Application of a mobile robot to spatial mapping of radioactive substances in indoor environment

Nuclear medicine requires the use of radioactive substances that can contaminate critical areas (dangerous or hazardous) where the presence of a human must be reduced or avoided. The present work uses a mobile robot in real environment and 3D simulation to develop a method to realize spatial mapping of radioactive substances. The robot should visit all the waypoints arranged in a grid of connectivity that represents the environment. The work presents the methodology to perform the path planning, control and estimation of the robot location. For path planning two methods are approached, one a heuristic method based on observation of problem and another one was carried out an adaptation in the operations of the genetic algorithm. The control of the actuators was based on two methodologies, being the first to follow points and the second to follow trajectories. To locate the real mobile robot, the extended Kalman filter was used to fuse an ultra-wide band sensor with odometry, thus estimating the position and orientation of the mobile agent. The validation of the obtained results occurred using a low cost system with a laser range finder.A medicina nuclear requer o uso de substâncias radioativas que pode vir a contaminar áreas críticas, onde a presença de um ser humano deve ser reduzida ou evitada. O presente trabalho utiliza um robô móvel em ambiente real e em simulação 3D para desenvolver um método para o mapeamento espacial de substâncias radioativas. O robô deve visitar todos os waypoinst dispostos em uma grelha de conectividade que representa o ambiente. O trabalho apresenta a metodologia para realizar o planejamento de rota, controle e estimação da localização do robô. Para o planejamento de rota são abordados dois métodos, um baseado na heurística ao observar o problema e ou outro foi realizado uma adaptação nas operações do algoritmo genético. O controle dos atuadores foi baseado em duas metodologias, sendo a primeira para seguir de pontos e a segunda seguir trajetórias. Para localizar o robô móvel real foi utilizado o filtro de Kalman extendido para a fusão entre um sensor ultra-wide band e odometria, estimando assim a posição e orientação do agente móvel. A validação dos resultados obtidos ocorreu utilizando um sistema de baixo custo com um laser range finder

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available

Chemical Plume Tracing by Discrete Fourier Analysis and Particle Swarm Optimization

A novel methodology for solving the chemical plume tracing problem that utilizes data from a network of stationary sensors has been developed in this study. During a toxic chemical release and dispersion incident, the imperative need of first responders is to determine the physical location of the source of chemical release in the shortest possible time. However, the chemical plume that develops from the source of release may evolve into a highly complex distribution over the entire contaminated region, making chemical plume tracing one of the most challenging problems known to date. In this study, the discrete Fourier series method was applied for re-construction of the contour map representing the concentration distribution of chemical over the contaminated region based on point measurements by sensors in a pre-installed network. Particle Swarm Optimization was then applied to the re-constructed contour map to locate the position of maximal concentration. Such a methodology was found to be highly successful in solving the chemical plume tracing problem via the sensor network approach and thus closes a long-standing gap in the literature. Furthermore, the nature of the methodology is such that a visual of the entire chemical dispersion process is made available during the solution process and this can be beneficial for warning purposes and evacuation planning. In the context of such chemical release scenarios, the algorithm developed in this study is believed to be able to play an instrumental role towards national defense for any country in the world that is subjected to such threats

Design of the Reverse Logistics System for Medical Waste Recycling Part II: Route Optimization with Case Study under COVID-19 Pandemic

Medical waste recycling and treatment has gradually drawn concerns from the whole society, as the amount of medical waste generated is increasing dramatically, especially during the pandemic of COVID-19. To tackle the emerging challenges, this study designs a reverse logistics system architecture with three modules, i.e., medical waste classification & monitoring module, temporary storage & disposal site (disposal site for short) selection module, as well as route optimization module. This overall solution design won the Grand Prize of the "YUNFENG CUP" China National Contest on Green Supply and Reverse Logistics Design ranking 1st. This paper focuses on the design of the route optimization module. In this module, a route optimization problem is designed considering transportation costs and multiple risk costs (e.g., environment risk, population risk, property risk, and other accident-related risks). The Analytic Hierarchy Process is employed to determine the weights for each risk element, and a customized genetic algorithm is developed to solve the route optimization problem. A case study under the COVID-19 pandemic is further provided to verify the proposed model. Limited by length, detailed descriptions of the whole system and the other modules can be found at https://shorturl.at/cdY59.Comment: 6 pages, 4 figures, under review by the 26th IEEE International Conference on Intelligent Transportation Systems (ITSC 2023

