식량 작물의 확보 및 생산량 예측은 국가 발전에 있어 필수적이며, 국가 경제뿐만 아니라 전 세계 식량 안보에 기여 한다. 최근 환경오염으로 인한 이상기후는 식량 작물 생산량에 직ㆍ간접적으로 부정적 영향을 끼치고 있어, 작물 수확량 예측 불확실성이 높아지고 있다. 특히, 노지 작물의 경우 생산량 감소와 품질 저하 문제가 화두 되고 있다. 이러한 문제는 농가들뿐만 아니라 소비자들에게도 큰 피해를 안겨주고 있다. 이러한 생산량 예측 이슈를 해결하기 위해 최근에는 인공지능 기술이 농업 분야에도 활발히 적용되고 있다. 작물 수확량의 정확한 예측을 위한 머신러닝 기반 연구가 집중적으로 수행되고 있다. 따라서, 본 연구에서는 이와 같은 인공지능 기반의 노지 작물 수확량 예측 기술(머신러닝, 딥러닝, 하이브리드 모델 등) 현황 및 작물 수확량에 가장 영향을 많이 끼치는 모델 파라미터 등을 조사하였다.

선박 발전기의 여자기는 출력 단자 전압을 일정하게 유지하기 위하여 여자전류 제어를 통해 자속을 조정한다. 여자기 내부에 있는 전압제어기는 통상적으로 비례 적분 제어방식이 사용되는데 게인과 시정수에 의해 결정되는 응답 특성은 적절치 못한 설정값에 의 해 원하지 않는 출력을 내며 이로 인해 선내 전력의 품질과 안정성을 떨어뜨릴 수 있다. 본 논문에서는 IEEE에서 제공하는 AC4A 타입의 여자기 모델을 통해 얻을 수 있는 안정적인 입출력 데이터를 활용하여 신경망 회로를 학습시킨 후 기존의 비례 적분 제어방식의 전압제 어기를 학습된 신경망 회로 제어기로 대체하여 시뮬레이션을 수행하였다. 그 결과 기존 대비 최대 9.63%까지 오버슈팅이 개선되었으며, 안정적인 응답 특성에 대한 우수성을 확인하였다.

본 연구는 인공지능(AI)을 정치적 리더십에 통합하여 위기 관리 분야 의 의사 결정에 혁신을 일으킬 수 있는지 알아본다. 특히 이 연구는 데 이터 분석, 위기 예측, 국제 관계에서 분야에서 인공지능의 역할을 강조 하면서 정치적 리더십에 있어서도 인공지능의 혁신적인 역량을 발휘할 수 있는지 조사하였다. 인공지능은 신속한 데이터 처리 및 예측 분석과 같은 큰 이점을 제공하지만 개인 정보 보호 및 알고리즘 편견과 같은 윤 리적 문제를 포함한 중대한 문제도 야기한다. 따라서 이 연구는 정치 지 도자들이 인공지능 발전에 대한 최신 정보를 지속적으로 학습하고, 윤리 적 지침을 설정하며, 인공지능에 대한 토론에 대중을 참여시켜야 할 필 요성을 강조한다. 또한 투명성을 우선시함으로써 지도자들은 인공지능의 이점을 활용하여 신뢰할 수 있는 정보에 입각한 정치적 환경을 조성할 수 있다.

