• Proceedings of the KIEE Conference
• /
• 1998.07g
• /
• pp.2189-2192
• /
• 1998

The objective of this paper is, based upon the principles of artificial life, to induce emergent behaviors of multiple autonomous mobile robots which form from simple local rules to complex global intelligence. Here, we propose an architecture of neural network learing with reinforcement signals which perceives the neighborhood information and decides the direction and the velocity of movement as mobile robots navigates in a group. As results of the simulations, the optimum weights are obtained in real time, which not only prevent from the collisions between agents and obstacles in the dynamic environment, but also have the mobile robots move and keep in various patterns.

• Journal of information and communication convergence engineering
• /
• v.5 no.1
• /
• pp.7-11
• /
• 2007

The aim of this paper is to design and implement a new ECG signal monitoring and analysis method for the home care of elderly persons or patients, using wireless sensor network (WSN) technology. The wireless technology for home-care purpose gives new possibilities for monitoring of vital parameter with wearable biomedical sensors and will give the patient freedom to be mobile and still be under continuously monitoring. Developed platform for portable real-time analysis of ECG signals can be used as an advanced diagnosis and alarming system. The ECG features are used to detect life-threatening arrhythmias, with an emphasis on the software for analyzing the P-wave, QRS complex, and T-wave in ECG signals at server after receiving data from base station. Based on abnormal ECG activity, the server transfer diagnostic results and alarm conditions to a doctor's PDA. Doctor can diagnose the patients who have survived from arrhythmia diseases.

• International Journal of Computer Science & Network Security
• /
• v.21 no.5
• /
• pp.192-198
• /
• 2021

Trustless, depression, happiness is a normal human emotion that everyone experiences at times. People face problems and hard circumstances every day due to an environment, social life, or traumatic developments in their lives. This study focused on a particular type of inconsistency patterns of behavior that experiences' students during the school time. Some students find depression interferes with their learning and test taking to such an extent that their grades are seriously affected. This study examined the awareness and readiness of a sample of Saudi Arabian high school teachers to recognize, understand, and respond to the ways in which students may respond to testing situations with depression. Findings suggest teachers learn from experience to use both direct and indirect ways to identify students with depression; employ test preparation and test taking strategies to help students reduce depression; and reach out to parents for additional assistance where teacher strategies are not sufficient.

• Electrical & Electronic Materials
• /
• v.11 no.11
• /
• pp.1-8
• /
• 1998

This paper presents an arificial neuro-fuzzy technique based prtial discharge (PD) pattern classifier to power system application. This may require a complicated analysis method employ -ing an experts system due to very complex progressing discharge form under exter-nal stress. After referring briefly to the developments of artificical neural network based PD measurements, the paper outlines how the introduction of new emerging technology has resulted in the design of a number of PD diagnostic systems for practical applicaton of residual lifetime prediction. The appropriate PD data base structure and selection of learning data size of PD pattern based on fractal dimentsional and 3-D PD-normalization, extraction of relevant characteristic fea-ture of PD recognition are discussed. Some practical aspects encountered with unknown stress in the neuro-fuzzy techniques based real time PD recognition are also addressed.

• Journal of the Semiconductor & Display Technology
• /
• v.22 no.2
• /
• pp.77-80
• /
• 2023

Since fires in uncontrolled environments pose serious risks to society and individuals, many researchers have been investigating technologies for early detection of fires that occur in everyday life. Recently, with the development of deep learning vision technology, research on fire detection models using neural network backbones such as Transformer and Convolution Natural Network has been actively conducted. Vision-based fire detection systems can solve many problems with physical sensor-based fire detection systems. This paper proposes a fire detection method using the latest YOLOv8, which improves the existing fire detection method. The proposed method develops a system that detects sparks and smoke from input images by training the Yolov8 model using a universal fire detection dataset. We also demonstrate the superiority of the proposed method through experiments by comparing it with existing methods.

• Journal of Korea Society of Industrial Information Systems
• /
• v.16 no.1
• /
• pp.31-36
• /
• 2011

Ubiquitous network and wireless sensor networks is being applied in various fields. Located at target areas, node of wireless sensor network uses batteries as a power source. Batteries have a limited energy in sensor network applications. Also, before use, the battery must be charged and It is difficult to replace the battery. Therefore, energy harvesting technology is being researched and being developed for long life of sensor node. Especially, sola energy is being extensively researched. because that can have great amounts of energy than other environmental energy in a short time. In this study, we tested battery charging and recharging, operation of sensor node using Solar Cell. Also, monitoring data gathering and voltage Analysis showed energy harvesting time of Sola Cell battery for sensor node and operation of sensor node.

