• Proceedings of the Korean Society of Computer Information Conference
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• 2021.07a
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• pp.305-307
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• 2021

Alzheimer's Disease (AD) is a cognitive disorder characterized by memory impairment that can be assessed at early stages based on administering clinical tests. However, the AD pathophysiological mechanism is still poorly understood due to the difficulty of distinguishing different levels of AD severity, even using a variety of brain modalities. Therefore, in this study, we present a hybrid EEG-fNIRS modalities to compensate for each other's weaknesses with the help of Machine Learning (ML) techniques for classifying four subject groups, including healthy controls (HC) and three distinguishable groups of AD levels. A concurrent EEF-fNIRS setup was used to record the data from 41 subjects during Oddball and 1-back tasks. We employed both a traditional neural network (NN) and a CNN-LSTM hybrid model for fNIRS and EEG, respectively. The final prediction was then obtained by using majority voting of those models. Classification results indicated that the hybrid EEG-fNIRS feature set achieved a higher accuracy (71.4%) by combining their complementary properties, compared to using EEG (67.9%) or fNIRS alone (68.9%). These findings demonstrate the potential of an EEG-fNIRS hybridization technique coupled with ML-based approaches for further AD studies.

• The Journal of the Convergence on Culture Technology
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• v.9 no.4
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• pp.699-706
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• 2023

Recently, the 4 generation industry revolution is developed with advanced and combined with a variety of new technologies. Conventional healthcare system is applied with IoT application. It provides many advantages with mobility and swift data transfers to patient and doctor. In despite of these kinds of advantages, it occurred security issues between basic devices and protocols in their applications. Especially, internet of things have restricted and limited resources such as small memory capacity, low capability of computing power, etc. Therefore, we can not utilize conventional mechanism. In this paper, we analyzed attacks and vulnerability in terms of security issues. To analyze security structure, features, demands and requirements, we solve the methods to be reduced security issues.

• The Journal of the Convergence on Culture Technology
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• v.9 no.4
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• pp.639-647
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• 2023

A variety of communications are developed and advanced by integration of wireless and wire connections with heterogeneous system. Traditional technologies are mainly focus on information technology based on computer techniques in the field of industry, manufacture and automation fields. As new technologies are developed and enhanced with traditional techniques, a lot of new applications are emerged and merged with existing mechanism and skills. The representative applications are IoT(Internet of Things) services and applications. IoT is breakthrough technologies and one of the innovation industries which are called 4 generation industry revolution. Due to limited resources in IoT such as small memory, low power and computing power, IoT devices are vulnerable and disclosed with security problems. In this paper, we reviewed and analyzed security challenges, threats and requirements under IoT service.

• Asia Marketing Journal
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• v.14 no.4
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• pp.57-75
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• 2013

It is a well-established theory that emotion is influential in cognitive processing. Extensive prior research on emotion has shown that emotional factors, such as affect, mood, and feeling, play as information indicating whether he or she has enough knowledge. Most of their findings focused on the effect of emotional valence (i.g., one's subjective positivity or negativity related with the emotion). Recently, several studies on emotion suggest that there is another dimension of emotion, which affects the type of cognitive processing. The studies argue that emotional certainty facilitates heuristic processing, whereas emotional uncertainty promotes systematic processing. Based on the findings, current study examines the effect of certainty on attitudes and recall. Specifically, the authors investigate the effect of certainty on how much effort individuals use to process advertising information and how certainty affects attitude formation toward the advertised product. The authors also focus on recall to clarify the working mechanism of certainty on attitudes, because recall performance reflects the depth of information processing. Based on previous findings, the authors hypothesize that uncertainty (vs. certainty) leads to more favorable attitudes as well as better recall, and conduct an experiment using a fictitious advertisement with 218 participants. The results confirm the predicted effects of certainty only on attitudes not recall. A possible explanation of this discrepancy between attitudes and recall lies in the measurement method, unaided recall. To rule out this possibility, the authors perform an additional analysis with the participants who recall any correct information of the target advertisement. The results show certainty has a negative effect on both attitudes and recall. A bootstrapping test reveals that recall mediates the effect of certainty on attitudes. This result confirms that certainty decreases elaboration, which in turn leads to less favorable attitudes relative to uncertainty. Additionally, our data shows the association among certainty, recall, and attitudes by showing the indirect effect of certainty on attitudes via recall. This research encourages practitioners in the field to emphasize that they should focus on target audiences' emotional certainty before they provide the persuasive message, by showing that uncertainty promotes effortful processing, which in turn leads to better memory and more favorable attitudes.

