• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.4
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• pp.677-684
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• 2017

Smart devices are used in a variety of industries, because applications for them are easy to develop and portable. However, industrial equipment can cause security problems for information and accidents when controlling the actuator of the equipment at a remote location. In this paper, we studied methods of solving these problems and the advantages of applying smart control systems to industrial equipment. We propose a manual manipulation method using queries and a smart control access procedure for controlling equipment using a smart device. In addition, we propose a data transmission method employing multiple encryption protocols and a user authentication method using unique information from the smart device and Q & A as the communication data protection and user authentication methods, respectively. In order to evaluate its performance, an operation test of the smart control system and user authentication comparison experiment were performed. In order to understand the advantages of applying the smart control system to the equipment, we conducted a comparative experiment with a teach pendant and evaluated its reaction time in case of error.

• Journal of KIISE:Computing Practices and Letters
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• v.14 no.1
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• pp.31-39
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• 2008

Sensor Node OS should support unified API and efficient sensor device management system to overcome the diversity of sensors and actuators. However, conventional OSs like Tiny-OS and Nano-Q+ do not. In this paper, we propose a sensor device driver management system that present application programmers with unified API and easy deployment of sensors. When a sensor is deployed in our device management system, the device driver is downloaded. This scheme differs from traditional OS like SOS in that only sensor device driver is downloaded, not the whole application image. We designed and implemented the system into Nano-Q+. We described the comparison with other OSs and showed that our system obtains the considerable speedup of downloading.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.1
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• pp.267-274
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• 2024

In this paper, we investigated the number of domestic patent applications by year, the number of domestic patent disclosures by year, and the number of domestic registrations by year regarding smart factories. The number of patent applications by applicant type was investigated. Based on the patents studied, it was found that the IPC appearing in the most patents was G05B 19/418. In addition, through social network analysis of smart factory patented IPCs, it was found that G05B 19/418 was the IPC with the highest degree of centrality. From the above, if the IPC of the core technology of the patent submitted for smart factory is G05B 19/418, the technology combined with G05B 23/02, that is, the technology combining "factory control" and "monitoring" is the most patented. When the IPC of the core technology was G06Q 50/04, it was confirmed that the technology combined with G06Q 50/10, that is, the technology combining "manufacturing" and "service" was the most applied for patents. Through this, it was found that in order to apply for a patent for a smart factory, it would be necessary to file a patent application that takes into account the connectivity between IPCs.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2012.05a
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• pp.387-390
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• 2012

In this paper, we propose a fast booting solution with embedded linux-based smart devices. We have divided the fast boot process into six steps, such as boot loader, kernel, file system, the init-scripts, shared libraries, and applications for an embedded linux-based boot process to improve the fast booting. Improvements for the fast boot are made in the boot loader phase, which is the first phase at power-up, and the init-script that runs the boot loader phase. To improve the fast booting, standby time from the boot loader and unnecessary initialization routine have been removed, and uncompressed kernel image loading as well as optimized copy routine have been applied. Further, a technology that replaces binary scripts in init-script phase and light-weight init process have been utilized to improve the boot.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.5
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• pp.215-219
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• 2021

In this paper, we propose a deep reinforcement learning algorithm-based bi-directional electricity negotiation scheme that adjusts and propose the price they want to exchange for negotiation over smart building and utility grid. By employing a deep Q network algorithm, which is a kind of deep reinforcement learning algorithm, the proposed scheme adjusts the price proposal of smart building and utility grid. From the simulation results, it can be verified that consensus on electricity price negotiation requires average of 43.78 negotiation process. The negotiation process under simulation settings and scenario can also be confirmed through the simulation results.

