• Journal of Climate Change Research
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• v.3 no.4
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• pp.235-243
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• 2012

Mobile sources are one of the most significant contributors to the inventory of greenhouse gas (GHG). The administration in Korea has set a goal of cutting GHG emissions of vehicles by 34.3% compared to Business As Usual (BAU) by 2020. To achieve this goal, GHG emission standards for vehicles have been applied since 2012, and now light-duty trucks are under consideration to be included to the vehicle types that will be regulated in the new version of GHG emission standards. Therefore, this study focuses on analyzing characteristics of exhaust GHGs (CO2, CH4, and N2O) emissions of diesel light-duty trucks according to their various driving modes. GHGs emissions of diesel light-duty trucks reduced in inverse proportion to the speed of the vehicles. GHGs emissions from the combined mode were 8% and 14% lower than those from the CVS- 75 and NEDC modes, respectively.

• Science of Emotion and Sensibility
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• v.22 no.2
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• pp.49-58
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• 2019

In the era of the 4th Industrial Revolution, artificial intelligence (AI) has become one of the core technologies in terms of the business strategy among information technology companies. Both international and domestic major portal companies are launching AI search services. These AI search services utilize voice, images, and other unstructured data to provide different experiences from existing text-based search services. An unfamiliar experience is a factor that can hinder the usability of the service. Therefore, the usability testing of the AI search services is necessary. This study examines the usability of the AI search service on the Naver App 8.9.3 beta version by comparing it with the search services of the current Naver App and targets 30 people in their 20s and 30s, who have experience using Naver apps. The usability of Smart Lens, Smart Voice, Smart Around, and AiRS, which are the Naver App beta versions of their artificial intelligence search service, is evaluated and statistically significant usability changes are revealed. Smart Lens, Smart Voice, and Smart Around exhibited positive changes, whereas AiRS exhibited negative changes in terms of usability. This study evaluates the change in usability according to the application of the artificial intelligence search services and investigates the correlation between the evaluation factors. The obtained data are expected to be useful for the usability evaluation of services that use AI.

• Journal of the Korea Organic Resources Recycling Association
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• v.30 no.4
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• pp.59-66
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• 2022

Lithium-ion batteries have greatly expanded along with the mobile phone market, and as the electric vehicle business is activated in earnest, they will attract many people's attention even afterwards. Until now, many people have attracted attention to the recovery of valuable metals inside lithium-ion batteries, but graphite, which is mainly used as an anode material, is also worth recycling. Therefore, in order to recover graphite with high purity and valuable metals, graphite that can be used as an anode material of a secondary battery may be generated again through a regeneration process of purifying and separating graphite from a waste lithium-ion battery and recovering electrical characteristics of graphite. This paper describes the process of converting waste graphite into regenerated graphite and the environmental and economic effects of regenerated graphite.

• Clean Technology
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• v.27 no.1
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• pp.33-38
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• 2021

Demand for lithium-based secondary batteries is greatly increasing with the explosive growth of related industries, such as mobile devices and electric vehicles. In Korea, there are several top-rated global lithium-ion battery manufacturers accounting for 40% of the global secondary battery business. Most discarded lithium secondary batteries are recycled as scrap to recover valuable metals, such as Nickel and Cobalt, but residual wastes are disposed of according to the residual lithium-ion concentration. Furthermore, there has not been an attempt on the possibility of water discharge system contamination due to the concentration of lithium ions, and the effluent water quality standards of public sewage treatment facilities are becoming stricter year after year. In this study, the as-received waste water generated from the cathode electrode coating process in the manufacturing of high-nickel-based NCM cathode material used for high-performance and long-term purposes was analyzed. We suggested a facile recycling process chart for waste water treatment. We revealed a correlation between lithium-ion concentration and pH effect according to the proposed waste water of each recycling process through analyzing standard water quality tests and daphnia ecological toxicity. We proposed a realistic waste water treatment plan for lithium electrode manufacturing plants via comparison with other industries' ecotoxicology.

