• International Journal of Advanced Culture Technology
• /
• v.9 no.4
• /
• pp.110-117
• /
• 2021

The purpose of the study is to develop effective teaching methods to strengthen the major learning capabilities of electronic engineering learners through inquiry learning using computing thinking ability. To this end, first, in the electronic engineering curriculum, we performed teaching-learning through an inquiry and learning model related to mathematics, probability, and statistics under the theme of various majors in electronic engineering, focusing on understanding computing thinking skills. Second, an efficient electronic engineering subject inquiry class operation using computing thinking ability was conducted, and electronic engineering-linked education contents based on the components of computer thinking were presented. Third, by conducting a case study on inquiry-style teaching using computing thinking skills in the electronic engineering curriculum, we identified the validity of the teaching method to strengthen major competency. In order to prepare for the 4th Industrial Revolution, by implementing mathematics, probability, statistics-related linkage, and convergence education to foster convergent talent, we tried to present effective electronic engineering major competency enhancement measures and cope with innovative technological changes.

• Journal of Communications and Networks
• /
• v.14 no.5
• /
• pp.471-480
• /
• 2012

Reliable detection of primary user activity increases the opportunity to access temporarily unused bands and prevents harmful interference to the primary system. By extracting a global decision from local sensing results, cooperative sensing achieves high reliability against multipath fading. For the effective combining of sensing results, which is generalized by a likelihood ratio test, the fusion center should learn some parameters, such as the probabilities of primary transmission, false alarm, and detection at the local sensors. During the training period in supervised learning, the on/off log of primary transmission serves as the output label of decision statistics from the local sensor. In this paper, we extend unsupervised learning techniques with an expectation maximization algorithm for cooperative spectrum sensing, which does not require an external primary transmission log. Local sensors report binary hard decisions to the fusion center and adjust their operating points to enhance learning performance. Increasing the number of sensors, the joint-expectation step makes a confident classification on the primary transmission as in the supervised learning. Thereby, the proposed scheme provides accurate parameter estimates and a fast convergence rate even in low signal-to-noise ratio regimes, where the primary signal is dominated by the noise at the local sensors.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.12 no.11
• /
• pp.5425-5448
• /
• 2018

Wireless sensor network has been widely used in Internet of Things (IoT) applications to support large and dense networks. As sensor nodes are usually tiny and provided with limited hardware resources, the existing multiple access methods, which involve high computational complexity to preserve the protocol performance, is not available under such a scenario. In this paper, we propose an intelligent Medium Access Control (MAC) protocol selection scheme based on machine learning in wireless sensor networks. We jointly consider the impact of inherent behavior and external environments to deal with the application limitation problem of the single type MAC protocol. This scheme can benefit from the combination of the competitive protocols and non-competitive protocols, and help the network nodes to select the MAC protocol that best suits the current network condition. Extensive simulation results validate our work, and it also proven that the accuracy of the proposed MAC protocol selection strategy is higher than the existing work.

• International Journal of Advanced Culture Technology
• /
• v.10 no.4
• /
• pp.86-93
• /
• 2022

To combine the science and technology creativity necessary in the era of the 4th Industrial Revolution, it is necessary to cultivate talents who can discover new knowledge and create new values by combining various knowledge with self-directed mathematical competencies. This research attempted to lay the foundation for the curriculum for fostering future creative convergence talent by preparing, executing, and reflecting on the learning plan after learners themselves understand their level and status through self-directed learning. Firstly, We would like to present a teaching-learning plan based on the essential capabilities of the future society, where the development of a curriculum based on mathematics curriculum and intelligent informatization are accelerated. Secondly, an educational design model system diagram was presented to strengthen the self-directed learning ability of mathematics subjects in the electronic engineering curriculum. Consequently, through a survey, we would like to propose the establishment of an educational system necessary for the 4th industry by analyzing learning ability through self-directed learning teaching methods of subjects related to mathematics, probability, and statistics.

