• Proceedings of the KOSOMBE Conference
• /
• v.1997 no.11
• /
• pp.99-102
• /
• 1997

A neural spike sorting technique has been developed that also has the capability of template learning. A system of software has been written that first obtains the templates by learning, and then performs the sorting of the spikes into single units. The spike sorting can be done in real time. The template learning consists of spike detection based on the discrete Haar transform (DHT), feature extraction by clustering of spike amplitude and duration, classification based on rms error, and fabrication of templates. The developed algorithms can be implemented into real time systems using digital signal processors.

• Proceedings of the IEEK Conference
• /
• 2006.06a
• /
• pp.935-936
• /
• 2006

In this paper, we have applied a real-time face processor includes detection, recognition, and learning to a intelligent store management service robot. We use the Haar classifier and adaboost learning algorithm for face detection. For face recognition and learning, a PCA algorithm and a SVDD algorithm is used. We have developed a store management service robot and applied these algorithms to verify the performance.

• Journal of the Semiconductor & Display Technology
• /
• v.19 no.2
• /
• pp.88-91
• /
• 2020

Image segmentation is the task of partitioning an image into multiple sets of pixels based on some characteristics. The objective is to simplify the image into a representation that is more meaningful and easier to analyze. In this paper, we apply deep-learning to pre-train the learning model, and implement an algorithm that performs image segmentation in real time by extracting frames for the stream input from the Android device. Based on the open source of DeepLab-v3+ implemented in Tensorflow, some convolution filters are modified to improve real-time operation on the Android platform.

• Proceedings of the Korean Society of Computer Information Conference
• /
• 2012.07a
• /
• pp.457-458
• /
• 2012

본 논문에서는 실시간 원격 수업(Real-Time Distance Education) 환경 하에서 학생들의 수업 집중 및 효율적인 교수를 위한 버즈학습(Buzz Learning)의 필요성을 제안한다. 이 학습법은 실시간 원격 수업에서 일어날 수 있는 집중력 저하 및 수업참가율 저조를 최소화하고, 학습자 간의 협동 및 상호작용을 향상시킨다. 또한 버즈법에 의한 그룹 편성 및 학습형태는 각 그룹에서 토의한 결과를 다시 전체가 모여 토의함으로써 소집단의 토의결과를 종합 정리하고 결론을 도출해 내는 집단 토의 형태를 띤다. 토의학습은 흔히 몇몇 학생의 경우 토의에 참가하지 않거나 또는 한 명이 독무대화하는 경향이 있는데 여기서는 그룹 전원이 토론에 적극 참여할 수 있게 한다. 본 논문에서는 실시간 원격 수업을 통하여 이뤄지는 실제 강의에 적용된 버즈 학습이 학생들의 수업참여도 및 학습 향상 면에서 우수함을 보여준다.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• v.14 no.3
• /
• pp.162-170
• /
• 2014

Appearance-based subspace models such as eigenfaces have been widely recognized as one of the most successful approaches to face recognition and tracking. The success of eigenfaces mainly has its origins in the benefits offered by principal component analysis (PCA), the representational power of the underlying generative process for high-dimensional noisy facial image data. The sparse extension of PCA (SPCA) has recently received significant attention in the research community. SPCA functions by imposing sparseness constraints on the eigenvectors, a technique that has been shown to yield more robust solutions in many applications. However, when SPCA is applied to facial images, the time and space complexity of PCA learning becomes a critical issue (e.g., real-time tracking). In this paper, we propose a very fast and scalable greedy forward selection algorithm for SPCA. Unlike a recent semidefinite program-relaxation method that suffers from complex optimization, our approach can process several thousands of data dimensions in reasonable time with little accuracy loss. The effectiveness of our proposed method was demonstrated on real-world face recognition and tracking datasets.