• Journal of Family Resource Management and Policy Review
• /
• v.9 no.4
• /
• pp.81-93
• /
• 2005

The purpose of this study were to examine the time use of college students, and then to investigate the relationship between the satisfaction of the time use and the self identity. The subjects were 249 students in college. It was analyzed by using frequency, mean, ANOVA, Duncan's multiple range analysis. The results were as follows: 1. College students were used to employ many obligate time including studying. Their peak time was 10 a.m., 6 p.m. or 10 p.m., and their slump time was from 7 a.m. to 9, or from noon to 2 p.m.. They enjoyed on weekend, and then tired on Monday. 2. College students were satisfied with the future time use than the present. In survey of self-identity inventory, they lacked confidence in their future, but they oriented their goal and were familiar with the social network. 3. The more college students satisfied time use, the more the level of self-identity was high. This, it was suggested the program of time management for college students.

• Journal of the Institute of Electronics and Information Engineers
• /
• v.50 no.12
• /
• pp.98-106
• /
• 2013

The duty-cycle synchronization among mobile sensor nodes with energy-harvesting is very important. The nodes should keep their duty-cycle same to others as much as possible because they have to cooperate each other and to consume energy efficiently. The distribution of node position in network affects not only node connectivity but also the active time of synchronized nodes, and it relates to network life-time finally. In this paper, we introduce a network topology change algorithm (TCA) for energy-harvesting mobile sensor networks based on self-synchronized duty-cycling. The algorithm tries to change a network topology into a balanced topology where the mobile sensor nodes are unified according to the density of the number of nodes. For TCA, both fluid flow algorithm and flock dispersion algorithm are proposed and they are evaluated through the simulation in agent based modeling language. TCA is applied to the energy-harvesting mobile sensor networks to improve the synchronization of duty-cycle and to reduce the variation of energy consumption among nodes.

• Journal of KIISE
• /
• v.42 no.12
• /
• pp.1495-1502
• /
• 2015

Data compression involves a trade-off between delay time and data size. Greater delay times require smaller data sizes and vice versa. There have been many studies performed in the field of wireless sensor networks on increasing network life cycle durations by reducing data size to minimize energy consumption; however, reductions in data size result in increases of delay time due to the added processing time required for data compression. Meanwhile, as energy generation occurs periodically in solar energy-based wireless sensor networks, redundant energy is often generated in amounts sufficient to run a node. In this study, this excess energy is used to reduce the delay time between nodes in a sensor network consisting of solar energy-based nodes. The energy threshold value is determined by a formula based on the residual energy and charging speed. Nodes with residual energy below the threshold transfer data compressed to reduce energy consumption, and nodes with residual energy above the threshold transfer data without compression to reduce the delay time between nodes. Simulation based performance verifications show that the technique proposed in this study exhibits optimal performance in terms of both energy and delay time compared with traditional methods.

• IMMUNE NETWORK
• /
• v.9 no.6
• /
• pp.274-284
• /
• 2009

Background: Swiprosin-1 was identified in human CD8+ lymphocytes, mature B cells and non-lymphonoid tissue. We have recently reported that swiprosin-1 is expressed in mast cells and up-regulated in both in vitro and in vivo. Methods: The expression of cytokines and swiprosin-1 were determined by by real time PCR and conventional PCR. Pharmacological inhibitors were treated to investigate potential mechanism of swiprosin-1 in mast cell activation. Actin content was evaluated by confocal microscopy and flow cytometry. Results: The swiprosin-1 augmented PMA/A23187-induced expression of cytokines and release of histamine. However, knock-down of swiprosin-1 showed only a modest effect on PMA/A23187-induced cytokine expression, suggesting that swiprosin-1 has gain-of-function characteristics. Swiprosin-1 was found in microvilli-like membrane protrusions and highly co-localized with F-actin. Importantly, either disruption of actin by cytochalasin B or inhibition of PI3 kinase, an enzyme involved in actin remodeling, by wortmannin blocked cytokine expression only in swiprosin-1-overexpressing cells. Conclusion: These results suggest that swiprosin-1 modulates mast cell activation potentially through actin regulation.