AUV based Precise Seabed Mapping with a Wave Energy-harvesting Surface Vehicle

조한길 포항공과대학교 일반대학원 (창의IT융합공학과) 2019 국내박사

The AUVs have a wide range of applications and are being deployed for various purposes in an oceanographic survey, geoscience, military surveillance, and industrial areas. However, in most cases, the AUVs have preprogrammed a plan to follow a preset route of waypoints and there are few reasoning and adapting for changes against an unexpected situation even in case of the commercial AUVs. To reach a higher intelligence level for AUV technology, the AUVs must perceive the surroundings and infer their current states based on the perceived information. For underwater perception, vision-based sensors are widely used but have limits to use in water due to rapid wavelength-dependent attenuation of light by water. With consideration for the water turbidity, sonars are a generic solution for underwater sensing. Compared to vision-based sensors, the lack of information of sonar data is indisputable: the loss of elevation information, perceptual ambiguity, and a high proportion of outlier, which complicate sonar data processing and three-dimensional (3D) map building. Another issue on AUV exploration is about connectivity. The AUVs should be connected to a network for sharing the data obtained from onboard sensors and intervention for high-level work. Therefore the subsea data can be transmitted to the air only via a relay station on the surface such as relay buoys. To overcome the issues, we propose a sustainable connected AUV system that consists of an AUV and surface vehicle. The AUV is able to perceive the environment regardless of water turbidity. The surface vehicle has affordable electrical payload for long-range data communication and maneuvering for relocation. The two vehicles are interlinked via acoustic communication. For the perception, sonar-based mapping is proposed, and for the electrical payload of the surface vehicle, a novel wave energy harvesting device is developed. First, we present a three-dimensional (3D) mapping method in one-way rectilinear scanning with an autonomous underwater vehicle (AUV) equipped with a forward-looking sonar (FLS) and a profiling sonar (PS). Our approach is to use an additional sonar and fuse acoustic measurements provided by the two sonar sensors. The FLS has a high resolution in a horizontal scan but has an uncertainty in the vertical direction. On the other hand, the PS provides a reliable vertical profile but its beam width is extremely narrow. An initial map is generated by the FLS and refined by combining vertical scan data provided by the PS. Second, a novel surface vehicle was proposed to support a long-term survey of AUV by harvesting wave energy. We proposed a wave energy converter called the wave turbine system (WTS) and verified the feasibility of the proposed system. To verify the proposed mechanism and identify the system parameters, we developed a hydrodynamic model for the WTS and simulated its behavior and power generation capability. From the quantitative simulation, optimal system parameters were analyzed. To check the reliability of the simulation result, we carried out verification tests in a water tank, and the simulation result was verified. Finally, The hardware systems for an AUV named Cyclops and an energy-harvesting surface vehicle were developed. The proposed method is implemented in the developed system and to demonstrate the validity and effectiveness of the proposed method, we conducted a series of tests in a water tank and also at sea. The total system was integrated, and validity was demonstrated through the sea trial.

