• Journal of the Korea Institute of Information Security & Cryptology
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• v.30 no.6
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• pp.1053-1065
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• 2020

With the recent development of hardware performance and artificial intelligence technology, sophisticated fake videos that are difficult to distinguish with the human's eye are increasing. Face synthesis technology using artificial intelligence is called Deepfake, and anyone with a little programming skill and deep learning knowledge can produce sophisticated fake videos using Deepfake. A number of indiscriminate fake videos has been increased significantly, which may lead to problems such as privacy violations, fake news and fraud. Therefore, it is necessary to detect fake video clips that cannot be discriminated by a human eyes. Thus, in this paper, we propose a deep-fake detection model applied with Bidirectional Convolution LSTM and Attention Module. Unlike LSTM, which considers only the forward sequential procedure, the model proposed in this paper uses the reverse order procedure. The Attention Module is used with a Convolutional neural network model to use the characteristics of each frame for extraction. Experiments have shown that the model proposed has 93.5% accuracy and AUC is up to 50% higher than the results of pre-existing studies.

• The Plant Pathology Journal
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• v.37 no.6
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• pp.521-532
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• 2021

Knowledge and better understanding of functions of the microbial community are pivotal for crop management. This study was conducted to study bacterial structures including Acidovorax species community structures and diversity from the watermelon cultivated soils in different regions of South Korea. In this study, soil samples were collected from watermelon cultivation areas from various places of South Korea and microbiome analysis was performed to analyze bacterial communities including Acidovorax species community. Next generation sequencing (NGS) was performed by extracting genomic DNA from 92 soil samples from 8 different provinces using a fast genomic DNA extraction kit. NGS data analysis results revealed that, total, 39,367 operational taxonomic unit (OTU), were obtained. NGS data results revealed that, most dominant phylum in all the soil samples was Proteobacteria (37.3%). In addition, most abundant genus was Acidobacterium (1.8%) in all the samples. In order to analyze species diversity among the collected soil samples, OTUs, community diversity, and Shannon index were measured. Shannon (9.297) and inverse Simpson (0.996) were found to have the highest diversity scores in the greenhouse soil sample of Gyeonggi-do province (GG4). Results from NGS sequencing suggest that, most of the soil samples consists of similar trend of bacterial community and diversity. Environmental factors play a key role in shaping the bacterial community and diversity. In order to address this statement, further correlation analysis between soil physical and chemical parameters with dominant bacterial community will be carried out to observe their interactions.

• Journal of Space Technology and Applications
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• v.1 no.3
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• pp.356-363
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• 2021

It was common knowledge in textbooks that images acquired using mid-infrared ray were not suitable for measuring temperature near room temperature. But a recent satellite image using a mid-infrared sensor show the possibility that the result measured using the mid-infrared sensor can also measure the temperature near room temperature. In this paper, the possibility and accuracy of extraction room temperature information from satellite images with mid-infrared sensors are reviewed. The mid-infrared satellite image reviewed in this paper showed the temperature of room temperature well, and regarding the reliability as an absolute value of the measured temperature, the effect of the heat transfer amount due to the direct reflection of sunlight on the surface and the effect of the infrared absorption amount absorbed in the atmosphere can be seen as a relatively small or constant value. However, the problem of uncertainty in the radiation coefficient due to physical properties, which is the limit of the non-contact thermometer, remained a problem to be solved.

• Imaging Science in Dentistry
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• v.52 no.3
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• pp.239-244
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• 2022

Purpose: Despite the proliferation of numerous morphometric and anthropometric methods for sex identification based on linear, angular, and regional measurements of various parts of the body, these methods are subject to error due to the observer's knowledge and expertise. This study aimed to explore the possibility of automated sex determination using convolutional neural networks(CNNs) based on lateral cephalometric radiographs. Materials and Methods: Lateral cephalometric radiographs of 1,476 Iranian subjects (794 women and 682 men) from 18 to 49 years of age were included. Lateral cephalometric radiographs were considered as a network input and output layer including 2 classes(male and female). Eighty percent of the data was used as a training set and the rest as a test set. Hyperparameter tuning of each network was done after preprocessing and data augmentation steps. The predictive performance of different architectures (DenseNet, ResNet, and VGG) was evaluated based on their accuracy in test sets. Results: The CNN based on the DenseNet121 architecture, with an overall accuracy of 90%, had the best predictive power in sex determination. The prediction accuracy of this model was almost equal for men and women. Furthermore, with all architectures, the use of transfer learning improved predictive performance. Conclusion: The results confirmed that a CNN could predict a person's sex with high accuracy. This prediction was independent of human bias because feature extraction was done automatically. However, for more accurate sex determination on a wider scale, further studies with larger sample sizes are desirable.