췌장낭성병변은 최근 영상기술의 발전으로 우연히 발견되는 비율이 점차 증가하고 있으며, 유병률은 복부컴퓨터단층촬영을 시행한 사람에서 많게는 13.5%까지 보고되었다. 그러나 췌장낭성질환의 정확한 진단은 양성에서 악성 질환까지 다양한 형태로 보일 수 있어 영상학적 진단만으로는 매우 어렵다. 초음파내시경은 췌장낭성병변을 비교적 정확하게 진단하고 치료를 결정하는 데 매우 중요한 도구로 사용된다. 그러나 내시경 초음파는 악성으로 진행 가능한 점액성 췌장낭종과 다른 췌장낭종질환을 구분하는 데 정확도가 65-75%에 불과하다. 인공지능은 대장암, 폐암, 유방암과 같은 여러 종류의 암 진단의 정확도를 향상시키는 데 효과적인 도구로 사용되고 있으며, 최근 연구에서는 췌장낭성병변에서도 점액성 종양과 비점액성 종양을 구분하고 악성으로 진행 위험도를 평가하는 데 도움이 되는 것으로 보고되고 있다. 인공지능의 적용은 영상분석에도 국한되지 않고 최근에는 췌장낭종의 액체 분석, 유전자 분석, confocal laser endomicroscopy 등 다양한 분야에서 활용되고 있으며 기대되는 연구 결과를 발표하고 있다. 인공지능은 의료 분야에서 아직 시작단계에 있어, 임상에 적용하기 위해 적절한 알고리즘을 개발하는 데에는 개발자들의 큰 노력이 필요하다. 그러나 이러한 기술은 앞으로 췌장낭종 병변을 보다 정확하게 진단하고 효과적이고 효율적으로 관리하는 데에 도움을 줄 수 있는 잠재력을 가지고 있다고 하겠다.

This paper investigates how the use of artificial intelligence (AI) in services may potentially have a negative impact on consumer wellbeing due to the fear of being replaced by AI. Drawing on temporal self-appraisal theory, the study proposes that the fear of being replaced by an AI agent (as opposed to a human) has a negative effect on consumers' psychological wellbeing. The research suggests that this fear negatively affects consumers' perceptions of self-continuity, and that self-continuity perceptions mediate the relationship between fear of AI replacement and psychological wellbeing. Furthermore, the study explores how the type of task intelligence replaced by AI, whether thinking or feeling tasks, moderates the effects of AI on self-continuity and wellbeing.

With the evolution of Artificial intelligence (AI), emotional artificial intelligence service agents (AISA) have become common in service industry. However, how artificial empathy of AISA contributes to customer acceptance remains an open question. This study draws on Anthropomorphism Theory and Customer AI Experience Theory to examine whether and how artificial empathy has influence on customer acceptance of AISA. Evidence from three experiments (N=1057) designed by the Experimental vignette method (EVM) shows that: (1) artificial empathy including perspective-taking, empathic concern and emotional contagion has a positive impact on customer acceptance of AISA (study 1); (2) customer AI experience (emotional experience quality, social experience activation and social experience quality) mediates the relationship between artificial empathy and customer acceptance of AISA (study 2); (3) artificial empathy for hedonic (vs. utilitarian) services leads to a stronger effect on customer acceptance of AISA (study 3). This paper enriches our understanding of artificial empathy and provides practical guidance for practitioners strategically managing AISA services in AI-enabled marketing interactions.

Artificial intelligence (AI) is transforming healthcare, yet little is known about how consumers experience and make decisions regarding follow-up care with medical AI. We take an interdisciplinary approach combining behavioral research and neuroscience to examine how anthropomorphism and personalization influence well-being and follow-up decisions. Study 1 found that consumers felt well-being after interacting with a highly personalized interaction, whether human or AI doctor. However, they preferred follow-up visits with the human doctor. Empathy mediated these effects. Study 2 used fMRI to show that the anterior cingulate cortex had greater activation when interacting with the human doctor, indicating more emotional processing and conflict resolution. These findings suggest that medical AI cannot currently replace human doctors, who remain vital for actual medical consultations and treatment. However, consumers viewed AI doctors positively and expressed a belief that AI will enhance well-being. By integrating neuroscience, this research provides biological evidence complementing behavioral findings.

Artificial intelligence (AI) technology is recognized as essential in the 4th industrial revolution (Schwab, 2017), which is capable of interacting with the environment and processing and transforming data information to inform goal-directed behavior (Paschen, Kietzmann, & Kietzmann, 2019). Due to the advances in intelligent systems and the incorporation of AI agents in smart devices, more than eight billion digital voice assistants will be used globally by 2024 (Thormundsson, 2022; Gilkson & Woolley, 2020). For successful and positive consumer-brand relationships, constructs such as trust, satisfaction, and commitment are vital (Garbarino & Johnson, 1999; Nyadzayo & Khajehzadehb, 2016). Unlike humans, Artificial intelligence agents could achieve relationship marketing engagement by encouraging users to anthropomorphize the other parties in their technology-mediated interactions, such as applications like chatbots, virtual assistants, and service robots (Steinhoff et al., 2019). Those applications can also use humanoid traits to engage customers in organizations (van Doorn et al., 2017).

