• Psychiatry investigation
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• v.15 no.12
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• pp.1144-1153
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• 2018

Objective One of the areas of social cognition is Theory of Mind (ToM) is defined as the capacity to interpret, infer and explain mental states underlying the behavior of others. When social cognition studies on neurodevelopmental disorders are examined, it can be seen that this skill has not been studied sufficiently in children with Specific Learning Disorder (SLD). Methods In this study, social cognition skills in children diagnosed with attention deficit hyperactivity disorder (ADHD), SLD or Autism Spectrum Disorder (ASD) evaluated before puberty and compared with controls. To evaluate the ToM skills, the first and second-order false belief tasks, the Hinting Task, the Faux Pas Test and the Reading the Mind in the Eyes Task were used. Results We found that children with neurodevelopmental disorders as ADHD, ASD, and SLD had ToM deficits independent of intelligence and language development. There was a significant correlation between social cognition deficits and problems experienced in many areas such as social communication and interaction, attention, behavior, and learning. Conclusion Social cognition is an important area of impairment in SLD and there is a strong relationship between clinical symptoms and impaired functionality.

• Advances in concrete construction
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• v.14 no.1
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• pp.15-34
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• 2022

The use of waste rubber in concrete can reduce natural aggregate consumption and improve some technical properties of concrete. Although there are several equations for estimating the mechanical properties of concrete containing waste rubber, limited numbers of machine learning-based models have been proposed to predict the mechanical properties of rubbercrete. In this study, an extensive database of the mechanical properties of rubbercrete was gathered from a comprehensive survey of the literature. To model the mechanical properties of rubbercrete, M5P tree and linear gene expression programming (LGEP) methods as two machine learning techniques were employed to achieve reliable mathematical equations. Two procedures of input variable selection were considered in this study. The crucial component ratios of rubbercrete and concrete age were assumed as the input variables in the first procedure. In contrast, the volumes of the coarse and fine waste rubber and the compressive strength of concrete without waste rubber were considered the second procedure of the input variables. The results show that the models obtained by LGEP are more accurate than those achieved by the M5P model tree and existing traditional equations. Besides, the volumes of the coarse and fine waste rubber and the compressive strength of concrete without waste rubber are better predictors of the mechanical properties of rubbercrete compared to the first procedure of input variable selection.

• Nuclear Engineering and Technology
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• v.56 no.4
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• pp.1472-1479
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• 2024

Cyber-Physical Energy Systems (CPESs) integrate cyber and hardware components to ensure a reliable and safe physical power production and supply. Renewable Energy Sources (RESs) add uncertainty to energy demand that can be dealt with flexible operation (e.g., load-following) of CPES; at the same time, scenarios that could result in severe consequences due to both component stochastic failures and aging of the cyber system of CPES (commonly overlooked) must be accounted for Operation & Maintenance (O&M) planning. In this paper, we make use of Deep Reinforcement Learning (DRL) to search for the optimal O&M strategy that, not only considers the actual system hardware components health conditions and their Remaining Useful Life (RUL), but also the possible accident scenarios caused by the failures and the aging of the hardware and the cyber components, respectively. The novelty of the work lies in embedding the cyber aging model into the CPES model of production planning and failure process; this model is used to help the RL agent, trained with Proximal Policy Optimization (PPO) and Imitation Learning (IL), finding the proper rejuvenation timing for the cyber system accounting for the uncertainty of the cyber system aging process. An application is provided, with regards to the Advanced Lead-cooled Fast Reactor European Demonstrator (ALFRED).

• Journal of The Korean Association For Science Education
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• v.12 no.2
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• pp.19-29
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• 1992

In this study, five learning models were compared and discussed in terms of their learning procedures and learning strateies. After a brief introduction of each model, the author discussed the differences and similarities among the five learning models. As a result, Kwon's procedual learning (Kwon, 1989) seemed to encompass almost all the learning models proposed by the other four author. All the models emphasized the importance of cognitive conflict. However, I. K.Kim(1991), Park(1992) and Y.M.Kim(1991) seemed to be concentrated their attention on the cognitive conflict between concepts ; while Hashweh and Kwon emphasized cognitive conflict between cognitive structure and environment. The study also suggested more study on the empirical evidence of the three kinds of the cognitive conflicts proposed by Kwon(1989) and on the development of learning strategies to induce and overcome the cognitive conflicts.

• Asia pacific journal of information systems
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• v.17 no.1
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• pp.33-58
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• 2007

Quiet often, an organization tries to grapple with inconsistent and partial information to generate relevant information to support decision making and action. As such, an organization scans the environment interprets scanned data, executes actions, and learns from feedback of actions, which boils down to computational interpretations and learning in terms of machine learning, statistics, and database. The ExOM proposed in this paper is geared to facilitate such knowledge discovery found in large databases in a most flexible manner. It supports a broad range of learning and classification styles and integrates them with traditional database functions. The learning and classification components of the ExOM are tightly integrated so that learning and classification of objects is less burdensome to ordinary users. A brief sketch of a strategy as to the expressiveness of terminological language is followed by a description of prototype implementation of the learning and classification components of the ExOM.