• Journal of Korea Trade
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• v.24 no.7
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• pp.1-28
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• 2020

Purpose - This paper empirically investigates the predictors and main determinants of consumers' ratings of mobile applications in the Google Play Store. Using a linear and nonlinear model comparison to identify the function of users' review, in determining application rating across countries, this study estimates the direct effects of users' reviews on the application rating. In addition, extending our modelling into a sentimental analysis, this paper also aims to explore the effects of review polarity and subjectivity on the application rating, followed by an examination of the moderating effect of user reviews on the polarity-rating and subjectivity-rating relationships. Design/methodology - Our empirical model considers nonlinear association as well as linear causality between features and targets. This study employs competing theoretical frameworks - multiple regression, decision-tree and neural network models - to identify the predictors and main determinants of app ratings, using data from the Google Play Store. Using a cross-validation method, our analysis investigates the direct and moderating effects of predictors and main determinants of application ratings in a global app market. Findings - The main findings of this study can be summarized as follows: the number of user's review is positively associated with the ratings of a given app and it positively moderates the polarity-rating relationship. Applying the review polarity measured by a sentimental analysis to the modelling, it was found that the polarity is not significantly associated with the rating. This result best applies to the function of both positive and negative reviews in playing a word-of-mouth role, as well as serving as a channel for communication, leading to product innovation. Originality/value - Applying a proxy measured by binomial figures, previous studies have predominantly focused on positive and negative sentiment in examining the determinants of app ratings, assuming that they are significantly associated. Given the constraints to measurement of sentiment in current research, this paper employs sentimental analysis to measure the real integer for users' polarity and subjectivity. This paper also seeks to compare the suitability of three distinct models - linear regression, decision-tree and neural network models. Although a comparison between methodologies has long been considered important to the empirical approach, it has hitherto been underexplored in studies on the app market.

• Journal of Service Research and Studies
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• v.13 no.3
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• pp.57-69
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• 2023

Early morning delivery possesses distinct characteristics that differentiate it from standard delivery services. This service typically involves delivering products to customers during the early morning hours, primarily before 7 AM. While online early morning delivery offers various advantages from a customer perspective, it also presents challenges that sellers and online shopping companies need to overcome. The early morning delivery market is experiencing significant growth in the online food retail sector, incorporating both PC-based online shopping and mobile shopping. The objective of this research is to identify the factors influencing customer satisfaction and the intention to reuse in the context of early morning delivery for online shopping. To model the online shopping environment with early morning delivery, independent factors were categorized into three types: System Properties, Product Characteristics, and Delivery Characteristics. This study examined the relationships among these three independent factors, the mediating factor of customer satisfaction, and the dependent variable of the intention to reuse. To conduct this research, empirical validation of the research hypotheses was carried out using the final dataset for analysis. Within this study, the previously explored System Properties, Product Characteristics, and Delivery Characteristics were established. Summarizing the findings of the analysis, it was discovered that System Properties and Product Characteristics played a significant role in determining the quality of early morning delivery services for online shopping. While product diversity and convenience had a positive impact, it is noteworthy that Delivery Characteristics did not influence customer satisfaction. Consequently, it can be concluded that there is no effect on the intention to reuse.

• Journal of Intelligence and Information Systems
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• v.29 no.3
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• pp.125-144
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• 2023

Recently, the use of Large Language Models (LLMs) such as ChatGPT has been increasing in various fields such as interactive commerce and mobile financial services. However, LMMs, which are mainly created by learning existing documents, can also learn various human biases inherent in documents. Nevertheless, there have been few comparative studies on the aspects of bias and discrimination in LLMs. The purpose of this study is to examine the existence and extent of nine types of discrimination (Age, Disability status, Gender identity, Nationality, Physical appearance, Race ethnicity, Religion, Socio-economic status, Sexual orientation) in LLMs and suggest ways to improve them. For this purpose, we utilized BBQ (Bias Benchmark for QA), a tool for identifying discrimination, to compare three large-scale language models including ChatGPT, GPT-3, and Bing Chat. As a result of the evaluation, a large number of discriminatory responses were observed in the mega-language models, and the patterns differed depending on the mega-language model. In particular, problems were exposed in elder discrimination and disability discrimination, which are not traditional AI ethics issues such as sexism, racism, and economic inequality, and a new perspective on AI ethics was found. Based on the results of the comparison, this paper describes how to improve and develop large-scale language models in the future.