시뮬레이션 기반 프레임워크를 통한 병원 내 환자 이송 프로세스 개선

이효진 포항공과대학교 융합대학원 2024 국내석사

최근, 헬스케어 서비스 품질 개선에 대한 사회적 요구가 증가함에 따라, 이를 위한 다양한 프로세스 개선 연구가 활발하게 진행되고 있다. 본 연구는 이러한 연구분야 중 병원 내 환자 이송 (Intra-hospital Patient Transfer)에 초점을 맞추어, 병원 내에서 환자의 병동 및 진료과 간 이송 프로세스를 다각적으로 분석하였다. 병원 내 환자 이송은 환자의 치료와 생명에 직접적으로 영향을 미칠 수 있기 때문에 이송 프로세스에서 나타나는 문제점을 식별하여 지연점을 해결하는 것이 필수적으로 요구된다. 이를 위해서, 본 연구는 병원 이송 프로세스의 효율성을 향상시키는 대안을 시뮬레이션 기반으로 도출하는 일반화된 프레임워크를 제시함으로써 환자 대기시간 감소라는 목표를 달성하는데 기여하였다. 해당 프레임워크는 크게 3가지 부분으로 구성된다. 첫번째는 데이터 기반으로 대안을 선정하는 것으로 구성된다. 선행연구 문헌조사를 통해 정의된 병원의 이송 프로세스 효율화를 위한 주요 대안을 기반으로, 분석대상 병원에 대해서 각 문제점을 데이터 기반으로 분석하고 대응되는 대안을 선정한다. 주요 대안은 총 6가지로 우선순위에 따른 이송, 동시이송, 거리기반 이송 배정, 이송요원 수 스케줄링, 대기장소 설정, 엘리베이터 운용을 포함한다. 두번째는 현재 병원의 이송 프로세스를 As-Is 시뮬레이션 모델로 구축하고, 실제 현황을 적절히 반영하였는지의 적합성을 검증하는 부분으로 구성된다. 병원의 레이아웃, 자원 (resource) 관련 정보 등을 반영하여 시뮬레이션 환경을 구성하고 이송반 이동시간, 환자 이송시간, 환자 대기시의 지표를 기준으로 t-test를 통해 실제 이송 데이터와의 분포가 유사한지를 검증한다. 마지막 세번째는 As-Is 시뮬레이션 모델을 기반으로, 앞서 선정된 대안을 반영한 시나리오들을 To-Be 시뮬레이션 모델로 구축하는 부분으로 구성된다. 이송 프로세스 효율화에 대한 대안의 효과성을 검증하기 위해 대안들로부터 도출 가능한 시나리오들을 각각 To-Be 시뮬레이션 모델로 구현하고 As-Is 시뮬레이션 모델 대비, 환자 대기시간이 개선되었는지를 비교 분석한다. 위의 일반화된 이송 프로세스 효율화 대안 도출 프레임워크를 적용한 사례연구로써, 국내 일산병원의 이송 프로세스 데이터를 바탕으로 분석을 진행하였고, 병원의 현재 이송 프로세스상 문제점을 식별하고, 적용가능한 대안과 대안의 효과성을 파악할 수 있었다. 결괴적으로, 총 4가지 대안인 환자 우선순위화, 동시이송, 이송요원수 스케줄링, 엘리베이터 운영의 네 가지 대안을 선정하였다. 해당 대안들로부터 도출된 15개의 To-Be 시뮬레이션 시나리오를 통해 단일한 대안들을 시행할 때보다 다수의 대안을 동시에 시행할 때, 대기시간 감소 효과가 높은 것으로 나타났다. 특히, 동시이송과 이송요원수 스케줄링 대안을 이행했을 때, As-Is 모델에 비해 대기시간이 가장 높은 감소 효과를 보이며 약 0.71분 감소하였다. 또한, 본 연구에서는 여러 대안을 동시에 시행하였을 때의 효과성을 파악할 수 있었는데, 4 가지 대안을 모두 시행하는 경우에 각 대안을 3개씩만 시행한 경우보다 상호연관적인 효과로 인해 오히려 대기시간의 감소 효과가 유의미하지 않은 것을 확인하였다. 추가적으로 시간대별 분석 결과를 통해, 오전시간대 (5시-6시)에 다른 시간대 대비, 최대 7분의 높은 대기시간 감소 효과가 도출되어, 본 연구의 목적인 환자 대기시간이 높은 시간대에 대한 효과성을 확인하며 효율적인 대안을 선정할 수 있었다. Recently, there has been an increasing demand for improving service quality in healthcare, leading to various studies on process improvement. This study focuses specifically on optimizing intra-hospital patient transfers, a critical component for facilitating patient transfers within hospital settings. Intra-hospital patient transfer is crucial as it can directly affect patient outcome, making it essential to identify and resolve problems within the process. To address this, this study proposes a generalized framework for deriving simulation-based policies to enhance the efficiency of the hospital’s patient transfer process, contributing to the goal of reducing patient waiting times. The framework is composed of three main parts. In phase 1, policies are derived through a literature review, focusing on key aspects such as patient prioritization, simultaneous transfers, distance-based porter assignment, porter shortage timeslot, porter waiting area designation, and elevator operation. The policies are selected after analyzing each problem corresponding to each policy. In phase 2, an As-Is simulation model is constructed to accurately depict the current state of patient transfer processes within the hospital environment. The suitability of the model is validated using a t-test to compare the distribution of the simulation data with historical patient transfer data based on indicators such as porter travel time, patient transport time, and patient waiting time. Lastly in phase 3, based on the As-Is simulation model, To-Be simulation models are constructed by incorporating the previously selected policies. To verify the effectiveness of these policies, scenarios derived from the policies are implemented in To-Be simulation models, and improvements in patient waiting times are compared against the As-Is simulation model. As a case study, the framework was applied to analyze intra-hospital transfer processes at Ilsan Hospital in South Korea. The current transfer process problems were identified and the effectiveness of the selected policies were evaluated. Four policies were selected: patient prioritization, simultaneous transfers, porter shortage timeslot, and elevator operation. The study’s findings from 15 simulated scenarios demonstrate that certain policies, especially simultaneous transfers and porter shortage timeslot policy, yield significant reductions in waiting times compared to the As-Is model. Interestingly, the most substantial reduction occurs with the implementation of three policies concurrently, rather than all four. Detailed analysis indicates a potential reduction of up to 7 minutes during 5 a.m. and 6 a.m., with minimal improvement observed at other times. This underscores the importance of targeted implementation based on simulation insights rather than using all policies indiscriminately.

