• Journal of Internet Computing and Services
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• v.20 no.2
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• pp.101-108
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• 2019

Workflow management systems (WfMSs) automate and manage workflows, which are implementations of organizational processes operated in process-centric organizations. In this paper, wepropose an algorithm to discover temporal work transference networks from workflow execution logs. The temporal work transference network is a special type of enterprise social networks that consists of workflow performers, and relationships among them that are formed by work transferences between performers who are responsible in performing precedent and succeeding activities in a workflow process. In terms of analysis, the temporal work transference network is an analytical property that has significant value to be analyzed to discover organizational knowledge for human resource management and related decision-making steps for process-centric organizations. Also, the beginning point of implementinga human-centered workflow intelligence framework dealing with work transference networks is to develop an algorithm for discovering temporal work transference cases on workflow execution logs. To this end, we first formalize a concept of temporal work transference network, and next, we present a discovery algorithm which is for the construction of temporal work transference network from workflow execution logs. Then, as a verification of the proposed algorithm, we apply the algorithm to an XES-formatted log dataset that was released by the process mining research group and finally summarize the discovery result.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.5
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• pp.902-910
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• 2016

Recently, the volume of mobile data traffic increases exponentially due to the emergence of various mobile services. In order to resolve the problem of mobile traffic increase, various new technologies have been devised. Especially, two-way relay communication in which two nodes can transfer data simultaneously through relay node, has gained lots of interests due to its capability to improve spectral efficiency. In this paper, we analyze the SM-PNC which combines Physical-layer Network Coding (PNC) and Spatial Modulation (SM) under two-way relay communication environment. Log-Likelihood Ratio (LLR) is considered and both separate decoding and direct decoding have been taken into account in performance analysis. Through performance evaluation, we have found that the bit error rate of the proposed scheme is improved compared to that of the conventional PNC scheme, especially when SNR is high and the number of antennas is large.

• Journal of Korean Library and Information Science Society
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• v.21
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• pp.253-289
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• 1994

The purpose of this study is about the search pattern of LINNET (Library Information Network System) OPAC users by transaction log, maintained by POSTECH(Pohang University of Science and Technology) Central Library, to provide feedback information of OPAC system design. The results of this study are as follows. First, for the period of this analysis, there were totally 11, 218 log-ins, 40, 627 transaction logs and 3.62 retrievals per a log-in. Title keyword was the most frequently used, but accession number, bibliographic control number or call number was very infrequently used. Second, 47.02% of OPAC, searches resulted in zero retrievals. Bibliographic control number was the least successful search. User displayed 2.01% full information and 64.27% local information per full information. Third, special or advanced retrieval features are very infrequently used. Only 22.67% of the searches used right truncation and 0.71% used the qualifier. Only 1 boolean operator was used in every 22 retrievals. The most frequently used operator is 'and (&)' with title keywords. But 'bibliographical control number (N) and accessionnumber (R) are not used at all with any operators. The causes of search failure are as follows. 1. The item was not used in the database. (15, 764 times : 79.42%). 2. The wrong search key was used. (3, 761 times : 18.95%) 3. The senseless string (garbage) was entered. (324 times : 1.63%) On the basis of these results, some recommendations are suggested to improve the search success rate as follows. First, a n.0, ppropriate user education and online help function let users retrieve LINNET OPAC more efficiently. Second, several corrections of retrieval software will decrease the search failure rate. Third, system offers right truncation by default to every search term. This methods will increase success rate but should considered carefully. By a n.0, pplying this method, the number of hit can be overnumbered, and system overhead can be occurred. Fourth, system offers special boolean operator by default to every keyword retrieval when user enters more than two words at a time. Fifth, system assists searchers to overcome the wrong typing of selecting key by automatic korean/english mode change.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.7 no.6
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• pp.1312-1317
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• 2003

This paper presents an integrated approach to unconstrained face recognition in arbitrary scenes. The front end of the system comprises of a scale and pose tolerant face detector. Scale normalization is achieved through novel combination of a skin color segmentation and log-polar mapping procedure. Principal component analysis is used with the multi-view approach proposed in[10] to handle the pose variations. For a given color input image, the detector encloses a face in a complex scene within a circular boundary and indicates the position of the nose. Next, for recognition, a radial grid mapping centered on the nose yields a feature vector within the circular boundary. As the width of the color segmented region provides an estimated size for the face, the extracted feature vector is scale normalized by the estimated size. The feature vector is input to a trained neural network classifier for face identification. The system was evaluated using a database of 20 person's faces with varying scale and pose obtained on different complex backgrounds. The performance of the face recognizer was also quite good except for sensitivity to small scale face images. The integrated system achieved average recognition rates of 87% to 92%.

• Magazine of the Korean Society of Agricultural Engineers
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• v.41 no.6
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• pp.33-43
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• 1999

Drought is a serious diaster in agriculutre, especially to upland crops. Hence, the Agricultural Drought Analysis Model (ADAM) that is integratable with GIS was applied to analyae agriculture drought in upland. ADAM is composed of two sub-models , one is a Soil Water Balance Model (SWBM) and the other is a Drougth Analysis Model (DAM) that is based on the Runs theory. The ADAM needs weather data, rainfall data and soil physical characteristics data as input and calculates daily soil moisture contents. GIS was integrated to the ADAM for the calculation of regional soil moisture using digitized landuse map, detaile dsoil map, thiessen network and district boundary . For the agriculutral drought analysis, the ADAM adapt the Runs theory for analyzing drought duration, severity and magnitude . Log-Pearson Type-III probability distribution function and Kolmogorov-Smirnov test were used to test the fitness of good of the model. The integration of ADAM with GIS was successfully implemented and would be operated effectively for the regional drought analysis.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1042-1045
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• 2003