• International Journal of Computer Science & Network Security
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• v.23 no.10
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• pp.147-156
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• 2023

Electronic voting machines (EVMs) are replacing research ballots due to the errors involved in the manual counting process and the lengthy time required to count the votes. Even though these digital recording electronic systems are advancements, they are vulnerable to tampering and electoral fraud. The suspected vulnerabilities in EVMs are the possibility of tampering with the EVM's memory chip or replacing it with a fake one, their simplicity, which allows them to be tampered with without requiring much skill, and the possibility of double voting. The vote data is shared among all network devices, and peer-to-peer verification is performed to ensure the vote data's authenticity. To successfully tamper with the system, all of the data stored in the nodes must be changed. This improves the proposed system's efficiency and dependability. Elections and voting are fundamental components of a democratic system. Various attempts have been made to make modern elections more flexible by utilizing digital technologies. The fundamental characteristics of free and fair elections are intractability, immutability, transparency, and the privacy of the actors involved. This corresponds to a few of the many characteristics of blockchain-like decentralized ownership, such as chain immutability, anonymity, and distributed ledger. This working research attempts to conduct a comparative analysis of various blockchain technologies in development and propose a 'Blockchain-based Electronic Voting System' solution by weighing these technologies based on the need for the proposed solution. The primary goal of this research is to present a robust blockchain-based election mechanism that is not only reliable but also adaptable to current needs.

• Biomolecules & Therapeutics
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• v.32 no.1
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• pp.77-83
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• 2024

Alzheimer's disease (AD) is a neurodegenerative disease characterized by neuronal cell death and memory impairment. Corticosterone (CORT) is a glucocorticoid hormone produced by the hypothalamic-pituitary-adrenal axis in response to a stressful condition. Excessive stress and high CORT levels are known to cause neurotoxicity and aggravate various diseases, whereas mild stress and low CORT levels exert beneficial actions under pathophysiological conditions. However, the effects of mild stress on AD have not been clearly elucidated yet. In this study, the effects of low (3 and 30 nM) CORT concentration on Aβ_25-35-induced neurotoxicity in SH-SY5Y cells and underlying molecular mechanisms have been investigated. Cytotoxicity caused by Aβ_25-35 was significantly inhibited by the low concentration of CORT treatment in the cells. Furthermore, CORT pretreatment significantly reduced Aβ_25-35-mediated pro-apoptotic signals, such as increased Bim/Bcl-2 ratio and caspase-3 cleavage. Moreover, low concentration of CORT treatment inhibited the Aβ_25-35-induced cyclooxygenase-2 and pro-inflammatory cytokine expressions, including tumor necrosis factor-α and interleukin-1β. Aβ_25-35 resulted in intracellular accumulation of reactive oxygen species and lipid peroxidation, which were effectively reduced by the low CORT concentration. As a molecular mechanism, low CORT concentration activated the nuclear factor-erythroid 2-related factor 2, a redox-sensitive transcription factor mediating cellular defense and upregulating the expression of antioxidant enzymes, such as NAD(P)H:quinone oxidoreductase, glutamylcysteine synthetase, and manganese superoxide dismutase. These findings suggest that low CORT concentration exerts protective actions against Aβ_25-35-induced neurotoxicity and might be used to treat and/or prevent AD.

