Minimum-Fuel Low-Thrust Trajectory Optimization Via a Direct Adaptive Evolutionary Approach

Space missions with low-thrust propulsion systems are of appreciable interest to space agencies because of their practicality due to higher specific impulses. This research proposes a technique to the solution of minimum-fuel non-coplanar orbit transfer problem. A direct adaptive method via Fitness Landscape Analysis (FLA) is coupled with a constrained evolutionary technique to explore the solution space for designing low-thrust orbit transfer trajectories. Taking advantage of the solution for multi-impulse orbit transfer problem, and parameterization of thrust vector, the orbital maneuver is transformed into a constrained continuous optimization problem. A constrained Estimation of Distribution Algorithms (EDA) is utilized to discover optimal transfer trajectories, while maintaining feasibility of the solutions. The low-thrust trajectory optimization problem is characterized via three parameters, referred to as problem identifiers, and the dispersion metric is utilized for analyzing the complexity of the solution domain. Two adaptive operators including the kernel density and outlier detection distance threshold within the framework of the employed EDA are developed, which work based on the landscape feature of the orbit transfer problem. Simulations are proposed to validate the efficacy of the proposed methodology in comparison to the non-adaptive approach. Results indicate that the adaptive approach possesses more feasibility ratio and higher optimality of the obtained solutions.BEAZ Bizkaia, 3/12/DP/2021/00150; SPRI Group, Ekintzaile Program EK-00112-202

Robust Estimation of Distribution Algorithms via Fitness Landscape Analysis for Optimal Low-Thrust Orbital Maneuvers

One particular kind of evolutionary algorithms known as Estimation of Distribution Algorithms (EDAs) has gained the attention of the aerospace industry for its ability to solve nonlinear and complicated problems, particularly in the optimization of space trajectories during on-orbit operations of satellites. This article describes an effective method for optimizing the trajectory of a spacecraft using an evolutionary approach based on EDAs, incorporated with fitness landscape analysis (FLA). The approach utilizes flexible operators that are paired with seeding and selection mechanisms of EDAs. Initially, the orbit transfer problem is mathematically modeled and the objectives and constraints are identified. The landscape feature of the search space is analyzed via the dispersion metric to measure the modality and ruggedness of the search domain. The obtained information are used as feedback in developing adaptive operators for truncation factor and constraints separation threshold of the employed EDA. A framework for spacecraft trajectory optimization has been presented where the dispersion value for a space mission is estimated using a k-nearest neighbors (k-NN) algorithm. The suggested method is used to solve several problems related to low-thrust orbit transfer of satellites in Earth’s orbit. Results demonstrate that the suggested framework for trajectory design and optimization of space transfers is effective enough to offer fuel-efficient and energy-efficient maneuvers for different thrust levels of the propulsion system. Moreover, the performance of the proposed approach is evaluated against non-adaptive EDA and other advanced evolutionary algorithms. The obtained results certify that the proposed adaptive evolutionary approach is superior in identifying feasible minimum-fuel and minimum-energy transfer trajectories.BEAZ Bizkaia, 3/12/DP/2021/00150; SPRI Group, Ekintzaile Program EK-00112-202

Adaptive Estimation of Distribution Algorithms for Low-Thrust Trajectory Optimization

A direct adaptive scheme is presented as an alternative approach for minimum-fuel low-thrust trajectory design in non-coplanar orbit transfers, utilizing fitness landscape analysis (FLA). Spacecraft dynamics is modeled with respect to modified equinoctial elements, considering $J_2$ orbital perturbations. Taking into account the timings of thrust arcs, the discretization nodes for thrust profile, and the solution of multi-impulse orbit transfer, a constrained continuous optimization problem is formed for low-thrust orbital maneuver. An adaptive method within the framework of Estimation of Distribution Algorithms (EDAs) is proposed, which aims at conserving feasibility of the solutions within the search process. Several problem identifiers for low-thrust trajectory optimization are introduced, and the complexity of the solution domain is analyzed by evaluating the landscape feature of the search space via FLA. Two adaptive operators are proposed, which control the search process based on the need for exploration and exploitation of the search domain to achieve optimal transfers. The adaptive operators are implemented in the presented EDA and several perturbed and non-perturbed orbit transfer problems are solved. Results confirm the effectiveness and reliability of the proposed approach in finding optimal low-thrust transfer trajectories.BEAZ Bizkaia, 3/12/DP/2021/00150; SPRI Group, Ekintzaile Program EK-00112-202

