• Industrial Engineering and Management Systems
• /
• 제8권3호
• /
• pp.171-180
• /
• 2009

Population learning algorithm (PLA) is a population-based method that was inspired by the similarities to the phenomenon of social education process in which a diminishing number of individuals enter an increasing number of learning stages. The study aims to develop a framework that repeatedly applying the PLA to solve the discrete resource constrained project scheduling problem with two objectives: minimizing project makespan and renewable resource availability, which are two most common concerns of management when a project is being executed. The PLA looping framework will provide a number of near Pareto optimal schedules for the management to make a choice. Different improvement schemes and learning procedures are applied at different stages of the process. The process gradually becomes more and more sophisticated and time consuming as there are less and less individuals to be taught. An experiment with ProGen generated instances was conducted, and the results demonstrated that the looping framework using PLA outperforms those using genetic local search, particle swarm optimization with local search, scatter search, as well as biased sampling multi-pass algorithm, in terms of several performance measures of proximity. However, the diversity using spread metric does not reveal any significant difference between these five looping algorithms.

• 교육시설 논문지
• /
• 제21권6호
• /
• pp.33-42
• /
• 2014

The objective of this study is to investigate learning factors (stability, attention and activation) in school by electroencephalogram (theta, alpha and beta waves) analysis during relaxed or concentrated. In order to measure electroencephalograms, MP 150 by Biopac and ECI Electro-Cap are employed. Three types of color temperatures (3000K, 5000K, 7000K) are used and 13 undergraduate and 12 graduate students are selected as experimental subjects. When subjects are relaxed during contemplation or concentrated during mental arithmetic, we compare with stability, attention and activation indices. The test results show that subjects were stable when color temperature is 5000K. Subjects gave best attention when color temperature is 7000K. Subjects activated well when color temperature is 3000K during relaxed attention. However, subjects activated rigorous when color temperature is 7000K during constrained attention.

• 한국음향학회지
• /
• 제21권6호
• /
• pp.522-530
• /
• 2002

본 논문에서는 선형 예측 후에 얻어지는 잔차 신호 (residual signal)를 신경 회로망에 바탕을 둔 비선형 예측기로 예측하는 방법을 제안하였다. 신경 회로망을 이용한 예측 방법의 타당성을 입증하기 위해, 먼저 선형 장구간 예측기와 신경 회로망이 도입된 비선형 장구간 예측기의 성능을 서로 비교하였다. 그리고 비선형 예측 후의 잔차 신호를 양자화 하는 과정에서 발생하는 양자화 오차의 영향에 대해 분석하였다. 제안된 신경망 예측기는 예측 오차뿐만 아니라 양자화의 영향을 함께 고려하였으며, 양자화오차에 대한강인성을 갖게 하기 위하여 쿤-터커 (Kuhn-Tucker) 부등식 조건을 만족하는 제한조건 역전파 알고리즘을 새로이 제안하였다. 실험 결과, 제안된 신경망 예측기는 제한조건을 갖는 학습 알고리즘을 사용했음에도 불구하고, 예측 이득이 크게 뒤떨어지지 않는 성능을 나타내었다.

• 한국정보통신학회논문지
• /
• 제17권1호
• /
• pp.32-40
• /
• 2013

본 논문에서는 빔포밍과 입사각분석 기반 마스킹을 이용한 다채널 음성개선 알고리즘이 제안된다. 제안된 알고리즘에서는 LCMV 빔포밍을 수행한 후에 입사각 분석을 이용한 멜-주파수 위너필터가 적용되어 잔존하는 잡음을 제거한다. 성능 향상을 위해서 빔포밍의 적응 필터 학습률과 목표 음성 스펙트럼 검출을 위한 입사각 임계치가 최적화된다. 성능 지수로서 PESQ와 출력 SNR이 측정되었으며 실험 결과 제안한 알고리즘이 종전의 최소분산 빔포밍 기법보다 PESQ 관점에서 0.09, 출력 SNR 관점에서 5.75 dB의 성능 향상시킴을 알 수 있었다.

• 제어로봇시스템학회:학술대회논문집
• /
• 제어로봇시스템학회 2003년도 ICCAS
• /
• pp.316-321
• /
• 2003

This paper presents a design method of the wavelet neural network based controller using direct adaptive control method to deal with a stable intelligent control of chaotic systems. The various uncertainties, such as mechanical parametric variation, external disturbance, and unstructured uncertainty influence the control performance. However, the conventional control methods such as optimal control, adaptive control and robust control may not be feasible when an explicit, faithful mathematical model cannot be constructed. Therefore, an intelligent control system that is an on-line trained WNN controller based on direct adaptive control method with adaptive learning rates is proposed to control chaotic nonlinear systems whose mathematical models are not available. The adaptive learning rates are derived in the sense of discrete-type Lyapunov stability theorem, so that the convergence of the tracking error can be guaranteed in the closed-loop system. In the whole design process, the strict constrained conditions and prior knowledge of the controlled plant are not necessary due to the powerful learning ability of the proposed intelligent control system. The gradient-descent method is used for training a wavelet neural network controller of chaotic systems. Finally, the effectiveness and feasibility of the proposed control method is demonstrated with application to the chaotic systems.