• Journal of the Korea Society of Computer and Information
• /
• v.24 no.3
• /
• pp.87-93
• /
• 2019

In this paper, we propose a real-time drone-based violent protest detection system. Our proposed system uses drones to detect scenes of violent protest in real-time. The important problem is that the victims and violent actions have to be manually searched in videos when the evidence has been collected. Firstly, we focused to solve the limitations of existing collecting evidence devices by using drone to collect evidence live and upload in AWS(Amazon Web Service)[1]. Secondly, we built a Deep Learning based violence detection model from the videos using Yolov3 Feature Pyramid Network for human activity recognition, in order to detect three types of violent action. The built model classifies people with possession of gun, swinging pipe, and violent activity with the accuracy of 92, 91 and 80.5% respectively. This system is expected to significantly save time and human resource of the existing collecting evidence.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.23 no.4
• /
• pp.47-52
• /
• 2023

With the growing data size and the increased computing load in machine learning, energy-efficient resource planning in IoT systems is becoming increasingly important. In this paper, we suggest a new resource planning policy for real-time workloads that can be fluctuated over time in IoT systems. To handle such situations, we categorize real-time tasks into fixed tasks and variable tasks, and optimize the resource planning for various workload conditions. Based on this, we initiate the IoT system with the configuration for the fixed tasks, and when variable tasks are activated, we update the resource planning promptly for the situation. Simulation experiments show that the proposed policy saves the processor and memory energy significantly.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.17 no.9
• /
• pp.2348-2360
• /
• 2023

As science and technology evolve, object detection of moving objects has been widely used in the context of machine learning and artificial intelligence. Traditional moving object detection algorithms, however, are characterized by relatively poor real-time performance and low accuracy in detecting moving objects. To tackle this issue, this manuscript proposes a modified Kalman filter algorithm, which aims to expand the equations of the system with the Taylor series first, ignoring the higher order terms of the second order and above, when the nonlinear system is close to the linear form, then it uses standard Kalman filter algorithms to measure the situation of the system. which can not only detect moving objects accurately but also has better real-time performance and can be employed to predict the trajectory of moving objects. Meanwhile, the accuracy and real-time performance of the algorithm were experimentally verified.

• Journal of the Korea Society of Computer and Information
• /
• v.26 no.12
• /
• pp.299-307
• /
• 2021

As the COVID-19 pandemic spread around the world, more and more universities are conducting real-time distance video lectures using ZOOM, Webex, and MS Teams. This study attempts to identify the factors influencing learner satisfaction of real-time distance video lectures. Based on the existing research, it was composed of five elements (system factor, content quality, interaction, self-direction, and learning motivation) as learner satisfaction elements of real-time distance video lectures. As a result of analyzing the structural equation model of 160 effective questionnaires by conducting a survey of college students in the metropolitan and Chungcheong areas, it was found that three factors (interaction, self-direction, and learning motivation) influence learner satisfaction. Real-time distance video lectures are expected to continue to expand in the future. Therefore, universities should continuously increase learner satisfaction through the development and evaluation of real-time distance video lecture satisfaction models.

• The Journal of the Convergence on Culture Technology
• /
• v.9 no.3
• /
• pp.321-328
• /
• 2023

This study is tried to to identify factors affecting academic achievement depending on the quality of class, learning immersion, level of academic achievement, and class type according to video production classes and real-time video classes in the on-contact learning situation due to the COVID-19 epidemic. The subjects of the study were 122 students enrolled in the nursing department at two universities. As a result of the study, the quality of the class was high in real-time video classes (t=-2.69, P=0.02), learning immersion was high in video production classes (t=1.14, P=0.28), and academic achievement was high in video production classes (t=4.24, P=0.01). Depending on the type of class, the effect on academic achievement is learning immersion in production video classes (β=.37, p<.001) has the most influence, and in real-time video classes, class quality (β=.29, p<.001) had the most influence on academic achievement. Based on the results of this study, it is suggested that it is necessary to develop a strategy for instructional design suitable for class types to improve academic achievement in an on-contact environment.