Study on the establishment and characterization of a novel CD56dimCD62L+ natural killer cell line with distinct immunostimulatory potential derived from extra-nodal NK/T lymphoma

양현걸 포항공과대학교 일반대학원 (융합생명공학부) 2019 국내박사

자연살해세포는 직접적인 세포 독성과 면역 조절 잠재력을 가진 선천성 림프구이다. 이렇게 특화된 기능들과 몸을 지키는 역할 때문에 자연살해세포에 대한 연구와 활용법에 대한 관심이 커져 왔다. 하지만 높아진 관심에도 불구하고, 자연상태의 자연살해세포들의 특징들, 이를 테면 혈액 속에 적게 존재하고, ex vivo 증식이 제한적이며, 순수한 세포들을 분리하기 위한 기술적 한계 등으로 인해 보다 심도 깊은 연구가 어려웠다. 따라서 영구적인 자연살해세포주를 만드는 것은 이러한 한계들을 극복할 수 있는 해결책이 될 수 있다. 순수한 자연살해세포들을 무한정 공급이 가능하며, 사용하기 쉬울 뿐 아니라, 윤리적 문제에서도 자유롭기 때문에 과학적인 연구뿐 아니라 바이오의학 분야에 있어서도 귀중한 도구로 사용될 수 있다. 연구의 첫번째 파트에서는 새롭게 구축된 자연살해세포주인 NK101을 형태학, 면역표현형, 세포독성, 사이토카인/키모카인 분비의 관점에서 종합적으로 분석하였다. 기본적으로 NK101의 경우 자연적인 자연살해세포와 유사하게 대형과립림프구 세포의 형태와 전형적인 표면 마커 프로필을 보일 뿐 아니라, 독성 과립의 내재 및 자가 변형/손실에 대한 인식 능력과 같은 다른 주요 특징들 역시 가지고 있었다. 흥미롭게도 NK101은 특이적인 CD56dimCD62L+ 표현형을 가지고 있었는데, 이는 자연살해세포의 분화 과정 중, 중간 단계에 해당하는 소그룹의 특징들로 알려져 왔다. 실제로 NK101의 경우 앞서 확인한 면역 표현형뿐만 아니라, 기능적인 부분 역시 CD56dimCD62L+자연살해세포 소그룹을 대표하는 다중 기능 작용기 특성과 유사한 것을 확인되었다. 다시 말해 NK101은 사이토카인 자극에 의해 향상된 분열능력 및 인터페론 감마 분비 촉진을 보일 뿐만 아니라, 암세포를 직접적으로 인지하고 죽일 수 있는 다중 기능 작용기를 가지고 있음을 확인하였다. 이러한 결과들은 NK101이 앞서 언급한 자연적인 자연살해세포들의 여러 한계로 인해 거의 연구되지 못했던 희귀한 다중 기능 자연살해세포 소그룹을 연구하는데 있어 유용한 모델로 쓰일 수 있음을 보여주고 있다. 연구의 두번째 파트에서는 NK101이 종양치료를 위한 세포치료제 플랫폼으로 가능성이 있는지를 연구하였다. 현재까지 임상시험에 들어간 자연살해세포주의 경우 NK-92가 유일하기 때문에, NK101을 세포독성, 사이토카인 분비 특성, 유전자 발현 프로필, 생산성 측면에서 NK-92와 직접적으로 비교하였다. NK101의 경우 NK-92와 비교하여 낮은 세포독성을 보였는데, 이는 상대적으로 낮은 perforin과 granzyme B의 발현 때문으로 보인다. 대신 NK101에서 인터페론 감마 및 TNF-α와 같은 면역 반응을 촉진하는 사이토카인들이 NK-92에 비해 높게 발현되는 것을 확인하였다. 반면, IL-1ra나 IL-10과 같이 면역 반응을 억제하는 사이토카인들의 경우 NK101에서는 거의 발현되지 않는 반면 NK-92에서 매우 높게 발현되었다. 유사한 맥락으로 백혈구의 증식을 긍정적으로 조절하는 유전자들이 NK101에서 높게 발현되는 반면, 반대의 역할, 즉 백혈구의 증식을 억제하는 유전자들의 경우 NK-92에서 높게 발현되는 것을 확인하였다. 이러한 기능성/발현양상의 차이는 면역력이 보존된 4T1 종양 모델에서 잘 나타났다. NK101의 경우 강한 종양-특이적 면역 반응과 함께 NK-92보다 강한 항암 효과를 보였다. 이뿐 아니라 생산성 측면에서 NK-92와 비교해, NK101은 해동 이후 회복이 훨씬 빠를 뿐 아니라, 20일 배양 기준 200배가 넘는 성장 프로필을 보여주었다. 종합적으로, 본 연구는 NK101이라는 새로운 자연살해세포주가 희귀한 CD56dimCD62L+ 소그룹으로서 가지는 차별화된 특징들을 강조할 뿐만 아니라, 이들이 면역항암요법의 새로운 세포치료제로써 가능성이 있음을 시사한다. Natural killer (NK) cells are innate lymphocytes endowed with direct cytotoxicity and immunomodulatory potential. Specialized functions and roles for the host defense gives rise to attention for NK cell study and its applications. However, despite elevated interest in understanding NK cells, characteristics of primary NK cells such as scarcity in blood, limited ex vivo life span, and the technical challenges in isolating pure population constrain further extensive study. Thus, establishing permanent NK cell line could become a solution overcoming those limitations. It is limitless in supply, easy-to-use, no