• Journal of the Semiconductor & Display Technology
• /
• v.19 no.2
• /
• pp.60-67
• /
• 2020

Recently, artificial intelligence related technologies including machine learning are being applied to various fields, and the demand is also increasing. In particular, with the development of AR, VR, and MR technologies related to image processing, the utilization of computer vision based on deep learning has increased. The algorithms for object recognition and detection based on deep learning required for image processing are diversified and advanced. Accordingly, problems that were difficult to solve with the existing methodology were solved more simply and easily by using deep learning. This paper introduces various deep learning-based object recognition and extraction algorithms used to detect and recognize various objects in an image and analyzes the technologies that attract attention.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2003.09a
• /
• pp.110-113
• /
• 2003

Most traffic accidents are caused by drivers' carelessness and lack of information on the surrounding objects. In this paper we proposed a model of human intention recognition through case-base learning and to build up an experiment system. The system can help us recognize object's intention (e.g. turn left, turn right or straight) by using detected data about human's motion, speed of the car and the distance between the car and the intersection. Furthermore, we included an example using case-base learning in this paper to improve the precision of recognition as well as an example to explain the use of the system. PC can be used to predict the driving reaction beforehand and send a warning signal to the driver in time if there is any danger.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.12 no.1
• /
• pp.115-122
• /
• 2017

Deep learning is artificial intelligence technology that can teach people like themselves who need machine learning. Deep learning has become of the most promising in the development of artificial intelligence to understand the world and detection technology, and Google, Baidu and Facebook is the most developed in advance. In this paper, we discuss the kind of deep learning frameworks, compare and analyze the efficiency of the image and speech recognition field of it.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.18 no.2
• /
• pp.327-347
• /
• 2024

Edge computing is frequently employed in the Internet of Vehicles, although the computation and communication capabilities of roadside units with edge servers are limited. As a result, to perform distributed machine learning on resource-limited MEC systems, resources have to be allocated sensibly. This paper presents an Improved MADDPG algorithm to overcome the current IoV concerns of high delay and limited offloading utility. Firstly, we employ the MADDPG algorithm for task offloading. Secondly, the edge server aggregates the updated model and modifies the aggregation model parameters to achieve optimal policy learning. Finally, the new approach is contrasted with current reinforcement learning techniques. The simulation results show that compared with MADDPG and MAA2C algorithms, our algorithm improves offloading utility by 2% and 9%, and reduces delay by 29.6%.

• International Journal of Internet, Broadcasting and Communication
• /
• v.15 no.3
• /
• pp.166-175
• /
• 2023

Reinforcement learning can be applied to a wide variety of problems. However, the fundamental limitation of reinforcement learning is that it is difficult to derive an answer within a given time because the problems in the real world are too complex. Then, with the development of neural network technology, research on deep reinforcement learning that combines deep learning with reinforcement learning is receiving lots of attention. In this paper, two types of neural networks are combined with reinforcement learning and their characteristics were compared and analyzed with existing value-based reinforcement learning algorithms. Two types of neural networks are FNN and CNN, and existing reinforcement learning algorithms are SARSA and Q-learning.

• Nuclear Engineering and Technology
• /
• v.54 no.11
• /
• pp.3985-3995
• /
• 2022

The emergence of new nanoscale technologies has imposed significant challenges to designing reliable electronic systems in radiation environments. A few types of radiation like Total Ionizing Dose (TID) can cause permanent damages on such nanoscale electronic devices, and current state-of-the-art technologies to tackle TID make use of expensive radiation-hardened devices. This paper focuses on a novel and different approach: using machine learning algorithms on consumer electronic level Field Programmable Gate Arrays (FPGAs) to tackle TID effects and monitor them to replace before they stop working. This condition has a research challenge to anticipate when the board results in a total failure due to TID effects. We observed internal measurements of FPGA boards under gamma radiation and used three different anomaly detection machine learning (ML) algorithms to detect anomalies in the sensor measurements in a gamma-radiated environment. The statistical results show a highly significant relationship between the gamma radiation exposure levels and the board measurements. Moreover, our anomaly detection results have shown that a One-Class SVM with Radial Basis Function Kernel has an average recall score of 0.95. Also, all anomalies can be detected before the boards are entirely inoperative, i.e. voltages drop to zero and confirmed with a sanity check.