• Molecules and Cells
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• v.44 no.11
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• pp.843-850
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• 2021

The rapid increase in collateral omics and phenotypic data has enabled data-driven studies for the fast discovery of cancer targets and biomarkers. Thus, it is necessary to develop convenient tools for general oncologists and cancer scientists to carry out customized data mining without computational expertise. For this purpose, we developed innovative software that enables user-driven analyses assisted by knowledge-based smart systems. Publicly available data on mutations, gene expression, patient survival, immune score, drug screening and RNAi screening were integrated from the TCGA, GDSC, CCLE, NCI, and DepMap databases. The optimal selection of samples and other filtering options were guided by the smart function of the software for data mining and visualization on Kaplan-Meier plots, box plots and scatter plots of publication quality. We implemented unique algorithms for both data mining and visualization, thus simplifying and accelerating user-driven discovery activities on large multiomics datasets. The present Q-omics software program (v0.95) is available at http://qomics.sookmyung.ac.kr.

• Journal of the Korea Convergence Society
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• v.9 no.1
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• pp.199-204
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• 2018

Smart phones that have lost their existing physical home buttons are being released at the same time, and the flow of users is also changing. The flow is highly subjective and can vary depending on the individual's tendencies. Therefore, this study investigated the change of the flow of smart phone users by Q methodology which is a method of subjectivity research. As a result, the proficiency level of how to turn on the power to start the screen showed a tendency to decrease the flow, and the playability, which is a result of curiosity about the new thing, The results were very positive in the continuity of navigation due to the use of software buttons instead of buttons. Therefore, it can be seen that the change in the hardware smartphone has a positive effect on the flow.

• Smart Media Journal
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• v.11 no.7
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• pp.39-51
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• 2022

Tableware is among the important necessities of daily life, and children's tableware is especially valued by the society. Under such social background, a study centered at users of children's tableware was conducted from four aspects, including health & safety, sensory experience, behavior guide, and emotional cognition. Children's tableware products were evaluated using the Q method to understand users' cognition of children's tableware products. The interviewees' views about children's tableware design in China can be divided into four types,and put forward different psychological needs for these four types, In the interviews, some interviewees suggested that the ergonomic and regional cultural differences shall all be considered during children's tableware design. Therefore, this study evaluates the design preferences of Chinese children tableware and validates the results of the paper based on the results.

• Journal of Electrical Engineering and Technology
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• v.12 no.3
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• pp.1195-1202
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• 2017

Induction motors often reach their maximum efficiency at the nominal load. In most applications, the machine load is not equal to the nominal load, thus reduces the motor efficiency and turns a greater percent of power into loss. In this paper, the induction motor control problem has been investigated to reduce the system losses. The Field Oriented Control method (FOC) has been employed in this paper. In this research, the mathematical equations related to system losses are calculated in relation to torque and speed, and then the q- and d-axis are summarized according to the current components. After that, the proposed method is applied along with d- and q-axis. In the recent three decades, many techniques have been suggested to improve the induction motor performance using smart and non-smart methods. In this paper, a new PSO-Fuzzy method have used in real time. The fuzzy logic method serves as speed controller in q-axis and PSO algorithm controls the optimum flux in d-axis. It will be proved that the use of this combined method will lead to a significant improvement in motor efficiency.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.1
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• pp.334-349
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• 2022

The effectiveness of Wi-Fi networks is greatly influenced by the optimization of contention window (CW) parameters. Unfortunately, the conventional approach employed by IEEE 802.11 wireless networks is not scalable enough to sustain consistent performance for the increasing number of stations. Yet, it is still the default when accessing channels for single-users of 802.11 transmissions. Recently, there has been a spike in attempts to enhance network performance using a machine learning (ML) technique known as reinforcement learning (RL). Its advantage is interacting with the surrounding environment and making decisions based on its own experience. Deep RL (DRL) uses deep neural networks (DNN) to deal with more complex environments (such as continuous state spaces or actions spaces) and to get optimum rewards. As a result, we present a new approach of CW control mechanism, which is termed as contention window threshold (CW_Threshold). It uses the DRL principle to define the threshold value and learn optimal settings under various network scenarios. We demonstrate our proposed method, known as a smart exponential-threshold-linear backoff algorithm with a deep Q-learning network (SETL-DQN). The simulation results show that our proposed SETL-DQN algorithm can effectively improve the throughput and reduce the collision rates.