Action Recognition in Videos: from Motion Capture Labs to the Web

This paper presents a survey of human action recognition approaches based on visual data recorded from a single video camera. We propose an organizing framework which puts in evidence the evolution of the area, with techniques moving from heavily constrained motion capture scenarios towards more challenging, realistic, "in the wild" videos. The proposed organization is based on the representation used as input for the recognition task, emphasizing the hypothesis assumed and thus, the constraints imposed on the type of video that each technique is able to address. Expliciting the hypothesis and constraints makes the framework particularly useful to select a method, given an application. Another advantage of the proposed organization is that it allows categorizing newest approaches seamlessly with traditional ones, while providing an insightful perspective of the evolution of the action recognition task up to now. That perspective is the basis for the discussion in the end of the paper, where we also present the main open issues in the area.Comment: Preprint submitted to CVIU, survey paper, 46 pages, 2 figures, 4 table

DYNAMIC THRESHOLDING GA-BASED ECG FEATURE SELECTION IN CARDIOVASCULAR DISEASE DIAGNOSIS

Electrocardiogram (ECG) data are usually used to diagnose cardiovascular disease (CVD) with the help of a revolutionary algorithm. Feature selection is a crucial step in the development of accurate and reliable diagnostic models for CVDs. This research introduces the dynamic threshold genetic algorithm (DTGA) algorithm, a type of genetic algorithm that is used for optimization problems and discusses its use in the context of feature selection. This research reveals the success of DTGA in selecting relevant ECG features that ultimately enhance accuracy and efficiency in the diagnosis of CVD. This work also proves the benefits of employing DTGA in clinical practice, including a reduction in the amount of time spent diagnosing patients and an increase in the precision with which individuals who are at risk of CVD can be identified

The Challenge of Machine Learning in Space Weather Nowcasting and Forecasting

The numerous recent breakthroughs in machine learning (ML) make imperative to carefully ponder how the scientific community can benefit from a technology that, although not necessarily new, is today living its golden age. This Grand Challenge review paper is focused on the present and future role of machine learning in space weather. The purpose is twofold. On one hand, we will discuss previous works that use ML for space weather forecasting, focusing in particular on the few areas that have seen most activity: the forecasting of geomagnetic indices, of relativistic electrons at geosynchronous orbits, of solar flares occurrence, of coronal mass ejection propagation time, and of solar wind speed. On the other hand, this paper serves as a gentle introduction to the field of machine learning tailored to the space weather community and as a pointer to a number of open challenges that we believe the community should undertake in the next decade. The recurring themes throughout the review are the need to shift our forecasting paradigm to a probabilistic approach focused on the reliable assessment of uncertainties, and the combination of physics-based and machine learning approaches, known as gray-box.Comment: under revie

ElectroMagnetic Analysis and Fault Injection onto Secure Circuits

International audienceImplementation attacks are a major threat to hardware cryptographic implementations. These attacks exploit the correlation existing between the computed data and variables such as computation time, consumed power, and electromagnetic (EM) emissions. Recently, the EM channel has been proven as an effective passive and active attack technique against secure implementations. In this paper, we review the recent results obtained on this subject, with a particular focus on EM as a fault injection tool

GP Representation Space Reduction Using a Tiered Search Scheme

The size and complexity of a GP representation space is defined by the set of functions and terminals used, the arity of those functions, and the maximal depth of candidate solution trees in the space. Practice has shown that some means to reduce the size or bias the search must be provided. Adaptable Constrained Genetic Programming (ACGP) can discover beneficial substructures and probabilistically bias the search to promote the use of these substructures. ACGP has two operating modes: a more efficient low granularity mode (1st order heuristics) and a less efficient higher granularity mode (2nd order heuristics). Both of these operating modes produce probabilistic models, or heuristics, that bias the search for the solution to the problem at hand. The higher granularity mode should produce better models and thus improve GP performance, but in reality it does not always happen. This research analyzes the two modes, identifies problems and circumstances where the higher granularity search should be advantageous but is not, and then proposes a new methodology that divides the ACGP search into two-tiers. The first tier search exploits the computational efficiency of 1st order ACGP and builds a low granularity probabilistic model. This initial model is then used to condition the higher granularity search. The combined search scheme results in better solution fitness scores and lower computational time compared to a standard GP application or either mode of ACGP alone

Population-based algorithms for improved history matching and uncertainty quantification of Petroleum reservoirs

