• Structural Engineering and Mechanics
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• 제62권5호
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• pp.537-550
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• 2017

Structural sizing is a rewarding task due to its non-convex constrained nature in the design space. In order to provide both global exploration and proper search refinement, a hybrid method is developed here based on outstanding features of Evolutionary Computing and Teaching-Learning-Based Optimization. The new method introduces an observer phase for memory exploitation in addition to vector-sum movements in the original teacher and learner phases. Proper integer coding is suited and applied for structural size optimization together with a fly-to-boundary technique and an elitism strategy. Performance of the proposed method is further evaluated treating a number of truss examples compared with teaching-learning-based optimization. The results show enhanced capability of the method in efficient and stable convergence toward the optimum and effective capturing of high quality solutions in discrete structural sizing problems.

• 한국통신학회논문지
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• 제30권4C호
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• pp.185-190
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• 2005

IS-95와 IMT-2000 시스템에서 사용되고 있는 여러 종류의 길쌈 부호기를 부호율 1/2, 구속장 3인 길쌈 부호기로 한정하여, 비터비 복호기에 복호길이 6인 Sliding-Window와 Neural Network의 LVQ(Learning Vector Quantization)및 PVSL(Prototype Vectors Selecting Logic)을 적용하여 순방향 실시간 복호기를 구현한다. 이론적으로 제한된 AWGN 채널환경에서의 심볼 전송전력 $S/(N_{0}/2)=1$을 성능비교 조건으로 하여 순방향 실시간 복호기와 기존의 비터비 복호기의 $강\cdot연판정$ BER 성능과 하드웨어 구성을 $비교\cdot분석$하여, 본 논문에서 제시된 순방향 실시간 복호기의 BER 성능의 우수성과 비화통신의 장점 및 하드웨어 구성의 단순합을 검증하였다.

• 전자통신동향분석
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• 제34권2호
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• pp.40-50
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• 2019

Considerable accuracy improvements in deep learning have recently been achieved in many applications that require large amounts of computation and expensive memory. However, recent advanced techniques for compacting and accelerating the deep learning model have been developed for deployment in lightweight devices with constrained resources. Lightweight deep learning techniques can be categorized into two schemes: lightweight deep learning algorithms (model simplification and efficient convolutional filters) in nature and transferring models into compact/small ones (model compression and knowledge distillation). In this report, we briefly summarize various lightweight deep learning techniques and possible research directions.

• 디지털산업정보학회논문지
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• 제19권3호
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• pp.245-251
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• 2023

Facial expression recognition plays a significant role in understanding human emotional states. With the advancement of AI and computer vision technologies, extensive research has been conducted in various fields, including improving customer service, medical diagnosis, and assessing learners' understanding in education. In this study, we develop a model that can infer emotions in real-time from a webcam using transfer learning with TensorFlow.js and MobileNet. While existing studies focus on achieving high accuracy using deep learning models, these models often require substantial resources due to their complex structure and computational demands. Consequently, there is a growing interest in developing lightweight deep learning models and transfer learning methods for restricted environments such as web browsers and edge devices. By employing MobileNet as the base model and performing transfer learning, our study develops a deep learning transfer model utilizing JavaScript-based TensorFlow.js, which can predict emotions in real-time using facial input from a webcam. This transfer model provides a foundation for implementing facial expression recognition in resource-constrained environments such as web and mobile applications, enabling its application in various industries.

• 전자통신동향분석
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• 제37권5호
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• pp.44-53
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• 2022

The Internet of Things (IoT) is growing exponentially, with the number of IoT devices multiplying annually. Accordingly, the paradigm is changing from cloud computing to edge computing and even tiny edge computing because of the low latency and cost reduction. Machine learning is also shifting its role from the cloud to edge or tiny edge according to the paradigm shift. However, the fragmented and resource-constrained features of IoT devices have limited the development of artificial intelligence applications. Edge MLaaS (Machine Learning as-a-Service) has been studied to easily and quickly adopt machine learning to products and overcome the device limitations. This paper briefly summarizes what Edge MLaaS is and what element of research it requires.

• International Journal of Computer Science & Network Security
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• 제24권4호
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• pp.170-178
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• 2024

Through the growth of the fifth-generation networks and artificial intelligence technologies, new threats and challenges have appeared to wireless communication system, especially in cybersecurity. And IoT networks are gradually attractive stages for introduction of DDoS attacks due to integral frailer security and resource-constrained nature of IoT devices. This paper emphases on detecting DDoS attack in wireless networks by categorizing inward network packets on the transport layer as either "abnormal" or "normal" using the integration of machine learning algorithms knowledge-based system. In this paper, deep learning algorithms and CNN were autonomously trained for mitigating DDoS attacks. This paper lays importance on misuse based DDOS attacks which comprise TCP SYN-Flood and ICMP flood. The researcher uses CICIDS2017 and NSL-KDD dataset in training and testing the algorithms (model) while the experimentation phase. accuracy score is used to measure the classification performance of the four algorithms. the results display that the 99.93 performance is recorded.