ethical concerns, and homogeneous population, being an invaluable tool not only in scientific research, but also in the field of biomedicine. In the first part of the study, a newly established NK cell line, NK101, was comprehensively characterized with regard to morphology, immunophenotype, cytotoxicity, and cytokines/chemokines secretion. Basically, NK101 resembled major features of natural NK cells including large-granular-lymphocyte morphology, typical surface marker profile, inclusion of cytolytic granules, and capacity of ‘missing-self’ recognition. Interestingly, NK101 had a unique CD56dimCD62L+ phenotype, which has been known as a feature of NK subset in the intermediate stage of differentiation. In agreement with the immunophenotypes, NK101 was verified to have polyfunctional effector properties that are representative of CD56dimCD62L+ NK subset. It displayed enhanced proliferation and interferon-γ secretion upon cytokine stimulation as well as direct cytotoxicity against cancer cells. These findings suggest that NK101 provides a valuable model for studying a unique polyfunctional NK cell subset, which has been little studied due to several limitations of primary NK cells. In the second part of the study, I assessed a potential of NK101 as a cellular platform for cancer treatment. Since NK-92 is only available NK cell line entering clinical trials, NK101 was compared with NK-92 in terms of cytotoxicity, cytokine signature, gene expression profile and manufacturing potential. NK101 expressed lower levels of perforin and granzyme B that correlated with weaker cytotoxicity than NK-92, but produced higher levels of pro-inflammatory cytokines including IFN-γ and TNF-α. On the other hand, anti-inflammatory cytokines such as IL-1 receptor antagonist and IL-10 were highly produced by NK-92, which were nearly undetectable in NK101. Similarly, genes linked to the positive regulation of leukocyte proliferation were enriched in NK101, while those associated with opposite function were highly upregulated in NK-92. Such functional and expressional disparities were well-represented in immunocompetent 4T1 tumor model where NK101 showed more potent anti-tumor effects than those of NK-92, accompanied with stronger tumor-specific immune responses. Regarding manufacturing potential, NK101 not only recovered rapidly after thawing, but also exhibited faster growth profile than NK-92, yielding more than 200-fold higher cell numbers after 20-day culture. Overall, this study not only highlights the distinctive features of a novel NK cell line, NK101, as a unique polyfunctional CD56dimCD62L+ NK subset, but also addresses the capability of NK101 as a new platform for adoptive cancer immunotherapy.

