized. A feasible route is defined as a simple circuit including the depot such that the total demand of the customers in the route does not exceed the vehicle capacity. While there have been significant advances recently in exact solution methodology, the VRP is not a well solved problem. We find most approaches still relying on the branch and bound method. These approaches employ various methodologies to compute a lower bound on the optimal value. We introduce a new modelling approach, termed route-splitting, for the VRP that allows us to address problems whose size is beyond the current computational range of set-partitioning models. The route-splitting model splits each vehicle route into segments, and results in more tractable subproblems. Lifting much of the burden of solving combinatorially hard subproblems, the route-splitting approach puts more weight on the LP master problem, Recent breakthroughs in solving LP problems (Nemhauser, 1994) bode well for our approach. Lower bounds are computed on five symmetric VRPs with up to 199 customers, and eight asymmetric VRPs with up to 70 customers. while it is said that the exact methods developed for asymmetric instances have in general a poor performance when applied to symmetric ones (Toth and Vigo, 2002), the route splitting approach shows a competent performance of 93.5% on average in the symmetric VRPs. For the asymmetric ones, the approach comes up with lower bounds of 97.6% on average. The route-splitting model can deal with asymmetric cost matrices and non-identical vehicles. Given the ability of the route-splitting model to address a wider range of applications and its good performance on asymmetric instances, we find the model promising and valuable for further research.

• The Transactions of the Korea Information Processing Society
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• v.6 no.2
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• pp.282-291
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• 1999

In order to construct the News Video Database System, cut detection technique is very important. In general, the color histogram, $\chi$2 histogram or Bin-to-Bin difference(B2B) techniques are mainly using for the scene partitioning. In this paper, we propose the efficient algorithm that is applied the weight in terms of NTSC standard to cut detection. This algorithm is able to reduce the time of acquiring and comparing histogram using by separate calculation of R, G, and B for the color histogram technique. And it also provide the efficient selection method fo threshold value by and use the news videos of KBS, MBC, SBS, CNN and NHK as experimental domains. By the result of experiment, we present the proposed algorithm is more efficient for cut detection than the previous methods, and that the basis for the automatic selection of threshold values.

• Journal of KIISE:Databases
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• v.28 no.3
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• pp.418-429
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• 2001

Recently, a GIS has been applicable to the most important computer applications such as urban information systems and transportation information systems. These applications require spatial operations for an efficient management of a large volume of data. In particular, a spatial join among basic operations has the property that its response time is increased exponentially according to the number of spatial objects included in the operation. Therefore, it is not proper to the systems demanding the fast response time. To satisfy these requirements, the efficient parallel processing of spatial joins has been required. In this paper, the efficient method for creating and allocating tasks to balance statically the load of each processor in a parallel spatial join is presented. A task graph is developed in which a vertex weight is calculated by the cost model I have proposed. Then, it is partitioned through a graph partitioning algorithm. According to the experiments in CC16 parallel machine, our method made an improvement in the static load balance by decreasing the variance of a task execution time on each processor.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.4 no.1
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• pp.115-124
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• 1968

It was shown that the most desirable characters for kenaf are high-fiber weight and moderately early maturity. Therefore, the objectives of this research on this crop is to find varieties possessing these characteristics. The experiments covered in this report provided new information relative to segregation, mode of inheritance, estimate of the number of genes involved in fiber weight and their response to short day length of 10 hours and the qualitative characters, such as, color of stem, capsule, petiole and shape of leaves. The associations which exist among these characters are also indicated. Fiber weight per plant, days to flowering, Stem color, Petiole color, Capsule color, and shape of leaves were studied in parental, $F_1$.$F_2$and backcross populations of a cross between Dashkent, a low-fiber weight but early maturing kenaf variety, and G 38 F-1, a high-fiber weight but late maturing kenaf variety. Crosses were made using the varieties, Dashkent and G 38 F-1 as parents. The Dashkent parent had the following characteristics: green stems, capsules and petioles and lobed shaped leaves; 105.8234 mean-days to flowering in the field, and 106.9222 mean-days under 10 hours short day treatment. The other parent, G 38 F-1 had red stems yellow capsules and red petioles and unlobed shaped leaves; 149.8921 mean-days to flowering in the field, and 62.3684 mean-days under 10 hours short day treatment. Both of the parents, $F_1$, $F_2$, $BC_1$ ($F_1$ X Dashkent, ) and $BC_2$($F_1$ ${\times}$ G38F-1) of the kenaf cross were grown at the Crops Experiment Station, Suwon, Korea in 1965. Color of stems, petioles and capsules, and shape of leaves were noted to be simply inherited as a single factor. Red stem color was dominant over green stem color, red petiole color was dominant over green petiole, lobed shaped leaves were dominant over unlobed shaped leaves and yellow capsules were dominant over green capsule. It was, also, noted that the factor for color of petiole was linked with the factor for shape of leaf with a 11.9587 percent recombination value, however no interaction or linkage were found among the color of stem and capsule color. Using Powers partitioning method, theoretical means and frequency distributions for each population, the days to flowering were calculated with the assumption that two gene pairs were involved. The values obtained fitted the theoretical values. In general this would indicate that Dashkent and G 38 F -1 were differentiated by two gene pairs. Heritability values were calculated as the percent of additive genetic variance. Heritability value of days to flowering, 89.5% in the broad sense and 79.91% in the narrow sense, indicated that the selection for this character would be effective in relatively early generations. Particularly, high positive correlations were found between days to flowering and the color of petioles and shape of leaves. However, there was no relation between days to flowering and capsule color nor between these and stem color. On the basis of the results of this experiment there is evidence that the hereditary factor for shape of leaves and the color of petioles is linked with an effective factor or factors for the characters of days to flowering. The association was sufficiently close to offer a possible simple and efficient means of selection for moderately early mat. uring plants by leaf shape and petiole color selection. Again using Powers partitioning method the frequency distribution for each population to the fiber weight were calculated with the assumption that two gene pairs, AaBb, were involved. Both phenotypic and genotypic dominance were complete. The obtained value did not agree with the theoretical value for $F_2$ and $BC_1$ ($F_1$ ${\times}$ Dashkent.) It seems that Dashkent and G 38 F-1 were differentiated by two major gene pairs but some the other minor genes are necessary. It is certain that the hereditary factor for shape of leaves and color of petioles is linked with an effective factor or factors for fiber weight. Also, high. yielding plants with moderately early maturity were found in the $F_2$ population. Thus, simultaneous selection for high-fiber yield and moderately early maturing plants should be possible in these populations. Phenotypic and genotypic correlation coefficients between fiber weight per plant and days to flowering, stem height and stem diameter were calculated. In general, genotypic correlations are higher than the phenotypic correlation. The highest correlation is found between stem height and fiber weight per plant (0.7852 in genotypic and 0.4103 in phenotypic) and between days to flowering and fiber weight per plant (0.7398 in genotypic and 0.3983 in phenotypic.) It was also expected that the selection of high stem height and moderately early maturing plants were given the efficient means of selection for high fiber weight.