• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.8
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• pp.3917-3941
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• 2019

The recent interest in artificial intelligence and machine learning has partly contributed to an interest in the use of such approaches for hyperspectral remote sensing (HRS) imagery classification, as evidenced by the increasing number of deep framework with deep convolutional neural networks (CNN) structures proposed in the literature. In these approaches, the assumption of obtaining high quality deep features by using CNN is not always easy and efficient because of the complex data distribution and the limited sample size. In this paper, conventional handcrafted learning-based multi features based on expert knowledge are introduced as the input of a special designed CNN to improve the pixel description and classification performance of HRS imagery. The introduction of these handcrafted features can reduce the complexity of the original HRS data and reduce the sample requirements by eliminating redundant information and improving the starting point of deep feature training. It also provides some concise and effective features that are not readily available from direct training with CNN. Evaluations using three public HRS datasets demonstrate the utility of our proposed method in HRS classification.

• Journal of the Korea Society of Computer and Information
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• v.29 no.3
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• pp.21-41
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• 2024

This study explores the relationship between users' attachment styles and their interactions with ChatGPT (Chat Generative Pre-trained Transformer), an advanced language model developed by OpenAI. As artificial intelligence (AI) becomes increasingly integrated into everyday life, it is essential to understand how individuals with different attachment styles engage with AI chatbots in order to build a better user experience that meets specific user needs and interacts with users in the most ideal way. Grounded in attachment theory from psychology, we are exploring the influence of attachment style on users' interaction with ChatGPT, bridging a significant gap in understanding human-AI interaction. Contrary to expectations, attachment styles did not have a significant impact on ChatGPT usage or reasons for engagement. Regardless of their attachment styles, hesitated to fully trust ChatGPT with critical information, emphasizing the need to address trust issues in AI systems. Additionally, this study uncovers complex patterns of attachment styles, demonstrating their influence on interaction patterns between users and ChatGPT. By focusing on the distinctive dynamics between users and ChatGPT, our aim is to uncover how attachment styles influence these interactions, guiding the development of AI chatbots for personalized user experiences. The introduction of the Perceived Partner Responsiveness Scale serves as a valuable tool to evaluate users' perceptions of ChatGPT's role, shedding light on the anthropomorphism of AI. This study contributes to the wider discussion on human-AI relationships, emphasizing the significance of incorporating emotional intelligence into AI systems for a user-centered future.

• Journal of The Korean Society of Agricultural Engineers
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• v.64 no.6
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• pp.77-86
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• 2022

Various odor reduction systems are being operated at pig houses to improve livestock odor issues. However, the quantitative evaluation of odor reduction efficiency is not sufficiently conducted. The analysis of factors that affect the reduction efficiency also has not been sufficiently conducted. Therefore, in this study, the reduction efficiency of representative odor reduction facilities (bio-curtain, scrubber) operated by domestic pig houses was evaluated. The odor reduction efficiency was evaluated by sampling the air before and after the odor reduction facility in 6 pig houses. Livestock odors were evaluated for complex odors, ammonia, hydrogen sulfide, and VOC. To find factors for reduction efficiency, temperature, humidity, pH of washing resolution, type of washing water, and ventilation rate was measured. As a result, it was found that the scrubber system had the highest reduction efficiency. The reduction efficiency was found to be affected by the scrubber's washing resolution, filler, operating conditions, and size. Bio-curtains may have problems such as deterioration of fan performance due to ventilation fan load, groundwater pollution, and excessive use of groundwater.

• Journal of Internet Computing and Services
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• v.25 no.1
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• pp.91-98
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• 2024

With the advancement of ICT (Information and Communication Technology), searching for judgments through the internet has become increasingly convenient. However, predicting sentencing based on judgments remains a challenging task for individuals. This is because sentencing involves a complex process of applying aggravating and mitigating factors within the framework of legal provisions, and it often depends on the subjective judgment of the judge. Therefore, this research aimed to develop a model for predicting sentencing using artificial intelligence by focusing on structuring the data from judgments, making it suitable for AI applications. Through theoretical and statistical analysis of previous studies, we identified variables with high explanatory power for predicting sentencing. Additionally, by analyzing 50 legal judgments related to serious crimes that are publicly available, we presented a framework for extracting essential information from judgments. This framework encompasses basic case information, sentencing details, reasons for sentencing, the reasons for the determination of the sentence, as well as information about offenders, victims, and accomplices evident within the specific content of the judgments. This research is expected to contribute to the development of artificial intelligence technologies in the field of law in the future.

• Journal of Intelligence and Information Systems
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• v.12 no.3
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• pp.127-142
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• 2006

As products become more complex, short-life cycled and customized, the design efforts require more knowledge-intensive, collaborative, coordinating, and information sharing. By sharing knowledge, information, component and process across different families of products, the product realization process will be more efficient, cost-effective and quick-responsive. The purpose of this paper is to propose an ontology-based product family modeling framework. The ideas of product family, ontology and Semantic Web were investigated in depth. A Semantic Web is originally defined as a web of data that can be processed directly or indirectly by machines, which operates intelligently. A Web Ontology Language (OWL) is designed for use by applications that need to process the content of information instead of just presenting information to humans. For the selected cellular phone product family, ontology was constructed and implemented using prot$\acute{e}$g$\acute{e}$-2000.

