• Journal of the Korean Society for Precision Engineering
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• v.14 no.5
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• pp.42-52
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• 1997

Computer aided process planning and Offline programming are decisive factors in successful implementation of automated robotic production. However, conventional offline programming procedure has proven ineffective due to time-consuming teaching process for robot programming and due to inefficient system modeling. The paper presents an efficient procedure to semi-automatically generate robot job programs for ship block welding applications. In the research, the teaching positions are automatically determined by predefined rules which are functions of the type and the dimensions of the given welding section of ship block. And a sequence of robot movements and welding conditions such as welding type, welding current, welding speed, and welding torch orientation, are determined by use of Standard Program which is experimentally proved to work well for the welding wection group. Finally, a robot program for the welding section is generated automatically. Based on the algorithm, a offline automatic teaching software is developed. The paper presents also the algorithm and structure of the software.

• Journal of KIISE
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• v.44 no.3
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• pp.287-294
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• 2017

With the personal-media development that has emerged through various means such as UCC and SNS, many media studies have been completed for the purposes of analysis and recognition, thereby improving the object-recognition level. The focus of these studies is a classification of media that is based on a recognition of the corresponding objects, rather than the use of the title, tag, and scripter information. The media-classification task, however, is intensive in terms of the consumption of time and energy because human experts need to model the underlying media ontology. This paper therefore proposes an automated approach for the modeling of the media-classification ontology schema; here, the OWL-DL Axiom that is based on the frequency of the recognized media-based objects is considered, and the automation of the ontology modeling is described. The authors conducted media-classification experiments across 15 YouTube-video categories, and the media-classification accuracy was measured through the application of the automated ontology-modeling approach. The promising experiment results show that 1500 actions were successfully classified from 15 media events with an 86 % accuracy.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.40 no.6
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• pp.90-97
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• 2003

This paper proposes a speech recognition based on HMM(Hidden Markov Model) using FSVQ(First Section Vector Quantization) and fuzzy concept. In the proposed paper, we generate codebook of First Section, and then obtain multi-observation sequences by order of large propabilistic values based on fuzzy rule from the codebook of the first section. Thereafter, this observation sequences of first section from codebooks is trained and in case of recognition, a word that has the most highest probability of first section is selected as a recognized word by same concept. Train station names are selected as the target recognition vocabulary and LPC cepstrum coefficients are used as the feature parameters. Besides the speech recognition experiments of proposed method, we experiment the other methods under same conditions and data. Through the experiment results, it is proved that the proposed method based on HMM using FSVQ and fuzzy concept is superior to tile others in recognition rate.

• Journal of the Korean Society of Safety
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• v.28 no.3
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• pp.107-113
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• 2013

Problem solving and/or decision making process usually encountered in human living consists of a sequence of human behaviors based upon his/her knowledge. Thus, Rasmussen introduced Skill-Rule-Knowledge paradigm to countermeasure human errors that can occur in Nuclear Power Plants. Unfortunately however, it was not so easy as expected since objective evidence have not been obtainable with conventional research techniques. With the help of EEG band pawer ratio techniques, this study tried to get psycho-physiological symptoms of human errors, if any, while human beings perform knowledge-based behaviors such as simple arithmetic computations with different difficulty level. A set of simulated works was carried out with a computer station. Four kinds of arithmetic computation tasks were given to 10 health male under-graduate students on different day individually, and during the experiment, EEG and ECG was measured continuously for objective psycho-physiological analysis. According to the results, ${\alpha}$/(${\alpha}+{\beta}$) as well as ${\alpha}/{\beta}$ band power ratio were sensitive to task difficulty level which consistently decreased both. However, any one of them failed to reveal the influence of tasks with different difficulty level in the aspect of task duration time. On the contrary, Heart Rate Variability was more suggestive than expected. To make a conclusion, it can be said that band power of EEG waves will be helpful in not only assessment of work difficulty level but also assessment of workers' skill development if supported by cardiac function such as HRV.

• Journal of KIISE
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• v.44 no.5
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• pp.496-502
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• 2017

Pointer Networks is a deep-learning model for the attention-mechanism outputting of a list of elements that corresponds to the input sequence and is based on a recurrent neural network (RNN). The coreference resolution for pronouns is the natural language processing (NLP) task that defines a single entity to find the antecedents that correspond to the pronouns in a document. In this paper, a pronoun coreference-resolution method that finds the relation between the antecedents and the pronouns using the Pointer Networks is proposed; furthermore, the input methods of the Pointer Networks-that is, the chaining order between the words in an entity-are proposed. From among the methods that are proposed in this paper, the chaining order Coref2 showed the best performance with an F1 of MUC 81.40 %. The method showed performances that are 31.00 % and 19.28 % better than the rule-based (50.40 %) and statistics-based (62.12 %) coreference resolution systems, respectively, for the Korean pronouns.