감성분석 기반 7 가지 감정을 반영한 재구매 예측 및 고객 구매행동 분석

박해균 포항공과대학교 융합대학원 2024 국내석사

최근 들어 온라인 플랫폼의 발달로 인해 디지털 커머스(digital commerce)가 크게 성장하고 있다. 이에 따라, 소비자들이 주로 온라인 플랫폼을 통한 구매의사결정을 하면서 온라인 구매리뷰의 중요성이 기업과 소비자 측면에서 대두되고 있는 추세에 있다. 이와 관련하여, 기존의 CSA(Customer Sentimental Analysis)와 재구매예측 선행연구들이 많이 진행되었으나, 각각 긍부정의 단순한 2가지 감정에 대한 분석과 텍스트 데이터나 그 외의 정형 데이터 중 일부만을 활용하는 한계점을 보여왔다. 이를 보완하고자 본 연구에서는 Phase 1의 7가지 감정분류 모델에서 7가지 감정인 분노(Anger), 혐오(Disgust), 두려움(Fear), 기쁨(Happiness), 슬픔(Sadness), 놀람(Surprise), 중립(Neutrality)을 분류하도록 학습하였고 93%의 정확도를 보였다. 이어진 Phase 2의 재구매예측 모델에서는 리뷰 데이터에서 Phase 1을 통해 추출한 7가지 감정을 반영하였고, 감정을 반영하지 않은 모델의 정확도인 89.04%에 비해, 90.25%로 상대적으로 높은 성능의 모델을 도출하였다. 이후 사후분석을 통해, 국내의 가장 대표적인 O2O(Online to Offline) 플랫폼인 네이버 쇼핑의 2024년 실제 데이터에 RFM Framework 기반의 K-Means 클러스터링을 통해 분석하였다. 결과적으로 본 연구는 고객군을 각 감정 점수를 반영하여 세분화함으로써 CRM(Customer Relationship Management) 측면에서 비재구매 고객을 관리하기 위해서 극단적인 부정적 감정에 해당하는 분노, 혐오가 발생하지 않도록 VoC(Voice of Customer)에 대한 처리나, 제품 구매후 서비스(A/S)를 통해 관리하는 것이 중요하다는 점을 제시하였다. The development of online platforms in recent years has catalyzed significant growth in digital commerce. Consequently, the importance of online purchase reviews has become prominent for businesses and consumers alike. However, previous research in Customer Sentiment Analysis (CSA) and Repurchase Prediction has shown limitations, primarily focusing on binary sentiment analysis or utilizing only some textual or structured data. To address these limitations, this study undertakes two phases. In Phase 1, a 7 sentiment classification model is trained to classify seven emotions—Anger, Disgust, Fear, Happiness, Sadness, Surprise and Neutrality—with an accuracy of 93%. Subsequently, in Phase 2, a repurchase prediction model is developed reflecting the seven emotions derived from Phase 1. This model achieves a notable accuracy of 90.25%, outperforming the model that does not incorporate emotions with an accuracy of 89.04%. Following this, in the subsequent post-analysis, RFM Framework-based K-Means clustering is employed on real data from Naver Shopping, a prominent O2O platform in South Korea, from the year 2024. In conclusion, this study highlights the critical role of managing extreme negative emotions such as Anger and Disgust in CRM (Customer Relationship Management) to effectively address non-repurchasing customers. Strategies can include proactive handling of customer dissatisfaction through Voice of Customer (VoC) initiatives and enhancing post-purchase services to optimize product quality.