by integrating deep learning, mechanistic model and field observations

학위논문(박사) -- 서울대학교대학원 : 농업생명과학대학 협동과정 농림기상학, 2022. 8. Youngryel Ryu.Rice (Oryza sativa) is a vital cereal crop that feeds more than 50% of the world population. However, the traditional anaerobic management leads rice production to consume ~40% of the irrigation water and emit ~10% of the global anthropogenic methane. A new paradigm for sustainable rice farming is urgently required amid challenges from increasing food demand, water scarcity, and reducing greenhouse gases emissions. Rice plants transpire considerable water overnight. Saving nighttime water loss is desirable but first need to understand the underlying mechanism of nocturnal stomatal opening. Apart from the night, optimizing daytime management is pivotal for designing an environmentally sustainable rice farming system. In a long-term strategy, detailed and reliable crop type map is compulsory to upscale new leaf level findings and site level methods to regional or global scale. Therefore, in this dissertation, we improved mechanistic understanding of nocturnal stomatal conductance in rice plants (Chapter II); provided an interdisciplinary and heuristic approach for designing an environmentally sustainable rice farming system with a case study in South Korea (Chapter III); and developed a new crop type referencing method by mining off-the-shelf Google Street View images to map crop types (Chapter IV). In chapter II, we proposed a “coordinated leaf trait” hypothesis to explain the ecological mechanism of nocturnal stomatal conductance (gsn) in rice. We conducted an open-field experiment by applying drought, nutrient deficiency, and the combined drought-nutrient deficiency stress. We found that gsn was neither strongly reduced by drought nor consistently increased by nutrient deficiency. With abiotic stress as a random effect, gsn was strongly positively correlated with nocturnal respiration (Rn). Notably, gsn primed early morning photosynthesis, as follows: Rn (↑) → gsn (↑) → gsd (daytime stomatal conductance) (↑) → A (assimilation) (↑). This photosynthesis priming effect diminished after mid-morning. Leaves were cooled by gsn as follows: gsn (↑) → E (transpiration) (↑) → Tleaf (leaf temperature) (↓). However, our results clearly suggest that evaporative cooling did not reduce Rn cost. Our results indicate that gsn is more closely related to carbon respiration and assimilation than water and nutrient availability, and that leaf trait coordination (Rn − gsn − gsd − A) is likely the primary mechanism controlling gsn. In chapter III, we aimed to increase current crop yield, reduce irrigation water consumption, and tackle the dilemma to simultaneously reducing CH4 and N2O emissions in a flooded rice production system. By proposing a heuristic and holistic method, we optimized farm management beyond previous most emphasized irrigation regimes while also exploring niches from other pivotal options regarding sowing window, fertilization rate, tillage depth, and their interactions. Specifically, we calibrated and validated the process-based DNDC model with five years of eddy covariance observations. The DNDC model later was integrated with the non-dominated sorting genetic algorithm (NSGA-III) to solve the multi-objective optimization problem. We found that the optimized management would maintain or even increase current crop yield to its potential (~10 t/ha) while reducing more than 50% irrigation demand and GHGs (CH4 & N2O) emissions. Our results indicate that earlier sowing window and improvements