As the use of artificial intelligence (AI) grows, so do the questions regarding this new technology and its potential uses. Among the various possibilities and employment that could be offered by AI is personalized news technology. Nowadays, it is already possible to produce journalistic content through AI (Carlson, 2014; Graefe & Haim, 2018). Digital storytelling has become a reality through automated journalism powered by AI (Caswell & Dörr, 2018; Galily, 2018; Linden, 2017; Thorne, 2020). “Artificial intelligence applies advanced analysis and logic-based techniques, including machine learning, to interpret events, support and automate decisions, and take actions” (Gartner Group, 2019). In personalized news technology, algorithms are responsible for selecting content and sorting it according to the personalization criteria (Powers, 2017). So far, AI has been studied in different fields with distinct research focuses (Loureiro et al., 2021). Studies of news-personalization technologies have mainly focused on research engines and filtering mechanisms (Darvishy et al., 2020; Haim et al., 2017; Manoharan & Senthilkumar, 2020). Few studies examine news aggregators (Haim et al., 2018; Kwak et al., 2021) and the effects of news personalization on audiences (Merten, 2021; Swart, 2021; Thurman et al., 2019), thus demanding further research. AI is an imminent reality for the future, reshaping the news media (Brennen et al., 2022; Linden, 2017; Thorne, 2020). Hence, it is still necessary to investigate the impacts that this technology potentially offers to users. Therefore, the current study seeks to respond to this need to deepen research into the area of news personalization through AI, by analyzing the response of audiences toward current and future technological tendencies. The main aim of this research is to investigate the levels of trust that users have in AI-generated personalized video news.

The following is not a conversation with a bank clerk. " Instead, let me introduce you to customized credit loans," "Do you want me to connect you to the screen of using COVID-19 support funds and checking balance?" These are the contents of consultations with AI chatbots at financial institutions. Chatbot, which used to be an additional tool for adding convenience to life, is now at the center of our lives.

The capability for Artificial Intelligence in the beauty industry is enormous, as customers are demanding increasingly customized offers that only these strategies can offer. However, there is still a scarcity of empirical research on customer experiences enabled by AI, which highlights this research's relevance, which we intend to bridge.

How do people perceive new technology-embedded machines? Based on the previous literature on mind perception, this research proposes how people perceive the mind of machines including artificial intelligence (AI), robots, recommendation systems, chatbots, and self-service technologies (SSTs).

The use of artificial intelligence (AI) service robots is on the rise. With service frontlines gradually shifting to human–robot interactions, the question of whether AI robots should be humanlike or machinelike has emerged. While many firms use robots that resemble humans in their appearance and actions, others use machinelike robots, assuming that very humanlike robots may lead to uncanny valley effects. There is no consensus on whether the anthropomorphism of service robots facilitates or constrains consumers’ experiences. To fill this gap, this article examines when and how service companies should use anthropomorphic AI service robots.

We are living in a world that is increasingly digital and undergoing dramatic changes as a result. In particular for luxury fashion, growing numbers of online customers as well as fast changing business environment, luxury retailers face the challenge of differentiating themselves by offering a better online customer experience (Chen et al. 2021). By doing so, luxury fashion retailers are increasingly deploying chatbots in their service encounters to enhance customer experience (Roy & Naidoo, 2021). Chatbots are powered by Artificial Intelligence (AI) (Hoyer et al. 2020) and are an example of AI robot that can provide human-computer interactions on a retail website (Lee et al. 2017). Intended to enhance the online customer experience, chatbots have the potential to provide a better understanding of the product performance, enable efficient use of customer time, and help build crucial customer relationships (Rese et al. 2020; Wilson-Nash et al. 2020; Xu et al. 2022). Therefore, chatbots’ potential has been highly valued by fashion retail industry and academia (Jiang et al. 2022).

