• Proceedings of the KWS Conference
• /
• 1995.10a
• /
• pp.3-20
• /
• 1995

Development of Real-Time Quality Evaluation of Friction Welding by Acousitc Emission : Report 1 ABSTRACT : According as the friction welding has been increasingly applied in manufacturing various machine components because of its significant economic and technical advantages, one of the important concerns is the reliable quality monitoring method for a good weld quality with both joint strength and toughness in the process of its production. However no reliable nondestructive test method is available at present to determine the weld quality particularly in process of production. So this paper presents an experimental examination and quantitative analysis for the real-time evaluation of friction weld quality by acoustic emission, as a new approach which attempts finally to develop an on-line quality monitoring system design for friction welds using AE techniques. As one of the important results, it was confirmed, through this study, that AE techniques can be reliably applied to evaluating the friction weld qualify with 100% joint strength, as the cumulative AE counts occurring during welding period were quantitatively correlated with reliability at 95% confidence level to the joint strength of welds. Real-Time Evaluation of Automatic Production Quality Control for Friction Welding Machine : Report 2 Abstract : Both in-process quality control and high reliability of the weld is one of the major concerns in applying friction welding to the economical and qualified mass-production. No reliable nondestructive monitoring method is available at present to determine the real-time evaluation of automatic production quality control for friction welding machine. This paper, so that, presents the experimental examinations and statistical quantitative analysis of the correlation between the initial cumulative counts of acoustic emission(AE) occurring during plastic deformation period of the welding and the tensile strength of the welded joints as well as the various welding variables, as a new approach which attempts finally to develop an on-line (or real-time) quality monitoring system and a program for the process of real-time friction welding quality evaluation by initial AE cumulative counts. As one of the important results, it was well confirmed that the initial AE cumulative counts were quantitatively and cubically correlated with reliability of 95% confidence level to the joint strength of the welds, bar-to-bar (SCM4 to SUM31, SCM4 to SUM24L) and that an AE technique using initial AE counts can be reliably applied to real-time strength evaluation of the welded joints, and that such a program of the system was well developed resulting in practical possibility of real-time quality control more than 100% joint efficiency showing good weld with no micro-structural defects.

• Journal of KIISE
• /
• v.41 no.11
• /
• pp.911-920
• /
• 2014

In this paper, we introduce an automatic scoring system for middle-school level writing test based on using machine learning techniques. We discuss overall process and features for building an automatic English writing scoring system. A "concept answer" which represents an abstract meaning of text is newly introduced in order to evaluate the elaboration of a student's answer. In this work, multiple machine learning algorithms are adopted for scoring English writings. We suggest a decision process "optimal combination" which optimally combines multiple outputs of machine learning algorithms and generates a final single output in order to improve the performance of the automatic scoring. By experiments with actual test data, we evaluate the performance of overall automated English writing scoring system.

• Journal of Korea Multimedia Society
• /
• v.1 no.1
• /
• pp.109-119
• /
• 1998

This paper describes a research result on automatic generation of Abstract Test Suite from INAP protocol in formal specifications by applying many existing related algorithms such as Rural Chinese Postman Tour and UIO sequence concepts. We use the I/O FSM generated from SDL specifications and a characterizing sequence concepts. We use the I/O FSM generated from SDL specifications and a characterizing sequence, called UIO sequence, is defined for the I/O FSM. The UIO sequence is combined with the concept of Rural Chinese Postman tour to obtain an optimal test sequence. It also proposes an estimation methodology of the fault courage for the Test Suite obtained by our method and their translation into the standardized test notation TTCN.

• International Journal of Computer Science & Network Security
• /
• v.22 no.12
• /
• pp.125-131
• /
• 2022

The diversity and complexity of criminal, administrative and civil cases resolved by the courts makes it difficult to develop universal automated tools for the analysis and evaluation of justice. However, big data generated in the scope of justice gives hope that this problem will be resolved as soon as possible. The big data applying makes it possible to identify typical options for resolving cases, form detailed rules for the individualization of a court decision, and correlate these rules with an abstract provisions of law. This approach allows us to somewhat overcome the contradiction between the abstract and the concrete in law, to automate the analysis of justice and to model e-justice for scientific and practical purposes. The article presents the results of using dimension reduction, SHAP value, and p-value to identify, analyze and evaluate the individualization of justice and the differentiation of legal regulation. Processing and analysis of arrays of court decisions by computational methods make it possible to identify the typical views of courts on questions of fact and questions of law. This knowledge, obtained automatically, is promising for the scientific study of justice issues, the improvement of the prescriptions of the law and the probabilistic prediction of a court decision with a known set of facts.