• Journal of KIISE
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• v.43 no.4
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• pp.470-479
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• 2016

As the number of various types of media data such as UCC (User Created Contents) increases, research is actively being carried out in many different fields so as to provide meaningful media services. Amidst these studies, a semantic web-based media classification approach has been proposed; however, it encounters some limitations in video classification because of its underlying ontology derived from meta-information such as video tag and title. In this paper, we define recognized objects in a video and activity that is composed of video objects in a shot, and introduce a reasoning approach based on description logic. We define sequential rules for a sequence of shots in a video and describe how to classify it. For processing the large amount of increasing media data, we utilize Spark streaming, and a distributed in-memory big data processing framework, and describe how to classify media data in parallel. To evaluate the efficiency of the proposed approach, we conducted an experiment using a large amount of media ontology extracted from Youtube videos.

• The KIPS Transactions:PartD
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• v.15D no.4
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• pp.441-450
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• 2008

Recently, access control on form-based web information systems has become one of the useful methods for implementing client systems in a service-oriented architecture. In particular, XForms language is being adopted in many systems as a description language for XML-based user interfaces and server interactions. In this paper, we propose an efficient algorithm for the evaluation of XPath-based access rules for XForms pages. In this model, an XForms page is a sequence of queries and the client system performs user interface realization along with XPath rule evaluations. XPath rules have instance-dependent predicates, which for the most part are shared between rules. For the efficient evaluation of shared predicate expressions in access control rules, we proposed a predicate graph model that reuses the previously evaluated results for the same context node. This approach guarantees that each predicate expression is evaluated for the relevant xml node only once.

• Journal of the Korea Concrete Institute
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• v.20 no.6
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• pp.697-704
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• 2008

This paper presents an analytical prediction of Nonlinear characteristics of prestressed concrete bridges by strengthened of externally tendon considering construction sequence, using unbonded tendon element and beam-column element based on flexibility method. Unbonded tendon model can represent unbounded tendon behavior in concrete of PSC structures and it can deal with the prestressing transfer of posttensioned structures and calculate prestressed concrete structures more efficiently. This tendon model made up the several nodes and segment, therefore a real tendon of same geometry in the prestressed concrete structure can be simulated the one element. The beam-column element was developed with reinforced concrete material nonlinearities which are based on the smeared crack concept. The fiber hysteresis rule of beam-column element is derived from the uniaxial constitutive relations of concrete and reinforcing steel fibers. The formulation of beam-column element is based on flexibility. Beam-column element and unbonded tendon element were be involved in A computer program, named RCAHEST (Reinforced Concrete Analysis in Higher Evaluation System Technology), that were used the analysis of RC and PSC structures. The proposed numerical method for prestressed concrete structures by strengthened of externally tendon is verified by comparison with reliable experimental results.

• Journal of Internet Computing and Services
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• v.22 no.2
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• pp.77-87
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• 2021

With the development of the Internet and personal computers, various and complex attacks begin to emerge. As the attacks become more complex, signature-based detection become difficult. It leads to the research on behavior-based log anomaly detection. Recent work utilizes deep learning to learn the order and it shows good performance. Despite its good performance, it does not provide any explanation for prediction. The lack of explanation can occur difficulty of finding contamination of data or the vulnerability of the model itself. As a result, the users lose their reliability of the model. To address this problem, this work proposes an explainable log anomaly detection system. In this study, log parsing is the first to proceed. Afterward, sequential rules are extracted by Bayesian posterior probability. As a result, the "If condition then results, post-probability" type rule set is extracted. If the sample is matched to the ruleset, it is normal, otherwise, it is an anomaly. We utilize HDFS datasets for the experiment, resulting in F1score 92.7% in test dataset.

• Journal of Information Processing Systems
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• v.9 no.4
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• pp.511-537
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• 2013

Numerous data intensive applications demand the efficient processing of a new type of query, which is called a progressive query (PQ). A PQ consists of a set of unpredictable but inter-related step-queries (SQ) that are specified by its user in a sequence of steps. A conventional DBMS was not designed to efficiently process such PQs. In our earlier work, we introduced a materialized view based approach for efficiently processing PQs, where the focus was on selecting promising views for materialization. The problem of how to efficiently find usable views from the materialized set in order to answer the SQs for a PQ remains open. In this paper, we present a new index technique, called the Dynamic Materialized View Index (DMVI), to rapidly discover usable views for answering a given SQ. The structure of the proposed index is a special ordered tree where the SQ domain tables are used as search keys and some bitmaps are kept at the leaf nodes for refined filtering. A two-level priority rule is adopted to order domain tables in the tree, which facilitates the efficient maintenance of the tree by taking into account the dynamic characteristics of various types of materialized views for PQs. The bitmap encoding methods and the strategies/algorithms to construct, search, and maintain the DMVI are suggested. The extensive experimental results demonstrate that our index technique is quite promising in improving the performance of the materialized view based query processing approach for PQs.