In this paper, we propose the speaker identification system that uses vowel that has speaker's characteristic. System is divided to speech feature extraction part and speaker identification part. Speech feature extraction part extracts speaker's feature. Voiced speech has the characteristic that divides speakers. For vowel extraction, formants are used in voiced speech through frequency analysis. Vowel-a that different formants is extracted in text. Pitch, formant, intensity, log area ratio, LP coefficients, cepstral coefficients are used by method to draw characteristic. The cpestral coefficients that show the best performance in speaker identification among several methods are used. Speaker identification part distinguishes speaker using Neural Network. 12 order cepstral coefficients are used learning input data. Neural Network's structure is MLP and learning algorithm is BP (Backpropagation). Hidden nodes and output nodes are incremented. The nodes in the incremental learning neural network are interconnected via weighted links and each node in a layer is generally connected to each node in the succeeding layer leaving the output node to provide output for the network. Though the vowel extract and incremental learning, the proposed system uses low learning data and reduces learning time and improves identification rate.

• Journal of Korea Society of Industrial Information Systems
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• v.19 no.6
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• pp.63-76
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• 2014

Recently the desired information has been recommended to many people in a number of ways using the smartphone. Though there are many applications for that purpose, but most applications does not consider the user's current situation. In order to automatically recommend the information considering the user's situation, it is necessary to predict the future behavior of the user from the records of the past behavior of the user. Therefore, in this paper, we propose a method that predicts the user's future behavior through association analysis based on the user's current behavior which is identified by applying the user's current situation data collected via a smartphone to the Bayesian network built from the user's life log. From the experiments and analysis for five students and five virtual workers, the usefulness of the proposed method is confirmed.

• Journal of the Korea Society of Computer and Information
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• v.9 no.4 s.32
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• pp.157-165
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• 2004

The paper suggests that web-server security management system will be able to detect the web service attack accurately and swiftly which is keeping on increasing at the moment and reduce the possibility of the false positive detection. This system gathers the results of many unit security modules at the real time and enhances the correctness of the detection through the correlation analysis procedure. The unit security module consists of Network based Intrusion Detection System module. File Integrity Check module. System Log Analysis module, and Web Log Analysis and there is the Correlation Analysis module that analyzes the correlations on the spot as a result of each unit security module processing. The suggested system provides the feasible framework of the range extension of correlation analysis and the addition of unit security module, as well as the correctness of the attack detection. In addition, the attack detection system module among the suggested systems has the faster detection time by means of restructuring Snort with multi thread base system. WSM will be improved through shortening the processing time of many unit security modules with heavy traffic.

• Animal Bioscience
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• v.36 no.1
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• pp.10-18
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• 2023

Objective: In this study, we aimed to position the Hungarian Merino among other Merinoderived sheep breeds, explore the characteristics of our sampled animals' genetic similarity network within the breed, and highlight single nucleotide polymorphisms (SNPs) associated with daily weight-gain. Methods: Hungarian Merino (n = 138) was genotyped on Ovine SNP50 Bead Chip (Illumina, San Diego, CA, USA) and positioned among 30 Merino and Merino-derived breeds (n = 555). Population characteristics were obtained via PLINK, SVS, Admixture, and Treemix software, within-breed network was analysed with python networkx 2.3 library. Daily weight gain of Hungarian Merino was standardised to 60 days and was collected from the database of the Association of Hungarian Sheep and Goat Breeders. For the identification of loci associated with daily weight gain, a multi-locus mixed-model was used. Results: Supporting the breed's written history, the closest breeds to Hungarian Merino were Estremadura and Rambouillet (pairwise F_ST values are 0.035 and 0.036, respectively). Among Hungarian Merino, a highly centralised connectedness has been revealed by network analysis of pairwise values of identity-by-state, where the animal in the central node had a betweenness centrality value equal to 0.936. Probing of daily weight gain against the SNP data of Hungarian Merinos revealed five associated loci. Two of them, OAR8_17854216.1 and s42441.1 on chromosome 8 and 9 (-log₁₀P>22, false discovery rate<5.5e-20) and one locus on chromosome 20, s28948.1 (-log₁₀P = 13.46, false discovery rate = 4.1e-11), were close to the markers reported in other breeds concerning daily weight gain, six-month weight, and post-weaning gain. Conclusion: The position of Hungarian Merino among other Merino breeds has been determined. We have described the similarity network of the individuals to be applied in breeding practices and highlighted several markers useful for elevating the daily weight gain of Hungarian Merino.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.12A
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• pp.949-961
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• 2009

This paper analyses BER (Bit Error Rate) performance of 2-hop wireless communications networks with hybrid decode-and-forward (HDF) relays. The conventional HDF method is usually based on the receive signal-to-noise ratio (SNR) for the relay to decide whether to forward the decoded data in order to obviate the erroneous detection at the relay. In contrast, we propose a new solution of using log-likelihood ratio (LLR) as an efficient alternative to SNR. The approximate BER expressions of different HDF schemes are also derived and verified by Monte-Carlo simulations. In addition, we compute the optimum thresholds for HDF schemes. A variety of numerical results demonstrate that the new LLR-based HDF significantly outperforms the SNR-based HDF for any threshold level and relay location under flat Rayleigh fading channel plus AWGN (Additive White Gaussian Noise).