• KIPS Transactions on Computer and Communication Systems
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• v.8 no.8
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• pp.201-208
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• 2019

Intra-Body Communication (IBC) is a communication method using the human body as a communication medium. The fact that our human body consists of water and electrolyte allow such communication method could work and have strength in low-power. However, because the IBC directly affects to human body by using it as a medium, there was a lack of research in communication protocols of each communication layer. In this paper, we suggests MAC parameters which affects the performance of communication in human body channel, and propose new MAC protocol. Our results shows that our MAC is suitable for supporting high data rate applications with comparable radio duty cycle performance.

• Journal of IKEEE
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• v.23 no.3
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• pp.768-776
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• 2019

With the deployment of 4-th generation industrial revolution, the computer based manufacturing technologies employing advanced IT technology are much more popular than any other past years. In this research, some novel S/W technologies related to the industrial X-ray CT (computed tomography) for the inspection of the industrial parts are introduced. First, newly constructed industrial X-ray CT is presented in this paper, where some basic principles and functions of the CT are described. Then some research platforms are developed to generate more advanced functionalities of the industrial CT. Especially, the data transform from CT to general S/W such as Matlab is conducted. And based on this techniques, some supplementary S/W platform such as GUI (graphical user interface) of the CT S/W and some 3D voxel based image processing technologies can be developed in this paper. The industrial CT is one of the rare research items and it's values can be much more enhanced when it is used with advanced IT technologies.

• KIPS Transactions on Computer and Communication Systems
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• v.8 no.3
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• pp.57-64
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• 2019

In this paper, an efficient prediction-based parallel simulation method using spatially partial simulation strategy is proposed for improving both the performance of the event-driven gate-level timing simulation and the debugging efficiency. The proposed method quickly generates the prediction data on-the-fly, but still accurately for the input values and output values of parallel event-driven local simulations by applying the strategy to the simulation at the higher abstraction level. For those six designs which had used for the performance evaluation of the proposed strategy, our method had shown about 3.7x improvement over the most general sequential event-driven gate-level timing simulation, 9.7x improvement over the commercial multi-core based parallel event-driven gate-level timing simulation, and 2.7x improvement over the best of previous prediction-based parallel simulation results, on average.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.12 no.2
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• pp.120-126
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• 2019

Recently, there is an increasing need for highly precise processing with the rapid development of precision machinery, electrical and electronics, and semiconductor industries. Cutting machine control relies on the operator's sense and experience in tradition, but it has been greatly enhanced by the adoption of CNC(Computerized Numeric Controller). In addition, cutting dynamics technology has been paid attention to reflect the operating state of machine in real time. This paper presents a method to detect and stabilize tool vibration by attaching an acoustic sensor to a CNC machine. The sensed acoustic data is synchronized with the tool position and the abnormal vibration frequency is separated from the collected acoustic frequency, then analyzed to detect the tool vibration. Also the reliability the tool vibration detection and stabilization is improved by applying the cutting dynamic method. The proposed method is analyzed and evaluated in terms of the surface roughness.

• KIPS Transactions on Computer and Communication Systems
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• v.8 no.4
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• pp.93-102
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• 2019

As interest in wellness grows, There is a lot of research about monitoring individual health using wearable devices. Accordingly, a variety of methods have been studied to distinguish exercise from daily activities using wearable devices. Most of these existing studies are machine learning methods. However, there are problems with over-fitting on individual person's learning, data discontinuously recognition by independent segmenting and fake activity. This paper suggests a detection method for exercise activity based on the physiological response principle of heart rate up and down during exercise. This proposed method calculates activity intensity and heart rate from triaxial and photoplethysmography sensor to determine a heart rate recovery, then detects exercise by estimating activity intensity or detecting a heart rate rising state. Experimental results show that our proposed algorithm has 98.64% of averaged accuracy, 98.05% of averaged precision and 98.62% of averaged recall.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.1
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• pp.9-18
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• 2021

Although Generative Adversarial Networks (GANs) have gained great popularity in computer vision and related fields, generating audio signals independently has yet to be presented. Unlike images, an audio signal is a sampled signal consisting of discrete samples, so it is not easy to learn the signals using CNN architectures, which is widely used in image generation tasks. In order to overcome this difficulty, GAN researchers proposed a strategy of applying time-frequency representations of audio to existing image-generating GANs. Following this strategy, we propose an improved method for increasing the fidelity of synthesized audio signals generated by using GANs. Our method is demonstrated on a public speech dataset, and evaluated by Fréchet Inception Distance (FID). When employing our method, the FID showed 10.504, but 11.973 as for the existing state of the art method (lower FID indicates better fidelity).

