• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.12 no.2
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• pp.113-119
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• 2019

We have developed the multi-functioned friction measuring instrument for the previous research. In here, we improved the performance of friction measuring instrument by applying the adaptive smoothing method and analyzed the friction of plate and monitoring function of friction surface through scratch tests. We substituted lubricant steel plate to lubricant oil used for reducing the friction when fabricating steel plate because lubricant oil was regarded as one of the major causes for the environmental pollution. In particular, the functions of various plate such as galvannealed steel sheets were analyzed because friction coefficient could be changed depending on the type of organic/inorganic plate or state of coating layer. Therefore, we demonstrated that adaptive smoothing method could enhance the accuracy of measuring instrument which eliminate the noise. As a result of using the method, it showed the reduction rate 0.0417% for the friction coefficient 0.16.

• International Journal of Computer Science & Network Security
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• v.21 no.9
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• pp.19-30
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• 2021

Diabetes Mellitus (DM) is one of common chronic diseases leading to severe health complications that may cause death. The disease influences individuals, community, and the government due to the continuous monitoring, lifelong commitment, and the cost of treatment. The World Health Organization (WHO) considers Saudi Arabia as one of the top 10 countries in diabetes prevalence across the world. Since most of the medical services are provided by the government, the cost of the treatment in terms of hospitals and clinical visits and lab tests represents a real burden due to the large scale of the disease. The ability to predict the diabetic status of a patient without the laboratory tests by performing screening based on some personal features can lessen the health and economic burden caused by diabetes alone. The goal of this paper is to investigate the prediction of diabetic and prediabetic patients by considering factors other than the laboratory tests, as required by physicians in general. With the data obtained from local hospitals, medical records were processed to obtain a dataset that classified patients into three classes: diabetic, prediabetic, and non-diabetic. After applying three machine learning algorithms, we established good performance for accuracy, precision, and recall of the models on the dataset. Further analysis was performed on the data to identify important non-laboratory variables related to the patients for diabetes classification. The importance of five variables (gender, physical activity level, hypertension, BMI, and age) from the person's basic health data were investigated to find their contribution to the state of a patient being diabetic, prediabetic or normal. Our analysis presented great agreement with the risk factors of diabetes and prediabetes stated by the American Diabetes Association (ADA) and other health institutions worldwide. We conclude that by performing class-specific analysis of the disease, important factors specific to Saudi population can be identified, whose management can result in controlling the disease. We also provide some recommendations learnt from this research.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.2
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• pp.714-720
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• 2021

The ITL (information theoretic learning) based on the kernel density estimation method that has successfully been applied to machine learning and signal processing applications has a drawback of severe sensitiveness in choosing proper kernel sizes. For the maximization of correntropy criterion (MCC) as one of the ITL-type criteria, several methods of adapting the remaining kernel size ( ) after removing the term have been studied. In this paper, it is shown that the main cause of sensitivity in choosing the kernel size derives from the term and that the adaptive adjustment of in the remaining terms leads to approach the absolute value of error, which prevents the weight adjustment from continuing. Thus, it is proposed that choosing an appropriate constant as the kernel size for the remaining terms is more effective. In addition, the experiment results when compared to the conventional algorithm show that the proposed method enhances learning performance by about 2dB of steady state MSE with the same convergence rate. In an experiment for channel models, the proposed method enhances performance by 4 dB so that the proposed method is more suitable for more complex or inferior conditions.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.14 no.3
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• pp.223-230
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• 2021

Currently, self-driving vehicles are on the verge of commercialization after testing. However, even though autonomous vehicles have not been fully commercialized, 81 accidents have occurred, and the driving method of vehicles to avoid accidents relies heavily on LiDAR. In order for the currently commercialized 3-level autonomous vehicle to develop into a 4-level autonomous vehicle, more information must be collected than previously collected information. Therefore, this paper proposes a Driving Situation Judgment System (DSJS) that accurately calculates the crisis situation the vehicle is in by useing the roughness of the road and the state of the passengers of surrounding vehicles including road information and weather information collected from existing autonomous vehicles. As a result of DSJS's PDM experiment, PDM was able to classify passengers 15.52% more accurately on average than the existing vehicle's passenger recognition system. This study can be a basic research to achieve the 4th level autonomous vehicle by collecting more various types than the data collected by the existing 3rd level autonomous vehicle.

• Korean Journal of Metals and Materials
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• v.46 no.11
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• pp.747-754
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• 2008

Recently, the requirement for the ultra thin copper foil increases with smaller and miniaturized electronic components. Therefore, it is important to examine the surface state of substrate depending on the processing parameter during the anodic oxidation. This study investigated the effect of the various electrolyte concentrations on anodizing of titanium anode prior to copper electrodeposition. Different surface morphology of anodized titanium was obtained at different electrolytic concentration 0.5 M to 3.0 M. In addition, the effect that the surfaces and the electrical characteristics on the electrodeposited copper layer was observed. In this study, surface anodized in the group containing 0.5M $H_2SO_4$ shows more uniform copper crystals with low surface roughness. the surface roughness and sheet resistance for 0.5M $H_2SO_4$ group were $1.353{\mu}m$ and $0.104m{\Omega}/sq$, respectively.