on irrigation practice together would be pivotal to maximizing crop yield while sustaining environmental benefits. We found that the optimal fraction of non-flooded days was around 54% of growing season length and its optimal temporal distributions were primarily in vegetative stages. Our study shows that the present farm yield (8.3-8.9 t/ha) in study site not only has not achieved its potential level but also comes at a great environmental cost to water resources (604-810 mm/yr) and GHGs emissions (CH4: 186-220 kg C/ha/yr; N2O: 0.3-1.6 kg C/ha/yr). Furthermore, this simple method could further be applied to evaluate the environmental sustainability of a farming system under various climate and local conditions and to guide policymakers and farming practices with comprehensive solutions. In chapter IV, we apply a convolutional neural network (CNN) model to explore the efficacy of automatic ground truthing via Google Street View (GSV) images in two distinct farming regions: Illinois and the Central Valley in California. Ground reference data are an essential prerequisite for supervised crop mapping. The lack of a low-cost and efficient ground referencing method results in pervasively limited reference data and hinders crop classification. In this study, we demonstrate the feasibility and reliability of our new ground referencing technique by performing pixel-based crop mapping at the state level using the cloud-based Google Earth Engine platform. The mapping results are evaluated using the United States Department of Agriculture (USDA) crop data layer (CDL) products. From ~130,000 GSV images, the CNN model identified ~9,400 target crop images. These images are well classified into crop types, including alfalfa, almond, corn, cotton, grape, rice, soybean, and pistachio. The overall GSV image classification accuracy is 92% for the Central Valley and 97% for Illinois. Subsequently, we shifted the image geographical coordinates 2–3 times in a certain direction to produce 31,829 crop reference points: 17,358 in Illinois, and 14,471 in the Central Valley. Evaluation of the mapping results with CDL products revealed satisfactory coherence. GSV-derived mapping results capture the general pattern of crop type distributions for 2011–2019. The overall agreement between CDL products and our mapping results is indicated by R2 values of 0.44–0.99 for the Central Valley and 0.81–0.98 for Illinois. To show the applicational value of the proposed method in other countries, we further mapped rice paddy (2014–2018) in South Korea which yielded fairly well outcomes (R2=0.91). These results indicate that GSV images used with a deep learning model offer an efficient and cost-effective alternative method for ground referencing, in many regions of the world.쌀(오리자 사티바)은 세계 인구의 50% 이상을 먹여 살리는 중요한 곡물 작물이다. 그러나 전통적인 혐기성 관리는 쌀 생산으로 관개수의 40%를 소비하고 전 세계 인공 메탄의 10%를 배출한다. 식량 수요 증가, 물 부족, 온실가스 배출 감소 등의 과제 속에서 지속 가능한 벼농사를 위한 새로운 패러다임이 시급하다. 벼는 하룻밤 사이에 상당한 양의 물을 내뿜는다. 야간 수분 손실을 줄이는 것은 바람직하지만, 먼저 야간 기공 개방의 기본 메커니즘을 이해할 필요가 있다. 야간과 별도로 주간 경영의 최적화는 환경적으로 지속 가능한 벼농사 시스템을 설계하는 데 매우 중요하다. 장기 전략에서, 새로운 잎 수준 발견과 현장 수준 방법을 지역적 또는 전역적 규모로 상향 조정하려면 상세하고 신뢰할 수 있는 작물 유형 맵이 필수적이다. 따라서, 본 논문에서 우리는 벼농사의 야간 기공 전도도에 대한 기계적 이해를 향상시켰다(제2장). 환경적으로 지속 가능한 벼농사 시스템을 설계하기 위한 학제 간 및 휴리스틱 접근법 제공(제3장). 그리고 새로운 작물 유형 참조 방법을 개발했다. 기성품인 Google Street View 이미지를 마이닝하여 자르기 유형을 매핑합니다. 2장에서 우리는 벼의 야행성 기공 전도도(gsn)의 생태학적 메커니즘을 설명하기 위해 "협동된 잎 형질" 가설을 제안했습니다. 가뭄, 영양 결핍 및 가뭄-영양소 결핍 복합 스트레스를 적용하여 노지 실험을 수행했습니다. 우리는 gsn이 가뭄에 의해 크게 감소하지도 않고 영양 결핍에 의해 지속적으로 증가하지도 않는다는 것을 발견했습니다. 