• Biomolecules & Therapeutics
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• v.32 no.3
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• pp.301-308
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• 2024

Alzheimer's disease (AD) is a progressive and irreversible neurodegenerative disorder characterized by extracellular amyloid plaques composed of amyloid β-peptide (Aβ). Studies have indicated that Ca²⁺ dysregulation is involved in AD pathology. It is reported that decreased capacitative Ca²⁺ entry (CCE), a refilling mechanism of intracellular Ca²⁺, resulting in increased Aβ production. In contrast, constitutive activation of CCE could decrease Aβ production. Panax ginseng Meyer is known to enhance memory and cognitive functions in healthy human subjects. We have previously reported that some ginsenosides decrease Aβ levels in cultured primary neurons and AD mouse model brains. However, mechanisms involved in the Aβ-lowering effect of ginsenosides remain unclear. In this study, we investigated the relationship between CCE and Aβ production by examining the effects of various ginsenosides on CCE levels. Aβ-lowering ginsenosides such as Rk1, Rg5, and Rg3 potentiated CCE. In contrast, ginsenosides without Aβ-lowering effects (Re and Rb2) failed to potentiate CCE. The potentiating effect of ginsenosides on CCE was inhibited by the presence of 2-aminoethoxydiphenyl borate (2APB), an inhibitor of CCE. 2APB alone increased Aβ42 production. Furthermore, the presence of 2APB prevented the effects of ginsenosides on Aβ42 production. Our results indicate that ginsenosides decrease Aβ production via potentiating CCE levels, confirming a close relationship between CCE levels and Aβ production. Since CCE levels are closely related to Aβ production, modulating CCE could be a novel target for AD therapeutics.

• Journal of Information Processing Systems
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• v.20 no.3
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• pp.375-390
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• 2024

Edge computing architecture has effectively alleviated the computing pressure on cloud platforms, reduced network bandwidth consumption, and improved the quality of service for user experience; however, it has also introduced new security issues. Existing anomaly detection methods in big data scenarios with cloud-edge computing collaboration face several challenges, such as sample imbalance, difficulty in dealing with complex network traffic attacks, and difficulty in effectively training large-scale data or overly complex deep-learning network models. A lightweight deep-learning model was proposed to address these challenges. First, normalization on the user side was used to preprocess the traffic data. On the edge side, a trained Wasserstein generative adversarial network (WGAN) was used to supplement the data samples, which effectively alleviates the imbalance issue of a few types of samples while occupying a small amount of edge-computing resources. Finally, a trained lightweight deep learning network model is deployed on the edge side, and the preprocessed and expanded local data are used to fine-tune the trained model. This ensures that the data of each edge node are more consistent with the local characteristics, effectively improving the system's detection ability. In the designed lightweight deep learning network model, two sets of convolutional pooling layers of convolutional neural networks (CNN) were used to extract spatial features. The bidirectional long short-term memory network (BiLSTM) was used to collect time sequence features, and the weight of traffic features was adjusted through the attention mechanism, improving the model's ability to identify abnormal traffic features. The proposed model was experimentally demonstrated using the NSL-KDD, UNSW-NB15, and CIC-ISD2018 datasets. The accuracies of the proposed model on the three datasets were as high as 0.974, 0.925, and 0.953, respectively, showing superior accuracy to other comparative models. The proposed lightweight deep learning network model has good application prospects for anomaly traffic detection in cloud-edge collaborative computing architectures.

• Journal of Aerospace System Engineering
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• v.18 no.4
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• pp.43-52
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• 2024

This paper presents an anomaly detection system that uses an LSTM-Autoencoder model to identify early-stage anomalies in the blade pitch system of floating wind turbines. The sensor data used in power plant monitoring systems is primarily composed of multivariate time-series data for each component. Comprising two unidirectional LSTM networks, the system skillfully uncovers long-term dependencies hidden within sequential time-series data. The autoencoder mechanism, learning solely from normal state data, effectively classifies abnormal states. Thus, by integrating these two networks, the system can proficiently detect anomalies. To confirm the effectiveness of the proposed framework, a real multivariate time-series dataset collected from a wind turbine model was employed. The LSTM-autoencoder model showed robust performance, achieving high classification accuracy.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.975-976
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• 1993