• Proceedings of the Korea Information Processing Society Conference
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• 2018.05a
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• pp.362-364
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• 2018

핸드폰 사용자가 늘어나면서 이와 관련하여 개인 정보 보안에 대한 중요성이 대두되고 있다. 이에 따라 제안된 알고리즘은 Extreme learning machine 으로부터 착안하여 변형하여 고안한 Maxtreme Learning Machine(MLM) 으로, 사용자들의 터치 스트로크 특성 벡터를 제안 알고리즘으로 학습하여 사용자들을 검증한다. 또한 특성 벡터의 순차적 융합 기법을 이용하여 더 많은 정보를 바탕으로 사용자를 높은 정확도로 검증 할 수 있다.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.12 no.1
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• pp.29-36
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• 2019

Unmanned Autonomous Vehicle's driving road in the national defense includes not only paved roads, but also unpaved roads which have rough and unexpected changes. This Unmanned Autonomous Vehicles monitor and recon rugged or remote areas, and defend own position, they frequently encounter environments roads of various and unpredictable. Thus, they need additional learning to drive in this environment, we propose a Additional Learning Nearest Neighbor (ALNN) which is modified from Approximate Nearest Neighbor to allow for quick learning while avoiding the 'Forgetting' problem. In addition, since the Execution speed of the ALNN algorithm decreases as the learning data accumulates, we also propose a solution to this problem using GPU parallel processing based on OpenGL Shader Language. The ALNN based on GPU algorithm can be used in the field of national defense and other similar fields, which require frequent and quick application of additional learning in real-time without affecting the existing learning data.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.6
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• pp.2168-2187
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• 2021

The smart grid replaces the traditional power structure with information inventiveness that contributes to a new physical structure. In such a field, malicious information injection can potentially lead to extreme results. Incorrect, FDI attacks will never be identified by typical residual techniques for false data identification. Most of the work on the detection of FDI attacks is based on the linearized power system model DC and does not detect attacks from the AC model. Also, the overwhelming majority of current FDIA recognition approaches focus on FDIA, whilst significant injection location data cannot be achieved. Building on the continuous developments in deep learning, we propose a Deep Learning based Locational Detection technique to continuously recognize the specific areas of FDIA. In the development area solver gap happiness is a False Data Detector (FDD) that incorporates a Convolutional Neural Network (CNN). The FDD is established enough to catch the fake information. As a multi-label classifier, the following CNN is utilized to evaluate the irregularity and cooccurrence dependency of power flow calculations due to the possible attacks. There are no earlier statistical assumptions in the architecture proposed, as they are "model-free." It is also "cost-accommodating" since it does not alter the current FDD framework and it is only several microseconds on a household computer during the identification procedure. We have shown that ANN-MLP, SVM-RBF, and CNN can conduct locational detection under different noise and attack circumstances through broad experience in IEEE 14, 30, 57, and 118 bus systems. Moreover, the multi-name classification method used successfully improves the precision of the present identification.

• International Journal of Computer Science & Network Security
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• v.24 no.7
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• pp.108-117
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• 2024

The selection and recommendation of a suitable job applicant from the pool of thousands of applications are often daunting jobs for an employer. The recommendation and selection process significantly increases the workload of the concerned department of an employer. Thus, Resume Classification System using the Natural Language Processing (NLP) and Machine Learning (ML) techniques could automate this tedious process and ease the job of an employer. Moreover, the automation of this process can significantly expedite and transparent the applicants' selection process with mere human involvement. Nevertheless, various Machine Learning approaches have been proposed to develop Resume Classification Systems. However, this study presents an automated NLP and ML-based system that classifies the Resumes according to job categories with performance guarantees. This study employs various ML algorithms and NLP techniques to measure the accuracy of Resume Classification Systems and proposes a solution with better accuracy and reliability in different settings. To demonstrate the significance of NLP & ML techniques for processing & classification of Resumes, the extracted features were tested on nine machine learning models Support Vector Machine - SVM (Linear, SGD, SVC & NuSVC), Naïve Bayes (Bernoulli, Multinomial & Gaussian), K-Nearest Neighbor (KNN) and Logistic Regression (LR). The Term-Frequency Inverse Document (TF-IDF) feature representation scheme proven suitable for Resume Classification Task. The developed models were evaluated using F-Score_M, Recall_M, Precission_M, and overall Accuracy. The experimental results indicate that using the One-Vs-Rest-Classification strategy for this multi-class Resume Classification task, the SVM class of Machine Learning algorithms performed better on the study dataset with over 96% overall accuracy. The promising results suggest that NLP & ML techniques employed in this study could be used for the Resume Classification task.

• 대한공업교육학회지
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• v.37 no.2
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• pp.147-160
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• 2012

The purpose of this study was to examine the effects on the academic achievement and learning satisfaction according to the presentation method of the multimedia materials for 'transportation technology' units of technology home economics subject. The subjects were assigned in third conditions; Text type explanation class, multimedia class and multimedia video class with narration. The data of six evaluation questions obtained from the survey of 93 high school girl were analyzed using SPSS program. The results of the study were as follows : First, in the learning satisfaction average level(M) of the students' overall responses to the questions, multimedia teaching learning class(experimental group 1) is the first(M=4.14), multimedia video class with narration(experimental group 2) is the second(M=3.16), and instructor-led class(control group) is the third (M=2.63). Therefore, the teaching learning multimedia class(experimental group 1) was most effective. Second, looking at the correlations between the students' responses to the questions, in an interesting class, the students have a retentive memory and comprehension, but a lower concentration can not a retentive memory. Third, multimedia teaching learning class(experimental group 1) has the best degree at the level of academic achievement, but instructor-led class(control group) and multimedia video class with narration(experimental group 2) have similar degree in the second place. To increase academic achievement, an instructor-led class is important to arouse interest and a multimedia video class with narration is required ways to improve level of concentration.