• Journal of the Semiconductor & Display Technology
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• v.22 no.3
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• pp.73-77
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• 2023

As the DeFi market continues to expand, fraudulent activities using smart contracts have also increased. HoneyPot and Ponzi schemes are well-known frauds that exploit smart contracts. While several studies have demonstrated the potential to detect smart contracts implementing these scams, there has been a lack of research focusing on simultaneously detecting both types of fraud. This paper addresses this gap by harnessing artificial intelligence to conduct experiments for the detection of both HoneyPot and Ponzi schemes. The study employs the CNN (Convolutional Neural Network) model, commonly used for malware detection. To effectively utilize CNN, the bytecode of smart contracts is transformed into visual representations. The experimental results showcase a recall rate of 0.89 and an F1 score of 0.85, indicating promising detection capabilities.

• Journal of Drive and Control
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• v.20 no.4
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• pp.54-63
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• 2023

High and intermittent loading cycles induce fatigue damage to transmission components, resulting in premature gearbox failure. To identify gearbox defects, numerous vibration-based diagnostics techniques, using several artificial intelligence (AI) algorithms, have recently been presented. In this paper, an optimized deep belief network (DBN) model for gearbox problem diagnosis was designed based on time-frequency visual pattern identification. To optimize the hyperparameters of the model, a particle swarm optimization (PSO) approach was integrated into the DBN. The proposed model was tested on two gearbox datasets: a wind turbine gearbox and an experimental gearbox. The optimized DBN model demonstrated strong and robust performance in classification accuracy. In addition, the accuracy of the generated datasets was compared using traditional ML and DL algorithms. Furthermore, the proposed model was evaluated on different partitions of the dataset. The results showed that, even with a small amount of sample data, the optimized DBN model achieved high accuracy in diagnosis.

• The Journal of Korea Robotics Society
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• v.19 no.2
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• pp.196-202
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• 2024

The ability to understand given environments and plan a sequence of actions leading to goal state is crucial for personal service robots. With recent advancements in deep learning, numerous studies have proposed methods for state representation in planning. However, previous works lack explicit information about relationships between objects when the state observation is converted to a single visual embedding containing all state information. In this paper, we introduce graph-based state representation that incorporates both object and relationship features. To leverage these advantages in addressing the task planning problem, we propose a Graph Neural Network (GNN)-based subgoal prediction model. This model can extract rich information about object and their interconnected relationships from given state graph. Moreover, a search-based algorithm is integrated with pre-trained subgoal prediction model and state transition module to explore diverse states and find proper sequence of subgoals. The proposed method is trained with synthetic task dataset collected in simulation environment, demonstrating a higher success rate with fewer additional searches compared to baseline methods.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• v.27 no.2
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• pp.130-138
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• 2016

Objectives: The aim of this study was to develop auditory-verbal and visual-spatial memory tasks using smart devices for children aged 8 to 10 years and examine their validity. Methods: One-hundred and fourteen school-aged children were recruited through internet advertising. We developed memory tasks assessing auditory-verbal memory, visual-spatial memory, and working memory, and then examined their construct validity by examining the developmental trend of the children's mean scores with age. In order to examine the concurrent validity of the tasks, we conducted correlation analyses between the children's scores on the newly developed auditory-verbal, visual-spatial memory and working memory tasks and their scores on well-known standardized tests of memory and working memory, including the auditory-verbal memory subtests of the Korean Luria-Nebraska Neuropsychological Battery for Children, Korean Rey-Osterrieth Complex Figure Test, digit span and arithmetic subtest of Korean Educational Development Institute Wechsler Intelligence Scale for Children Revised, and Corsi block test. Results: The memory and working memory scores measured by the newly developed tasks tended to increase with age. Further, there were significant correlations between the scores of the four cognitive tasks and the corresponding scores of the standardized assessment tools. Conclusion: This study revealed promising evidence for the validity of the memory tasks using smart devices, suggesting their utility for school-aged children in research and clinical settings.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• v.8 no.1
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• pp.101-112
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• 1997

