• Clean Technology
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• v.29 no.2
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• pp.118-125
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• 2023

Once a site is contaminated with polychlorinated biphenyls (PCBs), serious environmental and human health risks are inevitable. Therefore, innovative but economical in situ remediation technologies must be immediately applied to the contaminated site. Recently, nanoscale zero-valent iron (nano-ZVI) particles have successfully been applied for the dechlorination of various chlorinated organic compounds like TCE, PCE and DDT, and they are considered to be environmentally safe due to the high abundance of iron in the earth's crust. Nano-ZVIs are much more reactive than granular ones, but tend to agglomerate due to their high surface energy and magnetic properties. In order to prevent them from being agglomerated toward larger particles, TiO₂ was used as a support to immobilize the nano-ZVI particles as much as possible. 10wt% ZVI/TiO₂ was prepared by adding NaBH₄ slowly into an FeSO₄/TiO₂ aqueous slurry. In spite of their non-uniformity in size, the nano-ZVI particles were quite successfully dispersed onto the exterior surface of a non-porous TiO₂ powder. The ZVI/TiO₂ was then employed to degrade Aroclor 1242, a kind of PCBs standard, in spiked soil, and its reactivity towards the degradation of Aroclor 1242 was investigated. The fabricated ZVI/TiO₂ degraded Aroclor 1242 in soil quite effectively, but the creation of remaining dechlorinated compounds, possibly high molecular weight hydrocarbons, in the soil was unavoidable.

• Asia Marketing Journal
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• v.11 no.2
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• pp.149-172
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• 2009

Prior research has investigated different effects of brand performance information such as premium price information and market share information on brand equity components - quality perception and brand preference. It was shown that the differential effects of brand performance information could depend on product-related variables like product category concept and quality variation in the product category. In this study, we conducted an experiment to find out how personal characteristics such as self-construal, price perception and brand commitment could influence the effects of different types of brand performance information. The results show that individuals who have independent self-construal, favorable price perception and emotional commitment with the brand develop more favorable evaluation of the premium price performance information resulting in more positive evaluations of the brand. However, individuals who have interdependent self-construal, unfavorable price perception and cognitive commitment with the brand develop more favorable evaluation of the market share performance information resulting in more positive evaluations of the brand. We discuss the theoretical and practical implications of this study and its limitations, along with future research interests.

• Korean Journal of Culture and Social Issue
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• v.11 no.3
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• pp.1-21
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• 2005

Many researches have argued the most important dimension of perception or evaluation of person is morality and competence in Korean culture. This study was to investigate the cultural characteristics of the criteria of personhood. Two studies were conducted to investigate the criteria used to evaluate person by qualitative approach and the differences of evaluation of wrongdoer who violates interpersonal norm or individual autonomy by experimental method. In addition, anger as emotional response to wrongdoer is conceptualized in terms of moral and self-conscious emotion. Expression of anger is less an outpouring of emotion and more a culturally regulated and normative mode of managing and putting into practice our society's system of rights and obligations - its moral code. According to results of qualitative data by interview and focus group interview, the most important criteria used to evaluate personhood was interpersonal concern, esp, expectation and norm. The results of experiment revealed that violation of interpersonal norm domain evoked angrier towards violator than autonomy domain. The subjects ascribed more blame and responsibility to interpersonal norm violator than autonomy keeper. Also function of behavior inhibition of anger was higher in interpersonal norm domain than autonomy domain.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.12
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• pp.519-524
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• 2023

In hashing-based image retrieval, the hash code of a manipulated image is different from the original image, making it difficult to search for the same image. This paper proposes and evaluates a self-supervised deephashing model that generates perceptual hash codes from feature information such as texture, shape, and color of images. The comparison models are autoencoder-based variational inference models, but the encoder is designed with a fully connected layer, convolutional neural network, and transformer modules. The proposed model is a variational inference model that includes a SimAM module of extracting geometric patterns and positional relationships within images. The SimAM module can learn latent vectors highlighting objects or local regions through an energy function using the activation values of neurons and surrounding neurons. The proposed method is a representation learning model that can generate low-dimensional latent vectors from high-dimensional input images, and the latent vectors are binarized into distinguishable hash code. From the experimental results on public datasets such as CIFAR-10, ImageNet, and NUS-WIDE, the proposed model is superior to the comparative model and analyzed to have equivalent performance to the supervised learning-based deephashing model. The proposed model can be used in application systems that require low-dimensional representation of images, such as image search or copyright image determination.

• Journal of Internet Computing and Services
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• v.25 no.2
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• pp.57-67
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• 2024

This paper explores the potential of electromyography (EMG) as a means of gesture recognition for user input in gesture-based interaction. EMG utilizes small electrodes within muscles to detect and interpret user movements, presenting a viable input method. To classify user gestures based on EMG data, machine learning techniques are employed, necessitating the preprocessing of raw EMG data to extract relevant features. EMG characteristics can be expressed through formulas such as Integrated EMG (IEMG), Mean Absolute Value (MAV), Simple Square Integral (SSI), Variance (VAR), and Root Mean Square (RMS). Additionally, determining the suitable time for gesture classification is crucial, considering the perceptual, cognitive, and response times required for user input. To address this, segment sizes ranging from a minimum of 100ms to a maximum of 1,000ms are varied, and feature extraction is performed to identify the optimal segment size for gesture classification. Notably, data learning employs overlapped segmentation to reduce the interval between data points, thereby increasing the quantity of training data. Using this approach, the paper employs four machine learning models (KNN, SVC, RF, XGBoost) to train and evaluate the system, achieving accuracy rates exceeding 96% for all models in real-time gesture input scenarios with a maximum segment size of 200ms.