• The Journal of Advanced Prosthodontics
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• v.14 no.3
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• pp.150-161
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• 2022

PURPOSE. The purpose of the study was to assess the influence of build orientations and density of support structures on the trueness of the 3D printed removable partial denture (RPD) frameworks. MATERIALS AND METHODS. A maxillary Kennedy class III and mandibular class I casts were 3D scanned and used to design and produce two 3D virtual models of RPD frameworks. Using digital light processing (DLP) 3D printing, 47 RPD frameworks were fabricated at 3 different build orientations (100, 135 and 150-degree angles) and 2 support structure densities. All frameworks were scanned and 3D compared to the original virtual RPD models by metrology software to check 3D deviations quantitatively and qualitatively. The accuracy data were statistically analyzed using one-way ANOVA for build orientation comparison and independent sample t-test for structure density comparison at (α = .05). Points study analysis targeting RPD components and representative color maps were also studied. RESULTS. The build orientation of 135-degree angle of the maxillary frameworks showed the lowest deviation at the clasp arms of tooth 26 of the 135-degree angle group. The mandibular frameworks with 150-degree angle build orientation showed the least deviation at the rest on tooth 44 and the arm of the I-bar clasp of tooth 45. No significant difference was seen between different support structure densities. CONCLUSION. Build orientation had an influence on the accuracy of the frameworks, especially at a 135-degree angle of maxillary design and 150-degree of mandibular design. The difference in the support's density structure revealed no considerable effect on the accuracy.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.6
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• pp.1323-1328
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• 2021

In this paper, we present a medical electric scooter sharing platform for the transportation vulnerable who are experiencing difficulties and inconveniences in moving. The proposed medical electric scooter sharing platform for the transportation vulnerable includes basic mobile rental, return, and functions that incorporate the IOT technology of the currently operating personal mobility sharing platform. The safety function has been strengthened. The medical electric scooter sharing platform for the transportation vulnerable stores driving data on the server in real time through GPS, and strengthens the alarm and call function in advance of an accident to enable rapid SOS processing. By making the quick contact and responding to the situation, people with disabilities can drive safely and comfortably.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.4
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• pp.127-132
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• 2022

An anonymous encrypted communication networks that make it difficult to identify the trace of a user's access by passing through several virtual computers and/or networks, such as Tor, provides user and data privacy in the process of Internet communications. However, when it comes to abuse for inappropriate purposes, such as sharing of illegal contents, arms trade, etc. through such anonymous encrypted communication networks, it is difficult to detect and take appropriate countermeasures. In this paper, by extending the website fingerprinting technique that can identify access to a specific site even in anonymous encrypted communication, a method for specifying and classifying service types of websites for not only well-known sites but also unknown sites is proposed. This approach can be used to identify hidden sites that can be used for malicious purposes.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.5
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• pp.755-762
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• 2022

This paper propose a clustered algorithm that possible more efficient COVID-19 disease learning prediction within clustering using context-aware attribute information. In typically, clustering of COVID-19 diseases provides to classify interrelationships within disease cluster information in the clustering process. The clustering data will be as a degrade factor if new or newly processing information during treated as contaminated factors in comparative interrelationships information. In this paper, we have shown the solving the problems and developed a clustering algorithm that can extracting disease correlation information in using K-means algorithm. According to their attributes from disease clusters using accumulated information and interrelationships clustering, the proposed algorithm analyzes the disease correlation clustering possible and centering points. The proposed algorithm showed improved adaptability to prediction accuracy of the classification management system in terms of learning as a group of multiple disease attribute information of COVID-19 through the applied simulation results.