개미알고리즘을 이용한 드론의 제설 경로 최적화

학위논문(석사) -- 서울대학교대학원 : 공과대학 건설환경공학부, 2022.2. 김동규.Drones can overcome the limitation of ground vehicles by replacing the congestion time and allowing rapid service. For sudden snowfall with climate change, a quickly deployed drone can be a flexible alternative considering the deadhead route and the labor costs. The goal of this study is to optimize a drone arc routing problem (D-ARP), servicing the required roads for snow removal. A D-ARP creates computational burden especially in large network. The D-ARP has a large search space due to its exponentially increased candidate route, arc direction decision, and continuous arc space. To reduce the search space, we developed the auxiliary transformation method in ACO algorithm and adopted the random walk method. The contribution of the work is introducing a new problem and optimization approach of D-ARP in snow removal operation and reduce its search space. The optimization results confirmed that the drone travels shorter distance compared to the truck with a reduction of 5% to 22%. Furthermore, even under the length constraint model, the drone shows 4% reduction compared to the truck. The result of the test sets demonstrated that the adopted heuristic algorithm performs well in the large size networks in reasonable time. Based on the results, introducing a drone in snow removal is expected to save the operation cost in practical terms.드론은 혼잡시간대를 대체하고 빠른 서비스를 가능하게 함으로써 지상차량의 한계를 극복할 수 있다. 최근 기후변화에 따른 갑작스런 강설의 경우에, 드론과 같이 빠르게 투입할 수 있는 서비스는 운행 경로와 노동비용을 고려했을 때도 유연한 운영 옵션이 될 수 있다. 본 연구의 목적은 드론 아크 라우팅(D-ARP)을 최적화하는 것이며, 이는 제설에 필요한 도로를 서비스하는 경로를 탐색하는 것이다. 드론 아크 라우팅은 특히 큰 네트워크에서 컴퓨터 부하를 생성한다. 다시 말해D-ARP는 큰 검색공간을 필요로 하며, 이는 기하급수적으로 증가하는 후보 경로 및 호의 방향 결정 그리고 연속적인 호의 공간으로부터 기인한다. 검색공간을 줄이기 위해, 우리는 개미알고리즘에 보조변환방법을 적용하는 방안을 도입하였으며 또한 랜덤워크 기법을 채택하였다. 본 연구의 기여는 제설 운영에 있어 D-ARP라는 새로운 문제를 설정하고 최적화 접근법을 도입하였으며 검색공간을 최소화한 것이다. 최적화 결과, 드론은 지상트럭에 비해 약 5% ~ 22%의 경로 비용 감소를 보였다. 나아가 길이 제약 모델에서도 드론은 4%의 비용 감소를 보였다. 또한 실험결과는 적용한 휴리스틱 알고리즘이 큰 네트워크에서도 합리적 시간 내에 최적해를 찾음을 입증하였다. 이러한 결과를 바탕으로, 드론을 제설에 도입하는 것은 미래에 제설 운영 비용을 실질적으로 감소시킬 것으로 기대된다.Chapter 1. Introduction 4 1.1. Study Background 4 1.2. Purpose of Research 6 Chapter 2. Literature Review 7 2.1. Drone Arc Routing problem 7 2.2. Snow Removal Routing Problem 8 2.3. The Classic ARPs and Algorithms 9 2.4. Large Search Space and Arc direction 11 Chapter 3. Method 13 3.1. Problem Statement 13 3.2. Formulation 16 Chapter 4. Algorithm 17 4.1. Overview 17 4.2. Auxilary Transformation Method 18 4.3. Ant Colony Optimization (ACO) 20 4.4. Post Process for Arc Direction Decision 23 4.5. Length Constraint and Random Walk 24 Chapter 5. Results 27 5.1. Application in Toy Network 27 5.2. Application in Real-world Networks 29 5.3. Application of the Refill Constraint in Seoul 31 Chapter 6. Conclusion 34 References 35 Acknowledgment 40석