• 전자공학회논문지B
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• 제31B권7호
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• pp.179-189
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• 1994

A two-phase neural network finds exact feasible solutions for a constrained optimization programming problem. The time-varying programming neural network is a modified steepest-gradient algorithm which solves time-varying optimization problems. In this paper, we propose a time-varying two-phase optimization neural network which incorporates the merits of the two-phase neural network and the time-varying neural network. The proposed algorithm is applied to system identification and function approximation using a multi-layer perceptron. Particularly training of a multi-layer perceptrion is regarded as a time-varying optimization problem. Our algorithm can also be applied to the case where the weights are constrained. Simulation results prove the proposed algorithm is efficient for solving various optimization problems.

• 대한물리치료과학회지
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• 제13권3호
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• pp.77-84
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• 2006

The purpose of this study is to introduce the effect of attentional focus on performance of task. Previous studies has shown that motor learning can be enhanced by directing performers's attention to the effects of their movements(external focus), rather than to the body movement producing the effects(internal focus). Wulf and colleagues have invoked the 'constrained action hypothesis' to explain the comparative benefits of adopting an external rather than an internal focus of attention. This hypothesis proposed that when performers utilize an internal focus of attention, they may actually constrain or interfere with automatic control processes that would normally regulate the movement, whereas an external focus of attention allows the motor system to more naturally self-organize. Electromyography(EMG) was used to determine neuromuscular correlates of external versus internal focus differences. EMG activity was lower with an external relative to an internal focus. This suggest that an external focus of attention enhances movement economy, and presumably reduces 'noise' in the motor system that hampers fine movement control. Focusing on a more remote effect seems to facilitate the discriminability of the effect from the body movements that produced it and to be more beneficial than focusing on a very close effects. There might be an optimal distance of the effect, at which ti wis easily distinguishable from the body movement but at which it is also still possible for the performer to relate this effect to the movement techniques. Future Studies of motor learning of patient need to accommodate these new finding and account for the role of the learner's attentional focus and its influencing on learning.

• 한국정보과학회논문지:소프트웨어및응용
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• 제34권6호
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• pp.549-561
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• 2007

영상을 분석하여 얻은 증거를 바탕으로 장면의 의미를 추론하고 해석하는 것을 시각 기반 장면 이해라고 하며, 최근 인과적인 판단 및 추론 과정을 모델링하기에 유리한 베이지안 네트워크(BN)를 이용한 확률적인 접근 방법이 활발히 연구되고 있다. 하지만 실제 환경은 변화가 많고 불확실하기 때문에 의미 있는 증거를 충분히 확보하기 어려울 뿐만 아니라 전문가에 의한 설계로 유지하기 어렵다. 본 논문에서는 증거 및 학습 데이타가 부족한 장면인식 문제에서 효율적인BN 구조로 계산 복잡도가 줄어들고 정확도는 향상될 수 있는 BN 학습방법을 제안한다. 이 방법은 추론 대상 환경의 도메인 지식을 온톨로지로 표현하고 이를 제한적으로 사용하여 효율적인 계층구조의 BN을 구성한다. 제안하는 방법의 평가를 위하여 9종류의 환경에서 90장의 영상을 수집하고 레이블링하여 실험하였다. 실험 결과, 제안하는 방법은 증거의 수가 적은 불확실한 환경에서도 좋은 성능을 내고 학습의 복잡도가 줄어듦을 확인할 수 있었다.

• 비교문화연구
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• 제19권
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• pp.411-444
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• 2010

This paper explored the academic socialization of a group of Mongolian college students, learning Korean as their L3 (Third Language), by focusing on their uses of an electronic communication channel. From a perspective of the continua of bi-literacy, this case study investigated how Mongolian students who had limited exposure to a Korean learning community overcame academic challenges through the use of a written communication channel as a tool in the socialization process. Data were collected mainly through three methods: written products, interviews, and questionnaires. The results from this study were as follows. Interactional opportunities for these minority students were seriously constrained during the classroom practices in a Korean-speaking classroom. They also described the lack of communicative competence in Korean and the limited roles played by L2 (English) communication as key barriers to classroom practices. However, students' ways of engaging in electronic interactions differed widely in that they were able to broaden interactional circles by communicating their expertise and difficulties with their Korean peers through the electronic channel. More importantly, the communication pattern of "L2-L2/L3-L3" (on a L2-L3 continuum) emerging from data demonstrated how these students used a written channel as a socialization tool to mediate their learning process in a new community of learning. This study argues that a written communication channel should be taken as an essential part of teaching practices especially for foreign students who cannot speak Korean fluently in multi-cultural classes.