• Knowledge Management Research
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• v.25 no.2
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• pp.49-68
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• 2024

Increasing concerns regarding personal data privacy have complicated the acquisition of customer data through online marketing. This study investigates factors influencing customers' willingness to disclose information via a generative AI-based chatbot. Drawing on privacy calculus theory and regulatory focus theory, we explore how customer regulatory focus and familiarity with the generative AI-based chatbot shape disclosure intentions. Our study, involving 473 participants, reveals that low familiarity with the chatbot leads individuals with a prevention focus to perceive higher privacy risks and lower perceived usefulness compared to those with a promotion focus. However, with high familiarity, these differences diminish. Moreover, individuals with a promotion focus show a greater inclination to disclose information when familiarity with the generative AI-based chatbot is low, whereas this regulatory focus does not significantly impact disclosure intentions when familiarity is high. Perceived privacy risks mediate these relationships, underscoring the importance of understanding familiarity with the generative AI-based chatbot in facilitating personal information disclosure.

• Geophysics and Geophysical Exploration
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• v.27 no.2
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• pp.91-107
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• 2024

Single-channel seismic exploration has proven effective in delineating subsurface geological structures using small-scale survey systems. The seismic data acquired through zero- or near-offset methods directly capture subsurface features along the vertical axis, facilitating the construction of corresponding seismic sections. However, substantial noise in single-channel seismic data hampers precise interpretation because of the low signal-to-noise ratio. This study introduces a novel approach that integrate noise reduction and signal enhancement via matrix rank optimization to address this issue. Unlike conventional rank-reduction methods, which retain selected singular values to mitigate random noise, our method optimizes the entire singular value spectrum, thus effectively tackling both random and erratic noises commonly found in environments with low signal-to-noise ratio. Additionally, to enhance the horizontal continuity of seismic events and mitigate signal loss during noise reduction, we introduced an adaptive weighting factor computed from the eigenimage of the seismic section. To access the robustness of the proposed method, we conducted numerical experiments using single-channel Sparker seismic data from the Chukchi Plateau in the Arctic Ocean. The results demonstrated that the seismic sections had significantly improved signal-to-noise ratios and minimal signal loss. These advancements hold promise for enhancing single-channel and high-resolution seismic surveys and aiding in the identification of marine development and submarine geological hazards in domestic coastal areas.

• Archives of design research
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• v.13 no.2
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• pp.95-102
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• 2000

The purpose of this study is to find out what is the preference of users according to the location of benches at urban community parks. This location of benches is seperated into 4 patterns according to arranging pattern of water space, a walk, pergola and shelter, greenspace. To investigate the visual preference is examined by analyzing visual volume of 4 patterns. Results are as follows; 1. Factor analysis by the total data showed that 5 factors explain 60.40 percent of total variance of the location of bench visual character. They were classified by the sensitive factor, visual factor, physical-individual factor, distinct factor, density factor. Among 5 factors, the sensitive factor which represented psychological reaction was appreciated to be highest. 2. Most of 20 items showed the following scores of mean values in sementic differential experiment : Spot 1->Spot 4-> 2-> 3. The mean values between arrangement place locational differences showed significantly, that could explain to be a violent contrast between the natural factors(weater space, green space, etc) and the artificial factors (around of pergola, shelter, etc)

• Korean Journal of Cognitive Science
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• v.27 no.3
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• pp.441-467
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• 2016

This fMRI study is aim to investigate effects of competitive environment in cognitive empathic process in human brain. Empathy is known as a crucial factor for human's adaptive behavior in aspects of social cognition and it is almost automatic process, on the other hand competitive situation is psychologically devastated environment to win someone for getting rewards. We hypnotized that reading and understanding of other person's mind are a specific characteristic related to survival evolutionarily, however competition would have an effect on the empathic cognitive process because of mechanisms of competition. To manipulate the competitive atmosphere, one researcher took a role of competitor against participants and they were instructed to get monetary rewards when their performance was better than a competitor. 21 participants(9 males and 12 females) performed to judge the emotional valence of the empathic task consisted of illustrated images with various situation could be experienced in real world as on $1^{st}$ person perspective in both competitive and non-competitive condition, and did same performance with objects stimulus in control condition. In order to examine the competition effects on empathic process,, hemodynamic response were obtained during fMRI session and the imaging data were analyzed to identify brain regions where responses to each condition across the two consecutive runs. Participants' reaction time in competitive condition was faster statistically significant than non-competitive one. Activation for competitive condition increased in the following areas: ACC, mPFC, SMG, thalamus extended caudate and Nacc, parahippocampal gyrus, and for non-competitive condition increased paracingulate gyrus, temporal pole, vmPFC, superior occipital gyrus. As a result of regression analysis using empathic scores as covariance, the rSMG, IFG, fusiform gyrus, thalamus, putamen were correlated with higher empathic levels, and TPJ were correlated with lower empathic scores. We suggest that these observations could mean competitive environment have an effect on neural base of cognitive empathic process.

• Journal of Korea Society of Industrial Information Systems
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• v.12 no.5
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• pp.1-13
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• 2007

In this research, we found out that bridging nodes have great effect on the robustness of protein-protein interaction networks. Until now, many researchers have focused on node's degree as node's essentiality. Hub nodes in the scale-free network are very essential in the network robustness. Some researchers have tried to relate node's essentiality with node's betweenness centrality. These approaches with betweenness centrality are reasonable but there is a positive relation between node's degree and betweenness centrality value. So, there are no differences between two approaches. We first define a bridging node as the node with low connectivity and high betweenness value, we then verify that such a bridging node is a primary factor in the network robustness. For a biological network database from Internet, we demonstrate that the removal of bridging nodes defragment an entire network severally and the importance of the bridging nodes in the network robustness.