• Journal of Astronomy and Space Sciences
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• v.25 no.4
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• pp.425-434
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• 2008

The mean temperature equation is a key factor in calculating GPS meteorological information. A local mean temperature equation should be used to improve accuracy of GPS PWV (Precipitable Water Vapor). In this paper, four local mean temperature equations, HP, $HP_M,\;HPt_Y,\;and\;HPt_M$ from Ha & Park (2008) were used to analyze the effects of local models in determining GPS PWV. Four different sets of GPS PWVs were compared with radiosonde PWV to validate the accuracies of local models. GPS PWVs of four local models have similar trends compared against radiosonde PWV. The bias and RMS error were the same level: the bias is ${\sim}3mm$ and the RMS is ${\sim}3.6mm$ after the bias was removed. Especially, with $HPt_Y\;and\;HPt_M$ models one can obtain accurate PWVs even without surface temperature measurements. And we investigated dry bias of radiosonde measurements depending on sensor types and observation time at Sokcho weather station. After the radiosonde sensor equipment was changed from RS80-15L to GRS DFM-06, dry bias of radiosonde PWV decreased about 18.2% during daytime (KST 09:00), and 16.1% during nighttime (KST 21:00).

• Journal of KIISE:Software and Applications
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• v.35 no.3
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• pp.179-188
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• 2008

An important issue in multiagent reinforcement learning is how an agent should team its optimal policy through trial-and-error interactions in a dynamic environment where there exist other agents able to influence its own performance. Most previous works for multiagent reinforcement teaming tend to apply single-agent reinforcement learning techniques without any extensions or are based upon some unrealistic assumptions even though they build and use explicit models of other agents. In this paper, basic concepts that constitute the common foundation of multiagent reinforcement learning techniques are first formulated, and then, based on these concepts, previous works are compared in terms of characteristics and limitations. After that, a policy model of the opponent agent and a new multiagent reinforcement learning method using this model are introduced. Unlike previous works, the proposed multiagent reinforcement learning method utilize a policy model instead of the Q function model of the opponent agent. Moreover, this learning method can improve learning efficiency by using a simpler one than other richer but time-consuming policy models such as Finite State Machines(FSM) and Markov chains. In this paper. the Cat and Mouse game is introduced as an adversarial multiagent environment. And effectiveness of the proposed multiagent reinforcement learning method is analyzed through experiments using this game as testbed.

• Journal of KIISE:Software and Applications
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• v.37 no.4
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• pp.239-251
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• 2010

In modern distributed enterprise applications that have multilayered architecture, business entities are a kind of crosscutting concerns running through service components that implements business logic in each layer. When business entities are modified, service components related to them should also be modified so that they can deal with those business entities with new types, even though their functionality remains the same. Our previous paper proposed what we call the DTT (Data Type-Tolerant) component model to efficiently process the variability of business entities, which are data externalized from service components. While the DTT component model, by removing direct coupling between service components and business entities, exempts the need to rewrite service components when business entities are modified, it incurs the burden of implementing data type converters that mediate between them. To solve this problem, this paper proposes a method to use ontology as the metadata of both SCDTs (Self-Contained Data Types) in service components and business entities, and a method to generate data type converter code using the ontology. This ontology-based DTT component model greatly enhances the reusability of service components and the efficiency in processing data variability by allowing the computer to automatically generate data type converters without error.

• 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.

• Journal of KIISE:Software and Applications
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• v.33 no.4
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• pp.402-410
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• 2006

This paper presents a method for feature based block motion estimation that uses multi -resolution image sequences to obtain the panoramic images in the continuous ultrasound image sequences. In the conventional block motion estimation method, the centers of motion estimation blocks are set at the predetermined and equally spaced locations. This requires the large blocks to include at least one feature, which inevitably requires long estimation time. In this paper, we propose an adaptive method which locates the center of the motion estimation blocks at the feature points. This make it possible to reduce the block size while keeping the motion estimation accuracy The Harris-Stephen corner detector is used to get the feature points. The comer points tend to group together, which cause the error in the global motion estimation. In order to distribute the feature points as evenly as Possible, the image is firstly divided into regular subregions, and a strongest corner point is selected as a feature in each subregion. The ultrasound Images contain speckle patterns and noise. In order to reduce the noise artifact and reduce the computational time, the proposed method use the multi-resolution image sequences. The first algorithm estimates the motion in the smoothed low resolution image, and the estimated motion is prolongated to the next higher resolution image. By this way the size of search region can be reduced in the higher resolution image. Experiments were performed on three types of ultrasound image sequences. These were shown that the proposed method reduces both the computational time (from 77ms to 44ms) and the displaced frame difference (from 66.02 to 58.08).