• 한국임상약학회지
• /
• 제26권4호
• /
• pp.283-290
• /
• 2016

Experiential education is a core curriculum of 6-year pharmacy education. Practicing pharmacists lie at the heart of experiential education serving as preceptors for undergraduate pharmacy students during experiential education. Preceptors are, however, confronted with a challenge of caring for patients and teaching students at the same time in a time-constrained environment. To improve the effectiveness and outcomes of experiential education, practicing pharmacists are required to demonstrate educational competence. Even small teaching moments can provide students with valuable learning opportunities that they could not have from on their own. Thus, it is vital to provide education and training for preceptors to advance their teaching skills. This article will describe practical and effective teaching skills that preceptors could adopt in the experiential education for pharmacy students. It is important that preceptors should use different teaching skills for different learners, according to their level of experience and knowledge, learning styles and needs, as well as the type of the practice. Therefore, possessing diverse teaching skills provides flexibility to adapt teaching to each student's learning levels and needs, and to the charateristics of the practice environment. Preceptors' level of confidence and comfort in using teaching skills can be enhanced through continuous practice and training, which consequently leads to the improved effectiveness of experiential education and student's satisfaction with the education.

• 방송공학회논문지
• /
• 제26권7호
• /
• pp.855-861
• /
• 2021

Neural Architecture Search (NAS) is cutting-edge technology in the machine learning community. NAS Without Training (NASWOT) recently has been proposed to tackle the high demand of computational resources in NAS by leveraging some indicators to predict the performance of architectures before training. The advantage of these indicators is that they do not require any training. Thus, NASWOT reduces the searching time and computational cost significantly. However, NASWOT only considers high-performing networks which does not guarantee a fast inference speed on hardware devices. In this paper, we propose a multi objectives reward function, which considers the network's latency and the predicted performance, and incorporate it into the Reinforcement Learning approach to search for the best networks with low latency. Unlike other methods, which use FLOPs to measure the latency that does not reflect the actual latency, we obtain the network's latency from the hardware NAS bench. We conduct extensive experiments on NAS-Bench-201 using CIFAR-10, CIFAR-100, and ImageNet-16-120 datasets, and show that the proposed method is capable of generating the best network under latency constrained without training subnetworks.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제16권8호
• /
• pp.2801-2815
• /
• 2022

With the advent and realization of Software Defined Network (SDN) architecture, many organizations are now shifting towards this paradigm. SDN brings more control, higher scalability, and serene elasticity. The SDN spontaneously changes the network configuration according to the dynamic network requirements inside the constrained environments. Therefore, a monitoring system that can monitor the physical and virtual entities is needed to operate this type of network technology with high efficiency and proficiency. In this manuscript, we propose a real-time monitoring system for data collection and visualization that includes the Prometheus, node exporter, and Grafana. A node exporter is configured on the physical devices to collect the physical and virtual entities resources utilization logs. A real-time Prometheus database is configured to collect and store the data from all the exporters. Furthermore, the Grafana is affixed with Prometheus to visualize the current network status and device provisioning. A monitoring system is deployed on the physical infrastructure of the KOREN topology. Data collected by the monitoring system is further pre-processed and restructured into a dataset. A monitoring system is further enhanced by including machine learning techniques applied on the formatted datasets to identify the elephant flows. Additionally, a Random Forest is trained on our generated labeled datasets, and the classification models' performance are verified using accuracy metrics.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제18권5호
• /
• pp.1223-1237
• /
• 2024

Autonomous vehicles use onboard sensors to sense the surrounding environment. In complex autonomous driving scenarios, the detection and recognition capabilities are constrained, which may result in serious accidents. An efficient way to enhance the detection and recognition capabilities is establishing collaborations with the neighbor vehicles. However, the collaborations introduce additional challenges in terms of the data heterogeneity, communication cost, and data privacy. In this paper, a novel personalized federated learning framework is proposed for addressing the challenges and enabling efficient collaborations in autonomous driving environment. For obtaining a global model, vehicles perform local training and transmit logits to a central unit instead of the entire model, and thus the communication cost is minimized, and the data privacy is protected. Then, the inference similarity is derived for capturing the characteristics of data heterogeneity. The vehicles are divided into clusters based on the inference similarity and a weighted aggregation is performed within a cluster. Finally, the vehicles download the corresponding aggregated global model and train a personalized model which is personalized for the cluster that has similar data distribution, so that accuracy is not affected by heterogeneous data. Experimental results demonstrate significant advantages of our proposed method in improving the efficiency of collaborative perception and reducing communication cost.

• 한국게임학회 논문지
• /
• 제10권2호
• /
• pp.113-121
• /
• 2010

본 연구에서는 기존 교육용 시스템의 단방향성, 학습자 수준의 고려 부족 등의 단점에 대한 대안으로 휴대 인터넷 환경에 적합한 교육용 게임 시스템을 제안하였다. 기존 이러닝 콘텐츠와 플랫폼에 대해 분석해 보았으며 재사용성과 학습자 맞춤형 서비스에 부합되는 형태로 시스템을 설계하였다. 애니메이션 및 게임으로 구성되는 학습 콘텐츠는 학습자의 수준에 맞추어 가변적으로 제공되도록 할 수 있으며 학습자는 정해진 패턴이 아닌 변형된 요소들로 구성된 게임을 사용하여 교육효과가 증대될 수 있다. 본 시스템의 효용성을 보이기 위해, 영어 발음 학습법인 파닉스 교육법을 적용한 교육 게임을 예제로 제작하여 소개하였다. 그 결과 기존 이러닝의 단점을 보안할 수 있었으며 휴대 인터넷 환경의 장점인 쌍방향성을 활용하여 더욱 효과적인 교육용 게임 시스템을 구축할 수 있었다.