무생물적 스트레스를 무작위 효과로 사용하여 gsn은 야간 호흡(Rn)과 강한 양의 상관관계를 보였습니다. 특히, gsn은 Rn(↑) → gsn(↑) → gsd(주간 기공 전도도)(↑) → A(동화)(↑)와 같이 이른 아침 광합성을 프라이밍했습니다. 이 광합성 프라이밍 효과는 오전 중반 이후에 감소했습니다. 잎은 gsn에 의해 다음과 같이 냉각되었습니다: gsn(↑) → E(증산)(↑) → Tleaf(잎 온도)(↓). 그러나 우리의 결과는 증발 냉각이 Rn 비용을 감소시키지 않았다는 것을 분명히 시사합니다. 우리의 결과는 gsn이 물 및 영양소 가용성보다 탄소 호흡 및 동화와 더 밀접하게 관련되어 있으며 잎 형질 조정(Rn - gsn - gsd - A)이 gsn을 제어하는 주요 메커니즘일 가능성이 있음을 나타냅니다. 제3장에서 우리는 현재의 작물 수확량을 늘리고 관개 용수 소비를 줄이며 침수된 쌀 생산 시스템에서 CH4와 N2O 배출량을 동시에 줄이는 딜레마를 해결하는 것을 목표로 했다. 휴리스틱하고 전체론적 방법을 제안함으로써, 우리는 이전에 가장 강조되었던 관개 체제를 넘어 농장 관리를 최적화함과 동시에 파종 창, 수정률, 경작 깊이 및 이들의 상호 작용과 관련된 다른 중추적 옵션의 틈새를 탐색했다. 구체적으로, 우리는 5년간의 와류 공분산 관찰로 프로세스 기반 DNDC 모델을 교정하고 검증했다. DNDC 모델은 나중에 다중 객관적 최적화 문제를 해결하기 위해 비지배적 정렬 유전 알고리듬(NSGA-III)과 통합되었다. 우리는 최적화된 관리를 통해 50% 이상의 관개 수요와 GHG(CH4 & N2O) 배출량을 줄이면서 현재 농작물 수확량을 잠재력(~10t/ha)까지 유지하거나 증가시킬 수 있다는 것을 발견했습니다. 우리의 결과는 더 이른 파종 기간과 관개 관개 관행의 개선이 환경적 이익을 유지하면서 농작물 수확량을 최대화하는 데 중추적일 것이라는 것을 보여준다. 우리는 홍수 없는 날의 최적 부분이 성장기 길이의 약 54%였고 최적의 시간 분포는 주로 식물 단계에 있다는 것을 발견했다. 우리의 연구는 연구 현장의 현재 농장 수확량(8.3-8.9 t/ha)이 잠재적 수준을 달성했을 뿐만 아니라 수자원(604-810 mm/yr)과 GHGs 배출(CH4: 186-220 kg C/ha/yr; N2O: 0.3-1.6 kg C/ha/yr)에 막대한 환경 비용을 초래한다는 것을 보여준다. 또한, 이 간단한 방법은 다양한 기후 및 지역 조건 하에서 농업 시스템의 환경 지속 가능성을 평가하고 정책 입안자와 농업 관행을 포괄적인 해결책으로 안내하는 데 추가로 적용될 수 있다. 제4장에서는 컨볼루션 신경망(CNN) 모델을 적용하여 두 개의 구별되는 농업 지역에서 구글 스트리트 뷰(GSV) 이미지를 통해 자동 지상 트러싱의 효과를 탐구한다. 일리노이와 캘리포니아의 센트럴 밸리. 지상 참조 데이터는 감독된 작물 매핑을 위한 필수 전제 조건이다. 저렴하고 효율적인 지상 참조 방법이 없기 때문에 참조 데이터가 광범위하게 제한되고 작물 분류를 방해한다. 본 연구에서는 클라우드 기반 Google 어스 엔진 플랫폼을 사용하여 상태 수준에서 픽셀 기반 크롭 매핑을 수행하여 새로운 지상 참조 기술의 실현 가능성과 신뢰성을 입증한다. 매핑 결과는 미국 농무부(USDA) 작물 데이터층(CDL) 제품을 사용하여 평가된다. 약 130,000개의 GSV 이미지에서 CNN 모델은 약 9,400개의 목표 크롭 이미지를 식별했다. 이 이미지들은 알팔파, 아몬드, 옥수수, 면화, 포도, 쌀, 콩, 피스타치오 등의 작물 유형으로 잘 분류된다. 전체 GSV 이미지 분류 정확도는 센트럴 밸리의 경우 92%, 일리노이 주의 경우 97%이다. 그 후 이미지 지리적 좌표를 특정 방향으로 2~3회 이동하여 31,829개의 크롭 기준점을 생성했다. 즉, 일리노이에서 17,358개, 센트럴 밸리에서 14,471개였다. CDL 제품으로 매핑 결과를 평가한 결과 만족스러운 일관성이 나타났다. GSV에서 파생된 매핑 결과는 2011-2019년 작물 유형 분포의 일반적인 패턴을 포착한다. CDL 제품과 우리의 매핑 결과 사이의 전체 합치는 센트럴 밸리의 경우 0.44–0.99의 R2 값과 일리노이 주의 경우 0.81–0.98의 R2 값으로 표시된다. 제안된 방법의 다른 국가에서 적용 가치를 보여주기 위해, 꽤 좋은 결과를 얻은 한국의 논(2014–2018)을 추가로 매핑했다(R2=0.91). 이러한 결과는 딥 러닝 모델과 함께 사용되는 GSV 이미지가 세계의 많은 지역에서 지상 참조를 위한 효율적이고 비용 효율적인 대체 방법을 제공한다는 것을 나타낸다.1. Abstract 3 LIST OF FIGURES 9 LIST OF TABLES 13 ACKNOWLEDGEMENTS 14 Chapter I. Introduction 15 1.1. Study Background 15 1.2. Purpose of Research 15 Chapter II. Nocturnal stomatal conductance in rice: a coordinating bridge between prior respiration and photosynthesis next dawn 17 Abstract 17 1. Introduction 18 2. Materials and Methods 22 2.1 Plants and growth conditions 22 2.2 Leaf physiological traits 22 2.3 Rapid A/Ci response curves 24 2.4 Stomatal anatomy measurements 24 2.5 Statistical analyses 24 3. Results 25 3.1 Effects of abiotic stress on leaf traits 25 3.2 Nighttime leaf physiological traits 26 3.3 Significant priming effects of gsn on early morning photosynthesis (~5:00 – 7:00) 27 3.4 Path analyses only support the leaf trait coordination 28 3.5 Impacts of gsn on gsd and Amax under light-saturated conditions 29 3.6 Photosynthesis priming effects not detected after mid-morning (9:00) 31 4. Discussion 32 4.1 Abiotic stress results: Implications for different hypotheses 33 4.2 Enhanced carbon assimilation through coordinated regulation by gsn 34 4.3 Evaporative cooling: Passive thermoregulation via leaf trait coordination 36 References 37 Chapter III. Multi-objective optimization of crop yield, water consumption, and greenhouse gases emissions for sustainable rice production 42 Abstract 42 1. Introduction 