• International Journal of Computer Science & Network Security
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• v.23 no.1
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• pp.147-152
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• 2023

Mostly in motor fault detection the instantaneous values 3 axis vibration and 3phase current in time domain are acquired and converted to frequency domain. Vibrations are more useful in diagnosing the mechanical faults and motor current has remained more useful in electrical fault diagnosis. With having some experience and knowledge on the behavior of acquired data the electrical and mechanical faults are diagnosed through signal processing techniques or combine machine learning and signal processing techniques. In this paper, a single-layer LSTM based condition monitoring system is proposed in which the instantaneous values of three phased motor current are firstly acquired in simulated motor in in health and supply imbalance conditions in each of three stator currents. The acquired three phase current in time domain is then used to train a LSTM network, which can identify the type of fault in electrical supply of motor and phase in which the fault has occurred. Experimental results shows that the proposed single layer LSTM algorithm can identify the electrical supply faults and phase of fault with an average accuracy of 88% based on the three phase stator current as raw data without any processing or feature extraction.

• Journal of the Korea Society of Computer and Information
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• v.29 no.1
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• pp.11-19
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• 2024

Multi-agent systems can be utilized in various real-world cooperative environments such as battlefield engagements and unmanned transport vehicles. In the context of battlefield engagements, where dense reward design faces challenges due to limited domain knowledge, it is crucial to consider situations that are learned through explicit sparse rewards. This paper explores the collaborative potential among allied agents in a battlefield scenario. Utilizing the Multi-Robot Warehouse Environment(RWARE) as a sparse reward environment, we define analogous problems and establish evaluation criteria. Constructing a learning environment with the QMIX algorithm from the reinforcement learning library Ray RLlib, we enhance the Agent Network of QMIX and integrate Random Network Distillation(RND). This enables the extraction of patterns and temporal features from partial observations of agents, confirming the potential for improving the acquisition of sparse reward experiences through intrinsic rewards.

• The Transactions of the Korea Information Processing Society
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• v.2 no.6
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• pp.847-856
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• 1995

When a human recognizes nouns in a sentence, s/he associates them with the hyper concepts of onus. For computer to simulate the human's word recognition, it should build the knowledge base (WordNet)for the hyper concepts of words. Until now, works for the WordNet haven't been performed in Korea, because they need lots of human efforts and time. But, as the power of computer is radically improved and common MRD becomes available, it is more feasible to automatically construct the WordNet. This paper proposes the method that automatically builds the WordNet of Korean nouns by using the descripti on of onus in Korean MRD, and it proposes the rules for extracting the hyper concepts (hypernyms)by analyzing structrual characteristics of Korean. The rules effect such characteristics as a headword lies on the rear part of sentences and the descriptive sentences of nouns have special structure. In addition, the WordNet prototype of Korean Nouns is developed, which is made by combining the hypernyms produced by the rules mentioned above. It extracts the hypernyms of about 2,500 sample words, and the result shows that about 92per cents of hypernyms are correct.

• Korean Chemical Engineering Research
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• v.51 no.2
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• pp.221-225
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• 2013

Sustainability, in general, means the protection of environmental resources and economic prosperity, with the consideration of the social, economic and environmental effect, as well as human health and the enhancement of life. Profound consideration about sustainability has to handle the overall cycle of feedstock, resource extraction, transportation and production in addition to the environmental effect. Sustainable development of the chemical industries should be carried out complementarily by strengthening the chemical process safety of the industries. In this respect, chemical process safety can be called an opportunity to enhance the compatibility internationally. Changing new paradigm in chemical process safety is formed from the overall life cycle considering basic design of existing systems and production processes. To improve the chemical process safety, the integrated smart system is necessary, comprising various chemical safety database and knowledge base and improved methods of quantitative risk analysis, including management system. This paper discussed the necessity of overall life cycle in chemical process safety and proposed new technology to improve the sustainability. To develop the sustainable industries in process systems engineering, three S, which include Safety, Stability and Security, will have to be combined appropriate.

• Journal of The Korean Association of Information Education
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• v.24 no.1
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• pp.49-57
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• 2020

The society we are living in has being changed to the age of the intelligent information society after passing through the knowledge-based information society in the early 21st century. In this study, we have developed the instructional model for software education based on the machine learning which is a field of artificial intelligence(AI) to enhance the core competencies of learners required in the intelligent information society. This model is focusing on enhancing the core competencies through the process of problem-solving as well as reducing the burden of learning about AI itself. The specific stages of the developed model are consisted of seven levels which are 'Problem Recognition and Analysis', 'Data Collection', 'Data Processing and Feature Extraction', 'ML Model Training and Evaluation', 'ML Programming', 'Application and Problem Solving', and 'Share and Feedback'. As a result of applying the developed model in this study, we were able to observe the positive response about learning from the students and parents. We hope that this research could suggest the future direction of not only the instructional design but also operation of software education program based on machine learning.