In modern field management practices, there are two important steps that shed light on a multimillion dollar investment. The first step is history matching where the simulation model is calibrated to reproduce the historical observations from the field. In this inverse problem, different geological and petrophysical properties may provide equally good history matches. Such diverse models are likely to show different production behaviors in future. This ties the history matching with the second step, uncertainty quantification of predictions. Multiple history matched models are essential for a realistic uncertainty estimate of the future field behavior. These two steps facilitate decision making and have a direct impact on technical and financial performance of oil and gas companies. Population-based optimization algorithms have been recently enjoyed growing popularity for solving engineering problems. Population-based systems work with a group of individuals that cooperate and communicate to accomplish a task that is normally beyond the capabilities of each individual. These individuals are deployed with the aim to solve the problem with maximum efficiency. This thesis introduces the application of two novel population-based algorithms for history matching and uncertainty quantification of petroleum reservoir models. Ant colony optimization and differential evolution algorithms are used to search the space of parameters to find multiple history matched models and, using a Bayesian framework, the posterior probability of the models are evaluated for prediction of reservoir performance. It is demonstrated that by bringing latest developments in computer science such as ant colony, differential evolution and multiobjective optimization, we can improve the history matching and uncertainty quantification frameworks. This thesis provides insights into performance of these algorithms in history matching and prediction and develops an understanding of their tuning parameters. The research also brings a comparative study of these methods with a benchmark technique called Neighbourhood Algorithms. This comparison reveals the superiority of the proposed methodologies in various areas such as computational efficiency and match quality