The Role and Effectiveness of Education in Promoting Digital Inclusion : Focusing on Measuring the Impact of Digital Learnings

한규리 포항공과대학교 융합대학원 2024 국내석사

This study examines the role and effectiveness of education in promoting digital inclusion, focusing on the impact of digital learning programs. The research reveals that a significant portion of civil petitions related to digital issues focuses on the need for better digital education, with 'education' related petitions being the most frequent, particularly among women and middle-aged individuals in their 40s. Factors such as age, gender, region, and education level significantly impact the perceived difficulty of digital learning courses. Middle-aged individuals and women show a notably higher need for digital education. Younger people prioritize acquiring digital skills for academic and professional purposes, while older individuals focus on using digital devices for everyday tasks. Based on these findings, recommends that Digital Learning Center programs be customized to address the unique needs of different demographic groups. Continuous improvement through regular needs assessments is essential to effectively bridge the digital divide. By tailoring educational initiatives to specific personal factors, society can achieve digital inclusion, allowing all citizens to equitably benefit from digital technology. Keywords: Digital Inclusion, Digital Divide, Educational Effectiveness, Policy Effectiveness, Text Mining, Regression Analysis

The Changing Preferences in Job Conditions and Values for Young Adults : on the Influence of Material and Post-Material Values

박세윤 포항공과대학교 융합대학원 2024 국내석사

The Korean young adults’ labor market faces persistent challenges of high unemployment and frequent job turnover. Despite ongoing research on preferred job conditions, there's a gap in understanding how these align with their values. This study aims to bridge this gap by exploring how preferred material and post-material job conditions correlate with changes in young adults' values. Logistic regression analysis using data from the young adults social and economic status survey revealed that personal and economic factors significantly influenced both material and post- material values. However, social factors showed no significant relevance. Keyword network analysis of online data indicated a steady maintenance of mentions of material conditions despite a decrease in material values over time. Conversely, post- material values and preferences for post-material conditions increased annually. These findings suggest a shift towards considering post-material factors alongside material needs. Additionally, wage and experience were key considerations in job decisions. COVID-19 induced workplace conflicts, also leading to a preference for stability. Moreover, changing perceptions of age and a growing emphasis on qualitative job improvements and rest were observed.

An Analysis of South Korea's Discourse on AI : Focusing on International Hegemonic Competition

전솔영 포항공과대학교 융합대학원 2025 국내석사

This study examines South Korea's discourse on artificial intelligence (AI), focusing on its implications within the context of international hegemonic competition. Using advanced text mining techniques, including TF-IDF, K-means clustering, and Latent Dirichlet Allocation (LDA), the research analyzes data from Korean policy research institutions, legislative records, and government-affiliated organizations. The study is conducted in two phases: the first phase explores general discussions on AI, while the second phase focuses specifically on AI's role in security. Findings from the first phase reveal that AI research in South Korea is primarily centered on AI policy and digital transformation, with an emphasis on economic and industrial impacts. The studies highlight the recognition of data as a critical resource and the frequent examination of international case studies, reflecting an awareness of global hegemonic competition, particularly between the United States and China. In the second phase, security-related topics such as semiconductors, space, cybersecurity, energy, and North Korea emerge as key concerns. The analysis underscores three critical insights: (1) the recurrence of longstanding security issues on the Korean Peninsula, now amplified by AI technology, (2) the need for flexible strategies amidst geopolitical tensions between major powers, and (3) the necessity for South Korea to assume a leadership role in AI policy, aligning with nations facing similar challenges. These findings emphasize the urgent need for AI legislation in South Korea as a matter of national security and call for a shift in AI research priorities. Future research and policy must adopt a more comprehensive focus on security, transcending the current economic-centric approach, to position South Korea as a proactive leader in the global AI landscape.