• Journal of KIISE:Software and Applications
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• v.33 no.5
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• pp.508-524
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• 2006

We present our experience of combining, in a realistic setting, a static analyzer with a statistical analysis. This combination is in order to reduce the inevitable false alarms from a domain-unaware static analyzer. Our analyzer named Airac(Array Index Range Analyzer for C) collects all the true buffer-overrun points in ANSI C programs. The soundness is maintained, and the analysis' cost-accuracy improvement is achieved by techniques that static analysis community has long accumulated. For still inevitable false alarms (e.g. Airac raised 970 buffer-overrun alarms in commercial C programs of 5.3 million lines and 737 among the 970 alarms were false), which are always apt for particular C programs, we use a statistical post analysis. The statistical analysis, given the analysis results (alarms), sifts out probable false alarms and prioritizes true alarms. It estimates the probability of each alarm being true. The probabilities are used in two ways: 1) only the alarms that have true-alarm probabilities higher than a threshold are reported to the user; 2) the alarms are sorted by the probability before reporting, so that the user can check highly probable errors first. In our experiments with Linux kernel sources, if we set the risk of missing true error is about 3 times greater than false alarming, 74.83% of false alarms could be filtered; only 15.17% of false alarms were mixed up until the user observes 50% of the true alarms.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.7
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• pp.42-48
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• 2016

In this paper, a high-precision peak detector circuit that detects the output voltage information of a fly-back converter is proposed. The proposed design consists of basic analog elements with only one operational amplifier and three transistors. Because of its simple structure, the proposed circuit can minimize the delay time of the detection process, which has a strong impact on the precision of the regulation aspect of the fly-back converter. Furthermore, by using an amplifier and several transistors, the proposed detector can be fully integrated on-chip, instead of using discrete circuit elements, such as capacitors and diodes, as in conventional designs, which reduces the production cost of the fly-back converter module. In order to verify the performance of the proposed scheme, the peak detector was simulated and implemented by using a 0.35 m MagnaChip process. The gained results from the simulation with a sinusoidal stimulus signal show a very small detection error in the range of 0.3~3.1%, which is much lower than other reported detecting circuits. The measured results from the fabricated chip confirm the simulation results. As a result, the proposed peak detector is recommended for designs of high-performance fly-back converters in order to improve the poor regulation aspect seen in conventional designs.

• Journal of KIISE:Software and Applications
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• v.29 no.3
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• pp.167-179
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• 2002

In this paper, we propose a new acoustic model for characterizing segmental features and an algorithm based upon a general framework of hidden Markov models (HMMs) in order to compensate the weakness of HMM assumptions. The segmental features are represented as a trajectory of observed vector sequences by a polynomial regression function because the single frame feature cannot represent the temporal dynamics of speech signals effectively. To apply the segmental features to pattern classification, we adopted segmental HMM(SHMM) which is known as the effective method to represent the trend of speech signals. SHMM separates observation probability of the given state into extra- and intra-segmental variations that show the long-term and short-term variabilities, respectively. To consider the segmental characteristics in acoustic model, we present segmental-feature HMM(SFHMM) by modifying the SHMM. The SFHMM therefore represents the external- and internal-variation as the observation probability of the trajectory in a given state and trajectory estimation error for the given segment, respectively. We conducted several experiments on the TIMIT database to establish the effectiveness of the proposed method and the characteristics of the segmental features. From the experimental results, we conclude that the proposed method is valuable, if its number of parameters is greater than that of conventional HMM, in the flexible and informative feature representation and the performance improvement.

• The Journal of Korean Society for Radiation Therapy
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• v.18 no.2
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• pp.75-80
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• 2006

Purpose: In radiation therapy, precise calculation of dose toward malignant tumors or normal tissue would be a critical factor in determining whether the treatment would be successful. The Radiation Treatment Planning (RTP) system is one of most effective methods to make it effective to the correction of dose due to CT number through converting linear attenuation coefficient to density of the inhomogeneous tissue by means of CT based reconstruction. Materials and Methods: In this study, we carried out the measurement of CT number and calculation of mass density by using RTP system and the homemade inhomogeneous tissue Phantom and the values were obtained with reference to water. Moreover, we intended to investigate the effectiveness and accuracy for the correction of inhomogeneous tissue by the CT number through comparing the measured dose (nC) and calculated dose (Percentage Depth Dose, PDD) used CT image during radiation exposure with RTP. Results: The difference in mass density between the calculated tissue equivalent material and the true value was ranged from $0.005g/cm^3\;to\;0.069g/cm^3$. A relative error between PDD of RTP and calculated dose obtained by radiation therapy of machine ranged from -2.8 to +1.06%(effective range within 3%). Conclusion: In conclusion, we confirmed the effectiveness of correction for the inhomogeneous tissues through CT images. These results would be one of good information on the basic outline of Quality Assurance (QA) in RTP system.

• Journal of Satellite, Information and Communications
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• v.2 no.2
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• pp.16-21
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• 2007

Various perturbations by the sun, the moon and the earth itself cause a continuous change in nominal position of a geostationary satellite. In order to maintain the satellite within a required window, north-south station keeping for controlling inclination and right ascension of ascending node, and east-west station keeping for controlling eccentricity and longitude are required. In this paper, station keeping maneuver simulation for Communication, Ocean and Meteorological Satellite (COMS) was performed using COMS Flight Dynamics Software(FDS) and the results were analyzed. COMS performs weekly based east-west/north-south station keeping to maintain satellite within ${\pm}0.05^{\circ}$ at the nominal longitude of $128.2^{\circ}E$. In addition, COMS performs wheel off-loading maneuver twice a day to eliminate attitude error caused by one-solar wing in the south panel of the satellite. In this paper, station keeping maneuver considering wheel off-loading maneuver was performed and the results showed that COMS can be maintained well within ${\pm}0.05^{\circ}$ window using COMS FDS.