The purpose of this study was to examine usefulness of KEDI-Individual Basic Learning Skills Test as a diagnostic tool of learning disorders(LD). Learning disorder group consisted of two subgroups, verbal learning disorder group(VLD, n＝34) and nonverbal learning disorder group(NVLD, n＝14). Comparison group consisted of Dysthymia Disorder subgroup(n＝11) and Normal subgroup(n＝20). Performance of intelligence test and achievement test was examined in all 4 subgroups. In KEDI-WISC, VLD subgroup revealed primary problems in vocabulary, information and verbal-auditory attention test. NVLD group revealed primary problems in almost all performance tests such as visual acuity, psycho-motor coordination speed and visual-spatial organizations ability subtest. In KEDI-Individual Basic Learning Test, VLD group revealed primary problems in phonological coding process, word recognition and mathematics. For successful classification of LD children, the importance of achievement test and intelligence test was discussed by discriminant analysis and factor analysis. The results indicate that KEDI-Individual Basic Learning Skills is of considerable usefulness in diagnosing LD, but must be used in subtests, and additional tests must be conducted for thorough exploration of LD.

• Journal of the Korea Convergence Society
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• v.9 no.8
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• pp.127-133
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• 2018

The purpose of this study is to analyze the technology trend of artificial intelligent speaker(AI speaker) and to suggest direction of domestic AI speaker through the case study of AI speaker. As a research method, technical background was studied through literature, and then, case of AI speaker was investigated. As a result, It attempts to extend it to the visual interface. One of these attempts is attention to the built-in screen AI speaker. AI speakers should be a platform for humans and computers to interact with, not just convenience facilities. Based on the implications presented in this study, we hope to be able to use it as a reference for predicting the service development direction of domestic artificial intelligent speakers in the future.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.43 no.9 s.351
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• pp.135-141
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• 2006

In order to prevent traffic accident, it is very important that the driver regulates the location of rear view mirror using the automatic seat regulation system which guarantees the maximum vision of the possibility for accuracy. In order to solve this problem the paper deals with the automatic seat control system which guarantees comfortable and safe seating and good visual field. Also a automatic car seat control algorithm has been developed to regulate the back mirror. Particularly, the automatic seat control algorithm function for the air bag operation in case of an accident has been added depending on passengers weight. Moreover when the driver passes a dangerous area an algorithm has been developed which gives the driver a naming sign and has been simulated in a ubiquitous environment. The simulation result proved that the Intelligence analysis for traffic accidents can reduce franc accidents more than 25% than the currently existing methods.

• Science of Emotion and Sensibility
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• v.23 no.1
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• pp.69-78
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• 2020

This study compares and analyzes user experiences of intelligent personal assistant services based on the evaluation criteria of human-computer interaction to explore positive elements of user experiences and factors that could be improved. The research was conducted on Apple's Siri, Google's Google Associate, and Samsung's Bixby, which is presently the smartest personal assistant service on the market. The research method was to compare and analyze the concepts and characteristics of the current services through a literature review and by interviewing seven UI/UX design experts for the second 2 weeks using contextual inquiry. The interview reorganized Peter Morville's user experience honeycomb, reducing his seven usability principles down to five, asking questions about usability, convenience, visual attractiveness, reliability, and satisfaction. On the basis of the reconfigured usability principle, the assessment was conducted on the basis of the assessment timing and the system usability scale. This study is meaningful in that it analyzed the user experience of artificial intelligence personal assistant services both quantitatively and qualitatively.

• Journal of KIISE
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• v.45 no.1
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• pp.30-35
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• 2018

GuessWhat?! is a game in which two machine players, composed of questioner and answerer, ask and answer yes-no-N/A questions about the object hidden for the answerer in the image, and the questioner chooses the correct object. GuessWhat?! has received much attention in the field of deep learning and artificial intelligence as a testbed for cutting-edge research on the interplay of computer vision and dialogue systems. In this study, we discuss the objective function and characteristics of the GuessWhat?! game. In addition, we propose a simple solver for GuessWhat?! using a simple rule-based algorithm. Although a human needs four or five questions on average to solve this problem, the proposed method outperforms state-of-the-art deep learning methods using only two questions, and exceeds human performance using five questions.

• Journal of the Korea Society of Computer and Information
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• v.25 no.5
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• pp.11-18
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• 2020

This study proposes a technique for the determination of outlet blockage using deep learning in a conveyor system. The proposed method aims to apply the best model to the actual process, where we train various CNN models for the determination of outlet blockage using images collected by CCTV in an industrial scene. We used the well-known CNN model such as VGGNet, ResNet, DenseNet and NASNet, and used 18,000 images collected by CCTV for model training and performance evaluation. As a experiment result with various models, VGGNet showed the best performance with 99.03% accuracy and 29.05ms processing time, and we confirmed that VGGNet is suitable for the determination of outlet blockage.