• Journal of the Korea Society of Computer and Information
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• v.25 no.12
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• pp.315-320
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• 2020

The purpose of this study is to analyze and interpret the current situation of private security companies·guards for the past three years, security companies by size, general·special (new education), and qualification system provided by the Police Agency, Security Association, etc. It provides a theoretical foundation for private security and provides a new perspective for interpreting private security. As a result, through the current situation, this private security has a concentration of metropolitan area and facility security, an abnormal personal protection company contrast, the number of personal protection institutes, there is a special security shift to regular jobs, and the current continuous education On the other hand, the education of special security guards has been shown to be limited. In the qualification system, the utilization of security instructor qualifications and the utilization and public relations of personal probation officer qualifications will appear. The current state of typical private security is as follows. The first is the balanced development of private security and the clarity of business divisions. Second, the quality of private security education and educational institutions must be high. Third is the recognition of the qualification system and active public relations.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.2
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• pp.267-273
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• 2021

Non-orthogonal multiple access is the promised candidates in the next generation wireless networks to improve the spectral efficiency by superposing multiple signals. In general, the superposition coding is performed using the difference in channel gain between users based on the user's power allocation. However, when user pairs have the similar channel gain problem, NOMA can not be allowed in the scenario. To overcome this problem, phase rotation based NOMA is presented to increase minimum distance between superposed signals in the constellation point. This paper proposed a novel non-orthogonal multiple access based index modulation using phase rotation. The additional bits can transfer using the index bits that is allocated according to the activated state of the phase rotation. Simulation results are shown that bit error rate and achievable sum rate are better than conventional NOMA.

• Journal of Korea Multimedia Society
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• v.24 no.8
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• pp.1044-1058
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• 2021

Tempered electronic contents have multiplied in last few years, thanks to the emergence of sophisticated artificial intelligence(AI) algorithms. Deepfakes (fake footage, photos, speech, and videos) can be a frightening and destructive phenomenon that has the capacity to distort the facts and hamper reputation by presenting a fake reality. Evidence of ownership or authentication of digital material is crucial for combating the fabricated content influx we are facing today. Current solutions lack the capacity to track digital media's history and provenance. Due to the rise of misrepresentation created by technologies like deepfake, detection algorithms are required to verify the integrity of digital content. Many real-world scenarios have been claimed to benefit from blockchain's authentication capabilities. Despite the scattered efforts surrounding such remedies, relatively little research has been undertaken to discover where blockchain technology can be used to tackle the deepfake problem. Latest blockchain based innovations such as Smart Contract, Hyperledger fabric can play a vital role against the manipulation of digital content. The goal of this paper is to summarize and discuss the ongoing researches related to blockchain's capabilities to protect digital content authentication. We have also suggested a blockchain (smart contract) dependent framework that can keep the data integrity of original content and thus prevent deepfake. This study also aims at discussing how blockchain technology can be used more effectively in deepfake prevention as well as highlight the current state of deepfake video detection research, including the generating process, various detection algorithms, and existing benchmarks.

• Journal of Biomedical Engineering Research
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• v.42 no.6
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• pp.268-276
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• 2021

Functional near-infrared spectroscopy-based brain-computer interface (fNIRS-based BCI) has been receiving much attention. However, we are practically constrained to obtain a lot of fNIRS data by inherent hemodynamic delay. For this reason, when employing machine learning techniques, a problem due to the high-dimensional feature vector may be encountered, such as deteriorated classification accuracy. In this study, we employ an elastic net-based feature selection which is one of the embedded methods and demonstrate the utility of which by analyzing the results. Using the fNIRS dataset obtained from 18 participants for classifying brain activation induced by mental arithmetic and idle state, we calculated classification accuracies after performing feature selection while changing the parameter α (weight of lasso vs. ridge regularization). Grand averages of classification accuracy are 80.0 ± 9.4%, 79.3 ± 9.6%, 79.0 ± 9.2%, 79.7 ± 10.1%, 77.6 ± 10.3%, 79.2 ± 8.9%, and 80.0 ± 7.8% for the various values of α = 0.001, 0.005, 0.01, 0.05, 0.1, 0.2, and 0.5, respectively, and are not statistically different from the grand average of classification accuracy estimated with all features (80.1 ± 9.5%). As a result, no difference in classification accuracy is revealed for all considered parameter α values. Especially for α = 0.5, we are able to achieve the statistically same level of classification accuracy with even 16.4% features of the total features. Since elastic net-based feature selection can be easily applied to other cases without complicated initialization and parameter fine-tuning, we can be looking forward to seeing that the elastic-based feature selection can be actively applied to fNIRS data.

• Journal of the Semiconductor & Display Technology
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• v.21 no.2
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• pp.15-18
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• 2022

The ultraviolet-C emitting praseodymium doped yttrium phosphate (YPO₄:Pr³⁺) powder was synthesized by conventional solid-state reaction. The electroluminescence device was fabricated by simple screen-printing method using the synthesized YPO₄:Pr³⁺ powder, especially, polyvinylidene fluoride as an insulating layer was applied on the printed YPO₄:Pr³⁺ powder for stable performance of the electroluminescence. The electroluminescence properties were investigated under alternating current power system of 400 Hz. The device starts to emit at 350 V, which showed the ultraviolet-C emission peaking at the 233, 245, 264, 273 nm attributed to electronic transition of the Pr³⁺ ions. The electroluminescence intensity was increased as increasing the operating voltage and the device revealed stable performance up to 600 V due to the polyvinylidene fluoride serve as a protective layer.