• Journal of Intelligence and Information Systems
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• v.11 no.1
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• pp.65-93
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• 2005

Expert system implementation of human expert's diagnosis in art psychotherapy requires extensive knowledge on: (1) characteristics in a drawing; (2) psychological symptoms in a client; (3) relationships between the characteristics and the symptoms; (4) decision process; (5) knowledge elicitation and aquisition methods. Experts from many different fields provide such knowledge, ranging from art therapists who is on the spot, psychiatrists, psychologists, artists to knowledge engineers who know how to implement the decision system to a computer. The problems that make the implementation difficult are the expert's complex decision process and the ambiguity, the inconsistency and even the contradiction in the huge volume of the knowledge. Modeling the expert's decision process, we develope a framework of the system and then analyze and classify the knowledge. With the proposed classification, we present a suitable method of knowledge elicitation and aquisition. Then, we describe the subsets of knowledge in a unified structure using the ontology concept and Protege 2000 as a tool. Finally, we apply the system to a real case to show its usability and suitability.

• Proceedings of the KAIS Fall Conference
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• 2003.11a
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• pp.178-184
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• 2003

Case-based reasoning (CBR) often shows significant promise for improving effectiveness of complex and unstructured decision making. Consequently, it has been applied to various problem-solving areas including manufacturing, finance and marketing. However, the design of appropriate case indexing and retrieval mechanisms to improve the performance of CBR is still challenging issue. Most of previous studies to improve the effectiveness for CBR have focused on the similarity function or optimization of case features and their weights. However, according to some of prior researches, finding the optimal k parameter for k-nearest neighbor (k-NN) is also crucial to improve the performance of CBR system. Nonetheless, there have been few attempts which have tried to optimize the number of neighbors, especially using artificial intelligence (AI) techniques. In this study, we introduce a genetic algorithm (GA) to optimize the number of neighbors to combine. This study applies the new model to the real-world case provided by an online shopping mall in Korea. Experimental results show that a GA-optimized k-NN approach outperforms other AI techniques for purchasing behavior forecasting.

• International Journal of Advanced Culture Technology
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• v.8 no.4
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• pp.271-278
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• 2020

Modern financial services go beyond the stage of internet banking, and new concepts of financial transactions such as Internet of Things, mobile banking, electronic payments, and fintech have emerged. As a result, banks are less influential in financial transactions, and changes are being demanded. In the present era, the basic business of banks has decreased, and it is transforming into a space where both consumer finance work and reside. The bank office stands for the brand image of the bank, and it is represented by trust with customers in the basic business of financial transactions, and the rise in real estate value is a natural social phenomenon due to the nature of the location and location of real estate owned by the bank. The business method and space of the bank office that meets the new paradigm of the modern society is an inefficient space only for the convenience and rest of consumers, but it must be used as a variety of spaces suitable for the region to increase the functional value of the bank office. Through this study, as a convenience space for consumers, various service facilities should be introduced to understand the characteristics of the region as a convenience space for consumers, and various service facilities should be introduced to meet the needs of consumers, and the bank office should be improved as a complex service space for local residents.

• Proceedings of the SAREK Conference
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• 2009.06a
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• pp.1470-1475
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• 2009

Until now for the safety of structures and equipment monitoring technology to measure the amount of the physical, if that is the one, one-point or single-source target is one the most. Therefore, becoming more numerous and complex to measure the amount of physical measurement technology that is comprehensive and complex, multi-source concepts to the monitoring of a multi-sensing technology is required. Have the same characteristics of multi-source multi-use space such as a multi-structure of facilities/equipment is. The people's safety in a multi-use facility will be directly related to life and even a little carelessness can lead to large-scale disaster occurs because of several factors, risks and to manage detect in advance the development of an intelligent monitoring technology is essential. Therefore, this study shows that multiple structures/facilities to improve the quality of human life in research to maintain a safe and comfortable living space for multi-source intelligence to the development of monitoring technology to achieve that goal, and the ubiquitous sensor network system on the basis of the wireless transmission module, and multiple research facilities/equipment for the ultra-small sensors for health monitoring study was performed.

• The Journal of the Convergence on Culture Technology
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• v.5 no.2
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• pp.361-366
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• 2019

The amount of data created in the present intelligence information society is beyond imagination. Big data has a great diversity from every information via SNS and internet to the one created by government and enterprises. This various data is close at hand having infinite value as same as crude oil. Big data analysis and utilization by data mining over every areas in the modern industrial society is getting more important for finding useful correlation and strengthening forecasting power against the future uncertainty. Efficient management and utilization of big data produced by complex modern society will be researched in this paper. Also it addresses strategies and methods for securing overall industrial competitiveness, synergy creation among industries, cost reduction and effective application based on big data in the $4^{th}$ industrial revolution era.