43 2. Materials and methods 46 2.1 Study site 46 2.2 DNDC model 46 2.3 In situ data 47 2.4 Multi-objective optimization (MOO) algorithm 48 2.5 DNDC-NSGA-III integration and optimization 48 3. Results 50 3.1 DNDC model validation 50 3.2 The gaps between the current farming outcomes and optimized objectives 53 3.3 Approaching Pareto fronts through the heuristic and holistic management 55 3.4 The gaps between current farming practices to potential crop yield with optimal holistic management 56 4. Discussion 58 4.1 Could heuristic and holistic management increase current rice yield with less irrigation water? 58 4.2 Could heuristic and holistic management simultaneously reduce CH4 and N2O emissions? 59 4.3 Limitations and uncertainties 60 Reference 61 Chapter IV. Exploring Google Street View with Deep Learning for Crop Type Mapping 70 Abstract 70 1. Introduction 71 2. Materials and Methods 74 2.1 Study area 74 2.2 General methodology 75 2.3 Google Street View image collection 76 2.4 CNN model training and validation 77 2.5 Producing ground reference data and quality control 79 2.6 Mapping crop types 80 2.7 Mapping results evaluation 81 2.8 Additional test case 82 3. Results 83 3.1 GSV image classification 83 3.2 Producing ground reference data from classified GSV images 84 3.3 Mapping using the GSV derived ground reference 86 4. Discussion 96 4.1 Can we use GSV images to efficiently produce low-cost, sufficient, and reliable crop type ground reference data covering large areas? 96 4.2 Can we use GSV-derived reference data as “ground truth” to map crop types for large areas spanning many years? 97 Appendix 99 References 105 Chapter V. Conclusions 123 Supplementary Information Chapter II 125 Supplementary Information Chapter III 131 Supplementary Information Chapter IV 135 5. Abstract in Korean 138박

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available

Hybrid approaches for mobile robot navigation

The work described in this thesis contributes to the efficient solution of mobile robot navigation problems. A series of new evolutionary approaches is presented. Two novel evolutionary planners have been developed that reduce the computational overhead in generating plans of mobile robot movements. In comparison with the best-performing evolutionary scheme reported in the literature, the first of the planners significantly reduces the plan calculation time in static environments. The second planner was able to generate avoidance strategies in response to unexpected events arising from the presence of moving obstacles. To overcome limitations in responsiveness and the unrealistic assumptions regarding a priori knowledge that are inherent in planner-based and a vigation systems, subsequent work concentrated on hybrid approaches. These included a reactive component to identify rapidly and autonomously environmental features that were represented by a small number of critical waypoints. Not only is memory usage dramatically reduced by such a simplified representation, but also the calculation time to determine new plans is significantly reduced. Further significant enhancements of this work were firstly, dynamic avoidance to limit the likelihood of potential collisions with moving obstacles and secondly, exploration to identify statistically the dynamic characteristics of the environment. Finally, by retaining more extensive environmental knowledge gained during previous navigation activities, the capability of the hybrid navigation system was enhanced to allow planning to be performed for any start point and goal point