• Proceedings of the International Microelectronics And Packaging Society Conference
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• 2001.11a
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• pp.37-39
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• 2001

21세기의 벽두부터 국내외적으로 활발히 논의되고 있는 나노기술에 대한 정의를 생각해보는 것으로부터 우리가 나아갈 방향을 살펴보고자 한다. 나노기술이란, 원자 하나 하나 혹은 분자단위의 조작을 통해 1~100nm정도의 범위 안에서 근본적으로 새로운 물질이나 구조체를 만들어 내는 기술을 말한다. 즉 앞으로 우리는 경험해 보지 못한 새로운 현상에 대한 이해를 할 수 있어야 하고, 새로운 물질 자체를 다룰 수 있는 방법이 우리가 해야 할 구체적인 일이 될 것이란 말이 된다. 뿐만 아니라 나노기술은 종래의 정보.통신.전자 분야에서 주로 추구하던 마이크로화와 달리 재료, 기계, 전자, 의학, 약학, 에너지, 환경, 화학, 생물학, 농학, 정보, 보안기술 등 과학기술 분야 전반을 위시하여 사회분야가지 새로운 인식과 철학적인 이해가 필요하게 되었다. 21세기를 맞은 인류가 나아갈 방향을 나노세계에 대한 도전으로 보아야 하며, 과학기술의 새로운 틀을 제공할 것 임에 틀림 없다. 그러나, 이와 같은 나노기술의 출발점을 살펴보면 VLSI기술로 통칭할 수 있는 마이크로전자소자 기술이란 점이다. 국내의 VLSI기술은 메모리기술이라고 해도 과언이 아닐 것이다. 문제는 종래의 메모리기술은 대규모 투자와 집중적인 인력양성을 통해서 세계 최고 수준에 도달 할 수 있었다. 그러나 여기까지 오는 동안 사식 우리는 선진국의 뒷꽁무니를 혼신의 힘을 다해 뒤쫓아 온 결과라고 보아도 틀리지 않는다. 즉, 앞선자를 보고 뒤쫓는 사람은 갈방향과 목표가 분명하므로 최선을 다하면 따라 잡을 수 있다. 그런데 나노기술은 앞선 사람이 없다는 점이 큰 차이이다 따라서 뒷껑무니를 쫓아가는 습성을 가지고는 개척해 나갈 수 없다는 점을 깨닫지 않으면 안된다. 그런 점에서 이 시간 나노기술의 국내외 현황을 살펴보고 우리가 어떻게 할 것인가를 생각해 보는데 의미가 있을 것이다.하여 분석한 결과 기존의 제한된 RICH-DP는 실시간 서비스에 대한 처리율이 낮아지며 서비스 시간이 보장되지 못했다. 따라서 실시간 서비스에 대한 새로운 제안된 기법을 제안하고 성능 평가한 결과 기존의 RICH-DP보다 성능이 향상됨을 확인 할 수 있었다.(actual world)에서 가상 관성 세계(possible inertia would)로 변화시켜서, 완수동사의 종결점(ending point)을 현실세계에서 가상의 미래 세계로 움직이는 역할을 한다. 결과적으로, IMP는 완수동사의 닫힌 완료 관점을 현실세계에서는 열린 미완료 관점으로 변환시키되, 가상 관성 세계에서는 그대로 닫힌 관점으로 유지 시키는 효과를 가진다. 한국어와 영어의 관점 변환 구문의 차이는 각 언어의 지속부사구의 어휘 목록의 전제(presupposition)의 차이로 설명된다. 본 논문은 영어의 지속부사구는 논항의 하위간격This paper will describe the application based on this approach developed by the authors in the FLEX EXPRIT IV n$^{\circ}$EP29158 in the Work-package "Knowledge Extraction & Data mining"where the information captured from digital newspapers is extracted and reused in tourist information context.terpolation performance of CNN was relatively better than NN.콩과 자연 콩이 성분 분석에서 차이를 나타내지 않았다는 점, 네 번째. 쥐를 통한 다양섭취 실험에서 아무런 이상 반응이 없었다는 점등의 결과를 기준으로 알레르기에 대한 개별 검사 없이 안전한