This talk presents the overview of the author's research and development activities on fuzzy inference hardware. We involved it with two distinct approaches. The first approach is to use application specific integrated circuits (ASIC) technology. The fuzzy inference method is directly implemented in silicon. The second approach, which is in its preliminary stage, is to use more conventional microprocessor architecture. Here, we use a quantitative technique used by designer of reduced instruction set computer (RISC) to modify an architecture of a microprocessor. In the ASIC approach, we implemented the most widely used fuzzy inference mechanism directly on silicon. The mechanism is beaded on a max-min compositional rule of inference, and Mandami's method of fuzzy implication. The two VLSI fuzzy inference chips are designed, fabricated, and fully tested. Both used a full-custom CMOS technology. The second and more claborate chip was designed at the University of North Carolina(U C) in cooperation with MCNC. Both VLSI chips had muliple datapaths for rule digital fuzzy inference chips had multiple datapaths for rule evaluation, and they executed multiple fuzzy if-then rules in parallel. The AT & T chip is the first digital fuzzy inference chip in the world. It ran with a 20 MHz clock cycle and achieved an approximately 80.000 Fuzzy Logical inferences Per Second (FLIPS). It stored and executed 16 fuzzy if-then rules. Since it was designed as a proof of concept prototype chip, it had minimal amount of peripheral logic for system integration. UNC/MCNC chip consists of 688,131 transistors of which 476,160 are used for RAM memory. It ran with a 10 MHz clock cycle. The chip has a 3-staged pipeline and initiates a computation of new inference every 64 cycle. This chip achieved an approximately 160,000 FLIPS. The new architecture have the following important improvements from the AT & T chip: Programmable rule set memory (RAM). On-chip fuzzification operation by a table lookup method. On-chip defuzzification operation by a centroid method. Reconfigurable architecture for processing two rule formats. RAM/datapath redundancy for higher yield It can store and execute 51 if-then rule of the following format: IF A and B and C and D Then Do E, and Then Do F. With this format, the chip takes four inputs and produces two outputs. By software reconfiguration, it can store and execute 102 if-then rules of the following simpler format using the same datapath: IF A and B Then Do E. With this format the chip takes two inputs and produces one outputs. We have built two VME-bus board systems based on this chip for Oak Ridge National Laboratory (ORNL). The board is now installed in a robot at ORNL. Researchers uses this board for experiment in autonomous robot navigation. The Fuzzy Logic system board places the Fuzzy chip into a VMEbus environment. High level C language functions hide the operational details of the board from the applications programme . The programmer treats rule memories and fuzzification function memories as local structures passed as parameters to the C functions. ASIC fuzzy inference hardware is extremely fast, but they are limited in generality. Many aspects of the design are limited or fixed. We have proposed to designing a are limited or fixed. We have proposed to designing a fuzzy information processor as an application specific processor using a quantitative approach. The quantitative approach was developed by RISC designers. In effect, we are interested in evaluating the effectiveness of a specialized RISC processor for fuzzy information processing. As the first step, we measured the possible speed-up of a fuzzy inference program based on if-then rules by an introduction of specialized instructions, i.e., min and max instructions. The minimum and maximum operations are heavily used in fuzzy logic applications as fuzzy intersection and union. We performed measurements using a MIPS R3000 as a base micropro essor. The initial result is encouraging. We can achieve as high as a 2.5 increase in inference speed if the R3000 had min and max instructions. Also, they are useful for speeding up other fuzzy operations such as bounded product and bounded sum. The embedded processor's main task is to control some device or process. It usually runs a single or a embedded processer to create an embedded processor for fuzzy control is very effective. Table I shows the measured speed of the inference by a MIPS R3000 microprocessor, a fictitious MIPS R3000 microprocessor with min and max instructions, and a UNC/MCNC ASIC fuzzy inference chip. The software that used on microprocessors is a simulator of the ASIC chip. The first row is the computation time in seconds of 6000 inferences using 51 rules where each fuzzy set is represented by an array of 64 elements. The second row is the time required to perform a single inference. The last row is the fuzzy logical inferences per second (FLIPS) measured for ach device. There is a large gap in run time between the ASIC and software approaches even if we resort to a specialized fuzzy microprocessor. As for design time and cost, these two approaches represent two extremes. An ASIC approach is extremely expensive. It is, therefore, an important research topic to design a specialized computing architecture for fuzzy applications that falls between these two extremes both in run time and design time/cost. TABLEI INFERENCE TIME BY 51 RULES {{{{Time }}{{MIPS R3000 }}{{ASIC }}{{Regular }}{{With min/mix }}{{6000 inference 1 inference FLIPS }}{{125s 20.8ms 48 }}{{49s 8.2ms 122 }}{{0.0038s 6.4㎲ 156,250 }} }}