• Journal of the Korean Society of Marine Environment & Safety
• /
• v.11 no.1 s.22
• /
• pp.11-16
• /
• 2005

A ship can be considered as a large human-machine system and the interaction between worker and system affects the work performance and its efficiency. In the bridge if a ship, there exist many auditory signals as well as visual signals. However, only a few studies have been performed related to human recognition to alarm systems in the bridge. In this study, auditory icons and abstract sounds are compared to find more effective means if alarm systems. The study result shows that auditory icons are recognized faster than n abstract sounds. The result is expected to be use as a basic data for developing performance criteria q auditory display inside bridge and for designing integrated ship bridge alarm system.

• Proceedings of KOSOMES biannual meeting
• /
• 2005.05a
• /
• pp.7-12
• /
• 2005

A ship can be considered as a large human-machine system and the interaction between worker and system affects the work performed and its efficiency. Inside the bridge of a ship, there exist many auditory signals as well as visual signals. However, only a few studies have been performed related to human recognition to alarm systems in bridge. In this study, auditory icons and abstract sounds are compared to find more effective means of alarm systems. the study result shows tint auditory icons are recognized faster than abstract sounds. This result is expected to be used as a basic data for developing performance criteria of auditory display inside bridge and for designing integrated ship bridge alarm system.

• Journal of Information Science Theory and Practice
• /
• v.7 no.4
• /
• pp.33-44
• /
• 2019

Altmetrics measure the frequency of references about an article on social media platforms, like Twitter. This paper studies a variety of factors that affect the popularity of articles (i.e., the number of article mentions) in the field of psychology on Twitter. Firstly, in this study, we classify Twitter users mentioning research articles as academic versus non-academic users and experts versus non-experts, using a machine learning approach. Then we build a negative binomial regression model with the number of Twitter mentions of an article as a dependant variable, and nine Twitter related factors (the number of followers, number of friends, number of status, number of lists, number of favourites, number of retweets, number of likes, ratio of academic users, and ratio of expert users) and seven article related factors (the number of authors, title length, abstract length, abstract readability, number of institutions, citation count, and availability of research funding) as independent variables. From our findings, if a research article is mentioned by Twitter users with a greater number of friends, status, favourites, and lists, by tweets with a large number of retweets and likes, and largely by Twitter users with academic and expertise knowledge on the field of psychology, the article gains more Twitter mentions. In addition, articles with a greater number of authors, title length, abstract length, and citation count, and articles with research funding get more attention from Twitter users.

• Journal of architectural history
• /
• v.26 no.5
• /
• pp.7-18
• /
• 2017

The 1960s in Britain was the period of rapid economic and social change. Under this circumstance, the visionary architect Cedric Price designed the Fun Palace, of which idea came from the theatre producer, Joan Littlewood. They hoped this place to be an improvisational learning space, so Price proposed the building as 'kit of parts' which can respond to programmatic indeterminacy. Cybernetics was introduced to control this flexibility dramatically changed the character of the project from 'theatre of people' to 'interactive machine'. That resulted in the change of the status of user from subjective human beings to abstract data in the cybernetic algorithm as well, and led the project to a completely opposite direction from that Price intended. After Fun Palace, cybernetics technology could still be found in his other projects, and it can be assumed that this was because the algorithmic system of cybernetics were on the same line of thought of Price's idea - anti-building or 'kit of parts'. The effects of cybernetics varied in projects; Similar negative effect in Fun Palace can be found in Generator project, but on the other hand, in Potteries Thinkbelt project, cybernetics showed a positive aspect by contribution to the development of project on the formal analogy of algorithmic network.

• Journal of the Korea Society of Computer and Information
• /
• v.19 no.2
• /
• pp.151-159
• /
• 2014

We discuss the implementation of a compiler for an imperative programming language, using monad in Haskell. This compiler involves a recursive-descent parser conducting nondeterministic parsing, in which backtracking occurs to try with other rules when the application of a production rule fails to parse an input string. Haskell has some strong facilities for parsing. Its algebraic types represent abstract syntax trees in a smooth way, and program codes by monad parsing are so concise that they are highly readable and code size is reduced significantly, comparing with other languages. We also deal with the runtime environment of the assembler and code generation whose target is the Stack-Assembly language based on a stack machine.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.15 no.6
• /
• pp.2188-2203
• /
• 2021

With the rapid development of mobile Internet, smart phones have been widely popularized, among which Android platform dominates. Due to it is open source, malware on the Android platform is rampant. In order to improve the efficiency of malware detection, this paper proposes deep learning Android malicious detection system based on behavior features. First of all, the detection system adopts the static analysis method to extract different types of behavior features from Android applications, and extract sensitive behavior features through Term frequency-inverse Document Frequency algorithm for each extracted behavior feature to construct detection features through unified abstract expression. Secondly, Long Short-Term Memory neural network model is established to select and learn from the extracted attributes and the learned attributes are used to detect Android malicious applications, Analysis and further optimization of the application behavior parameters, so as to build a deep learning Android malicious detection method based on feature analysis. We use different types of features to evaluate our method and compare it with various machine learning-based methods. Study shows that it outperforms most existing machine learning based approaches and detects 95.31% of the malware.