• Journal of the Korea Society of Computer and Information
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• v.28 no.8
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• pp.21-30
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• 2023

The study proposes a model that utilizes Python-based deep learning text classification techniques to detect the legality of illegal financial advertising posts on the internet. These posts aim to promote unlawful financial activities, including the trading of bank accounts, credit card fraud, cashing out through mobile payments, and the sale of personal credit information. Despite the efforts of financial regulatory authorities, the prevalence of illegal financial activities persists. By applying this proposed model, the intention is to aid in identifying and detecting illicit content in internet-based illegal financial advertisining, thus contributing to the ongoing efforts to combat such activities. The study utilizes convolutional neural networks(CNN) and recurrent neural networks(RNN, LSTM, GRU), which are commonly used text classification techniques. The raw data for the model is based on manually confirmed regulatory judgments. By adjusting the hyperparameters of the Korean natural language processing and deep learning models, the study has achieved an optimized model with the best performance. This research holds significant meaning as it presents a deep learning model for discerning internet illegal financial advertising, which has not been previously explored. Additionally, with an accuracy range of 91.3% to 93.4% in a deep learning model, there is a hopeful anticipation for the practical application of this model in the task of detecting illicit financial advertisements, ultimately contributing to the eradication of such unlawful financial advertisements.

• Journal of Convergence for Information Technology
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• v.12 no.2
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• pp.1-16
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• 2022

Social network service (SNS) using mobile or web is growing rapidly, and the emergence of various platform services is causing innovative changes in social network service (SNS). This study is to identify the target relation factors of social network users and to empirically study the causal relationship of how much these factors affect user satisfaction through pleasure and self-efficacy. To present an effective and efficient development plan in. In order to empirically verify the research model of this study, a survey was conducted with the general public who had experience using social network services (SNS). Path analysis was performed. As a result, it was possible to verify the correlation of the object relational factors on user satisfaction through pleasure and self-efficacy.First, non-excluded had a significant effect on pleasure, but did not significantly affect self-efficacy. Second, stability attachment did not significantly affect both enjoyment and self-efficacy. Third, social ability did not significantly affect both enjoyment and self-efficacy. Fourth, self-centeredness did not have a significant effect on both enjoyment and self-efficacy. Fifth, pleasure had a significant effect on both self-efficacy and user satisfaction. Sixth, self-efficacy had a significant effect on user satisfaction.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.43 no.6
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• pp.873-881
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• 2023

The evolution of civil engineering technology, exemplified by recent milestones like the completion of the Gangnam Global Business Center (GBC), has fostered the construction of expansive civil and architectural structures both above and below the earth's surface. This surge in construction necessitates a commensurate advancement in research and technology pertaining to safety protocols applicable to these vast edifices. Such protocols encompass a spectrum of concerns, ranging from the preemptive mitigation of accidents to the effective management of exigencies such as fires. As the trajectory of construction endeavors continues unabated, encompassing both subterranean and elevated domains, a concomitant imperative emerges to refine the methodologies underpinning precise indoor positioning. To address this need, an innovative web-based simulator has been devised to emulate indoor positioning scenarios for rigorous testing. This research further entails the development of an indoor positioning data Application Programming Interface (API) fortified by Geographic Information System (GIS) spatial operation techniques. This API is anchored in the construction of intricate test data, centered on the spatial layout of building 13 at the Electronics and Telecommunications Research Institute (ETRI). Consequently, the study renders feasible the expeditious provisioning of diverse signal-based and image-based spatial information, pivotal for enhancing the navigational acumen of mobile devices. Path delineation, cellular signal mapping, landmark identification, and ancillary navigational aids are among the manifold datasets promptly furnished by the indoor positioning data API. In summation, this study engenders a crucial leap towards the fortification of safety protocols and navigational precision within the expansive confines of modern architectural wonders.