Artificial intelligence (AI) is producing more and more branded content such as image, text, video and sound. This area of so-called generative AI became particularly popular with the public after the launch of ChatGPT. Furthermore, political correctness has been discussed in recent years, since society is becoming increasingly sensitive to certain issues surrounding topics such as racism or gender equality. Therefore, it is more important than ever for brands to communicate in a politically correct way. In the past, humans were responsible for negative brand communication and brand voice. However, with the development of these AI-tools and platforms, AI also creates brand voice and this AI-generated brand voice can similarly cause such negative feelings.

In this study, machine learning models are proposed to predict the Vickers hardness of AlSi10Mg alloys fabricated by laser powder bed fusion (LPBF). A total of 113 utilizable datasets were collected from the literature. The hyperparameters of the machine-learning models were adjusted to select an accurate predictive model. The random forest regression (RFR) model showed the best performance compared to support vector regression, artificial neural networks, and k-nearest neighbors. The variable importance and prediction mechanisms of the RFR were discussed by Shapley additive explanation (SHAP). Aging time had the greatest influence on the Vickers hardness, followed by solution time, solution temperature, layer thickness, scan speed, power, aging temperature, average particle size, and hatching distance. Detailed prediction mechanisms for RFR are analyzed using SHAP dependence plots.

Since high-level radioactive wastes contain long-lived nuclides and emit high energy, they should be disposed of permanently through a deep geological disposal system. In Korea, the first (2016.07) and the second (2021.12) basic plans for the management of high-level disposal systems were proposed to select sites for deep geological disposal facilities and to implement business strategies. Leading countries such as Finland, Sweden and France have developed and applied safety cases to verify the safety of deep geological disposal systems. By examining the regulatory status of foreign leading countries, we analyze the safety cases ranging from the site selection stage of the deep geological disposal system to the securing of the permanent disposal system to the investigation, analysis, evaluation, design, construction, operation, and closure. Based on this analysis, we will develop safety case elements for long-term safety of deep geological disposal systems suitable for domestic situation. To systemically analyze data based on safety cases, we have established a database of deep geological disposal system regulations in leading foreign countries. Artificial intelligence text mining and data visualization techniques are used to provide database in dashboard form rather than simple lists of data items, which is a limitation of existing methods. This allows regulatory developers to understand information more quickly and intuitively and provide a convenient interface so that anyone can easily access the analyzed data and create meaningful information. Furthermore, based on the accumulated bigdata, the artificial intelligence learns and analyzes the information in the database through deep learning, and aims to derive a more accurate safety case. Based on these technologies, this study analyzed the legal systems, regulatory standards, and cases of major international leading countries and international organizations such as the United States, Sweden, Finland, Canada, Switzerland, and the IAEA to establish a database management system. To establish a safety regulation base suitable for the domestic deep geological disposal environment, the database is provided as data to refer to and apply systematic information management on regulatory standards and regulatory cases of overseas leading countries, and it is expected that it will play a key role as a forum for understanding and discussing the level of safety of deep geological disposal system among stakeholders.

Water electrolysis holds great potential as a method for producing renewable hydrogen fuel at large-scale, and to replace the fossil fuels responsible for greenhouse gases emissions and global climate change. To reduce the cost of hydrogen and make it competitive against fossil fuels, the efficiency of green hydrogen production should be maximized. This requires superior electrocatalysts to reduce the reaction energy barriers. The development of catalytic materials has mostly relied on empirical, trial-and-error methods because of the complicated, multidimensional, and dynamic nature of catalysis, requiring significant time and effort to find optimized multicomponent catalysts under a variety of reaction conditions. The ultimate goal for all researchers in the materials science and engineering field is the rational and efficient design of materials with desired performance. Discovering and understanding new catalysts with desired properties is at the heart of materials science research. This process can benefit from machine learning (ML), given the complex nature of catalytic reactions and vast range of candidate materials. This review summarizes recent achievements in catalysts discovery for the hydrogen evolution reaction (HER) and oxygen evolution reaction (OER). The basic concepts of ML algorithms and practical guides for materials scientists are also demonstrated. The challenges and strategies of applying ML are discussed, which should be collaboratively addressed by materials scientists and ML communities. The ultimate integration of ML in catalyst development is expected to accelerate the design, discovery, optimization, and interpretation of superior electrocatalysts, to realize a carbon-free ecosystem based on green hydrogen.