Development of enhanced mRNA delivery systems using end-modified poly(beta-amino ester)s

김성준 포항공과대학교 융합대학원 2025 국내석사

Drug development has historically evolved in the reverse order of the central dogma. Early therapeutics targeted proteins, exemplified by hormone treatments like insulin and antibody- based therapies. Advances in RNA-targeted technologies subsequently enabled the development of RNA-based therapeutics, such as RNA interference and mRNA vaccines, which directly induce protein synthesis in vivo. Most recently, gene editing technologies like CRISPR-Cas9 have opened pathways for DNA-level therapeutics to correct genetic disorders. However, DNA- based therapies face significant challenges, including inefficient nuclear delivery and the risk of permanent genetic alterations, which have positioned RNA-based therapeutics as the primary focus of current drug development. Lipid nanoparticles (LNPs), the leading delivery platform for mRNA therapeutics, including COVID-19 vaccines, have revolutionized the field. However, LNPs have several limitations, such as off-target effects, the requirement for cold chain storage, and the necessity of repeated administration due to limited immune durability. These limitations have driven extensive research into developing alternative non-viral delivery systems that retain LNPs' advantages while overcoming their drawbacks. In this study: • Chapter II presents a polymeric gene delivery system utilizing poly(beta-amino ester) (PBAE) nanoparticles (PNPs) with terminal endcap modifications, addressing the limitations of LNPs in mRNA delivery. • Chapter III introduces PNPs functionalized with polyethylenimine (PEI) at the PBAE ends, achieving high mRNA delivery efficiency with selective targeting to the lungs. In conclusion, this research establishes a PBAE-based drug delivery platform leveraging terminal modification strategies, providing a robust foundation for advancing mRNA therapeutic delivery technologies.

Enhancing RF Transmission and Shielding Performance Using Glass-Based Frequency-Selective Surfaces : RF 전송 및 차폐 능력 개선을 위한 유리 기반 주파수 선택 표면

박승덕 포항공과대학교 융합대학원 2025 국내석사

This dissertation presents the design and application of frequency-selective surfaces (FSS) to enhance electromagnetic shielding and RF transmission in glass-based systems. First, transparent FSS designs utilizing Ag Metal Mesh patterns are developed to address the limitations of traditional shielding methods. The proposed designs achieve high optical transparency (85%) and shielding effectiveness exceeding 50 dB below 1 GHz, while maintaining visibility and enabling precise frequency-selective EMI shielding. Simulation and experimental results confirm the effectiveness of these designs in improving electromagnetic performance. Building on this foundation, the research extends to Low-E glass, which suffers from significant RF signal attenuation due to its metallic coatings, despite its superior thermal insulation. Advanced FSS patterns, including double-grid slots, fractal geometries, and laminated configurations, are applied to enhance RF performance while preserving the thermal and optical properties of Low-E glass. At 4.5 GHz, the designs achieve an absorption rate of 90%, and within the 1.9–2.7 GHz range, transmission efficiencies exceed -3 dB. These findings demonstrate the feasibility of FSS as a practical solution for applications requiring both energy efficiency and electromagnetic functionality.

Factors Influencing Turnover and Job Satisfaction in Large Corporations : Organizational-Level Insights from Employee Online Reviews

류연수 포항공과대학교 융합대학원 소셜데이터사이언스전공 2025 국내석사

This study investigates the factors influencing turnover and job satisfaction in large corporations in South Korea, using organizational-level data derived from online employee reviews. By analyzing 83 companies across various industries, this research focuses on the roles of Perceived Organizational Support (POS), Career Plateau, and Job Satisfaction, with turnover rates measured at the organizational level through sustainability reports. Unlike prior studies that primarily examine turnover at the individual level or rely on turnover intentions as a proxy, this study uses aggregated metrics and actual voluntary turnover rates to provide a more objective and systemic perspective. The findings reveal that while POS and Career Plateau significantly influence Job Satisfaction, their direct effects on turnover are limited in the context of large corporations. This can be attributed to the unique structural and institutional characteristics of these organizations, such as superior salaries, comprehensive welfare policies, and structured career management systems, which mitigate the impact of POS and Career Plateau on turnover. Instead, Job Satisfaction emerges as a critical mediating variable, reinforcing its central role in turnover dynamics. To analyze the data, this study employed a Word2Vec-based vocabulary dictionary and K-Means clustering to process textual reviews from the Blind platform, followed by Structural Equation Modeling (SEM) to test the relationships among variables. The organizational-level focus of this research offers industry-wide insights, avoiding the limitations of small or homogeneous samples. By integrating computational methods with Social Exchange Theory (SET), this study provides a robust framework for examining turnover and job satisfaction in large organizational contexts, offering practical implications for corporate HR practices and future research directions.