2기와 3기 위암의 분자유전학적 특성과 종양 면역 미세 환경에 대한 통합적 분석

학위논문 (박사)-- 서울대학교 대학원 : 의과대학 의학과, 2018. 2. 김우호.Tumor microenvironment immune type (TMIT) is the novel classification scheme based on both the expression of PD-L1 and density of CD8-positive tumor infiltrating lymphocytes. We aimed to apply this classification in stage II and III gastric cancer (GC) patients and assess the prognostic and molecular genetic implications of this classification. A total of 392 Stage II and III GC patients who were treated by curative surgical resection followed by 5-fluorouracil based adjuvant chemotherapy in Seoul National University Bundang Hospital were included in this study. Tissue microarrays were constructed from the formalin fixed paraffin embedded tissue samples, and the clinical information were collected retrospectively. Based on the immunohistochemistry (IHC) results of PD-L1 and CD8, TMIT classification of GC was performed as follows: type I (PD-L1+/CD8High), type II (PD-L1-/CD8Low), type III (PD-L1+/CD8Low), type IV (PD-L1-/CD8High). The clinicopathologic features including overall survival according to these four types were analyzed for the evaluation of prognostic performance of TMIT. For the comprehensive assessment of molecular characteristics of GC in immuno-oncology related perspective, IHC for tumor infiltrating immune cell markers (CD8, Foxp3), markers for epithelial-mesenchymal transition (E-cadherin, vimentin), markers representing cancer stem cells (CD44, Sox2, CD133, OCT3/4), as well as EBV in situ hybridization and microsatellite instability testings were performed. To elucidate the possible relationship between mutational profiles of GC and immune microenvironment, we analyzed gene expression data and clinical information from two publicly available transcriptome database. In addition, we performed deep targeted sequencing on 80 selected cases from all four TMITs, using the targeted sequencing panel of 170 recurrently mutated genes in various types of solid tumors. I have found that EBV+ and MSI-H GCs are distinct subtypes that are tightly associated with TMIT I (PD-L1+/CD8High), and OS within the CD8High group differs according to PD-L1 expression. Therefore, I conclude that co-assessment of PD-L1 and CD8+ TILs is clinically relevant, has a possible prognostic role, and warrants further investigation as a predictive marker for immune checkpoint blockade. Moreover, I have found an inverse association between EMT phenotype and PD-L1 expression, and close association between EMT features and TMIT II in GCs, which are the opposite results compared to other types of solid tumors. Additional TMIT-associated tumor characteristics include cancer stemess: I have found a tight association between CD44 positivity, a cancer stem cell marker, and TMIT I phenotype, which is consistent with recent findings that CD44+ tumor cells play important roles on cancer progression by expressing PD-L1. Finally, by performing deep targeted sequencing on selected GC tissue samples, I have found that TMIT I tumors have more numbers of somatic mutations compared to other groups and are enriched with somatic mutations of major cancer related genes including PIK3CA. TMIT II tumors were enriched with mutations of RUNX1 gene, and NTRK3 mutations were relatively specific to TMIT IV. TMIT III had unique somatic mutational profile, harbouring mutations of genes such as APC, TSC1, JAK1, MET, HRAS and RHEB. Clustering analysis based on somatic mutational profiles have identified two groups, one with higher mutational burden (cluster 1) and the other with lower (cluster 2)cluster 1 had significant association with MSI-H GCs and showed the slight tendency of shorter overall survival. Recent advances of immunotherapy in solid tumors have facilitated the search for valuable predictive factor for favorable treatment outcome. TMIT was developed for better understanding of immune microenvironment and more effective immune treatment strategy. Based on the findings from this study, we conclude that application of TMIT classification in GC would be helpful for selecting the patients who would have favorable response to immunotherapy, and that this classification could be utilized as the significant prognostic indicator in stage II and III GC. By clarifying the relationship between molecular profile and microenvironment of GC, we expect to have clues for deeper understanding of the pathogenesis of GC as well as the oncogenesis and progression of other types of solid tumor.Chapter 1. Introduction 1 1.1 Disease burden of gastric cancer 1 1.2 Gastric cancer as a candidate for immunotherapy 1 1.3 Emergence of novel classification: Tumor microenvironment immune type (TMIT) 2 Chapter 2. Materials and Methods 6 2.1 Patients and samples 6 2.2 Immunohistochemistry 7 2.3 In situ hybridization 8 2.4 Microsatellite instability testing 9 2.5 Processing and analysis of publicly available gene expression data 9 2.6 Deep targeted sequencing using cancer-related gene panel 10 2.7 Statistical analysis 12 Chapter 3. Results 14 3.1 Clinicopathologic characteristics 14 3.2 TMIT in stage II and III GC cohort 19 3.3 IHC based molecular classification and TMIT 22 3.4 Analysis of prognostic significance 25 3.5 Analysis of EMT and cancer stem cell markers by IHC 30 3.6 Targeted sequencing of cancer-related genes in stage II and III GC 34 Chapter 4. Discussion 50 4.1 Molecular biologic and clinical significance of TMIT 50 4.2 Somatic mutational profiles of stage II and III gastric cancer 56 4.3 Conclusive remarks 59 Bibliography 61 Abstract in Korean 73Docto