레이저 활성 세포 분리 기기를 이용한 조직 내 세포 분리 및 전장 유전체 및 전사체 분석 기술 개발

학위논문 (박사) -- 서울대학교 대학원 : 공과대학 협동과정 바이오엔지니어링전공, 2020. 8. 권성훈.In this dissertation, Spatially-resolved Laser Activated Cell Sorting (SLACS) technique is introduced, and its applications in genomics and transcriptomics are demonstrated. All biological mass is comprised of biological cells, each of which contain its own multi-billion bytes worth of data from genetic molecules, such as DNA or RNA. After the Human Genome Project sequenced one persons genome in ten years, the massively parallel sequencing technologies that are referred to the next generation sequencing (NGS) sprouted innovations in biology, providing further insights into biology and generating revolutions in diagnostics and therapeutics. However, these technologies were only applicable to pools of heterogeneous genetic molecules, hindering thorough explorations of genetic landscapes in the different cells within a biospecimen. Therefore, efforts to separate each and every cell from the pool of cells have generated numerous single cell isolation methodologies, which can be categorized into three: those that separate cell using microfluidics, microarrays, and optics. Advancement in micro-technologies particularly provided advantages in manipulating single cells because biological cell sizes that usually range from microns to tens of microns. State-of-art cell separation technologies that utilize microfluidic properties were rapidly commercialized, enabling high throughput single cell analysis that can process hundreds to thousands of single cells at a time. These utilize cell dissociation and compartmentalization in a microfluidic chambers or a pico-liter droplets, in which biomolecular techniques can amplify the desired genetic molecules. The amplified products such as the genomes or the transcriptomes of the single cells are sequenced through NGS, providing insights into how the dissociated cells were functioning in the biospecimen. However, the dissociation process of the cells that are originally adhered to each other can be harsh and requires the surface proteins that interact with another to be degraded. This process has raised many doubts on whether the cell state is the same before it is dissociated within a solvent. Therefore, microarrays of chemically synthesized oligonucleotides that can capture the poly adenosine tail, or poly (A) tail, were developed to capture the messenger RNAs (mRNAs) directly from the biological specimens. These technologies, however, require large resolution of the oligonucleotide spots because of the technical limitations in chemical DNA synthesis technologies and cross-contaminations between the spots. Optical separation of the cells from biospecimen has been extensively investigated with conventional laser capture microdissection (LCM) devices that utilize laser to transfer target area of interest to the desired receiver. However, these utilize either ultraviolet (UV) lasers to catapult the desired areas that can be highly damaging to the biomolecules within, or thermoplastics that can be melt down using near-infrared (IR) lasers and transfer the desired region of interest for further biological assays. However the thermoplastic approach often cause cross-contamination and has low throughput because the specimen has to be isolated in a contact manner. In this dissertation, the development of an optical cell sorter, or spatially-resolved laser activated cell sorter (SLACS) that uses pulsed near-IR laser that can optomechanically isolate the cells with low damage and high throughput is described. The engineering process of this novel device and two softwares and their applications in NGS technologies are described. Furthermore, the applications of SLACS for genomics and transcriptomics are demonstrated. Proof-of-concept studies for future applications of SLACS are also described.본 학위 논문에서는 이 논문에서는 SLACS (Spatially-resolved Laser Activated Cell Sorting) 기술이 도입되었으며 유전체학 및 전사체학에 대한 응용이 시연되었다. 모든 생물학적 덩어리는 생물학적 세포로 구성되며, 각각의 세포는 DNA 또는 RNA와 같은 유전자 분자로부터 얻은 수십억 바이트의 데이터를 포함한다. 휴먼 게놈 프로젝트가 10 년 안에 한 사람의 게놈을 시퀀싱 한 후, 차세대 시퀀싱 (NGS)과 관련되는 대규모 병렬 시퀀싱 기술은 생물학의 혁신을 불러 일으켜 생물학에 대한 통찰력을 제공하고 진단 및 치료에서 혁명을 일으켰다. 그러나 이 기술들은 이종 유전자 분자의 풀에만 적용 할 수 있으며, 생물 표본 내의 다른 세포에서 유전자 지형의 철저한 탐색을 방해하였다. 따라서, 세포 풀에서 각각의 모든 세포를 따로 분리하려는 노력은 수많은 단일 세포 분리 방법론을 생성하였으며, 이는 미세유체학, 마이크로 어레이 및 광학을 사용하여 세포를 분리하는 것의 세 가지로 분류 될 수 있다. 일반적으로 수 마이크로미터에서 수십 마이크로미터에 이르는 생물학적 세포 크기 때문에 단일 기술의 진보는 단일 세포 조작에 이점을 제공 하였다. 미세 유체 특성을 이용하는 최첨단 세포 분리 기술이 빠르게 상용화되어 한 번에 수백에서 수천 개의 단일 세포를 처리 할 수 있는 고 처리량 단일 세포 분석이 가능해졌습니다. 이들은 미세 분자 챔버 또는 피코 리터 액적에서 세포 해리 및 구획화를 이용하며, 여기서 생체 분자 기술은 원하는 유전자 분자를 증폭시킬 수 있다. 단일 세포의 게놈 또는 전 사체와 같은 증폭 된 생성물은 NGS를 통해 시퀀싱되어, 해리 된 세포가 생체 시편에서 어떻게 기능하는지에 대한 통찰력을 제공한다. 그러나, 원래 서로 부착 된 세포의 해리 과정은 가혹할 수 있으며, 서로 상호 작용하는 표면 단백질이 분해 될 것을 요구한다. 이 공정은 전지 상태가 용매 내에서 해리되기 전에 동일한 지에 대해 많은 의문을 제기했다. 따라서, 폴리아데노신 꼬리 또는 폴리 (A) 꼬리를 포획 할 수 있는 화학적으로 합성 된 올리고 뉴클레오티드의 마이크로 어레이는 생물학적 표본으로부터 메신저 RNA (mRNA)를 직접 포획하기 위해 개발되었다. 그러나, 이들 기술은 화학적 DNA 합성 기술의 기술적 한계 및 스폿 간의 교차 오염으로 인해 올리고 뉴클레오티드 스폿의 큰 해상도를 요구한다. 생체 시료로부터 세포의 광학적 분리는 관심 대상 영역을 원하는 수신기로 전달하기 위해 레이저를 이용하는 종래의 레이저 캡처 미세 해부 (LCM) 장치로 광범위하게 조사되었다. 그러나, 이들은 자외선 (UV) 레이저를 사용하여 생체 내 분자에 크게 손상을 줄 수있는 원하는 영역을 만들거나 근적외선 (IR) 레이저를 사용하여 녹일 수 있고 추가 생물학적 물질을 위해 원하는 관심 영역을 전달할 수있는 열가소성 수지를 사용한다. 분석. 그러나 열가소성 방식은 종종 교차 오염을 유발하고 시편을 접촉 방식으로 분리해야하기 때문에 처리량이 낮다. 이 논문에서는 광학 셀 분류기 또는 낮은 손상과 높은 처리량으로 셀을 광학적으로 분리 할 수 있는 펄스 형 근적외선 레이저를 사용하는 SLACS (공간적으로 해결 된 레이저 활성화 셀 분류기)의 개발에 대해 설명하였다. 이 새로운 장치의 엔지니어링 프로세스와 NGS 기술의 두 소프트웨어 및 응용 프로그램에 대해 설명하였다. 또한, 게놈 및 전 사체에 대한 SLACS의 적용이 입증되었다. SLACS의 향후 응용에 대한 개념 증명 연구도 설명하였다.CHAPTER 1. INTRODUCTION １ 1.1. Spatially resolved omics for atlasing human cells in the biological circuitry ２ 1.1.1. The emergence of single cell sequencing technologies ３ 1.1.2. Spatially resolved omics technologies and needs for development ７ 1.2. Main Concept: Development of spatially-resolved laser activated cell sorter (SLACS) and compatible omics technologies １４ 1.3. Outline of the dissertation １５ CHAPTER 2. BACKGROUND １６ 2.1. Previous spatial omics technologies １７ 2.1.1. In situ spatial omics technologies １７ 2.1.2. Isolate-and-transfer technologies for spatial omics ２０ 2.2. Commercialized spatial omics technologies ２３ 2.3. Previous research in the group ２５ CHAPTER 3. PLATFORM DEVELOPMENT ２９ 3.1. Development of SLACS and remote selection system ３０ 3.2. Whole genome sequencing strategies for SLACS ３３ 3.3. Whole transcriptome sequencing strategies for SLACS ４２ CHAPTER 4. PLATFORM APPLICATION ４８ 4.1. Applications of SLACS to spatial genomics ４９ 4.2. Applications of SLACS to spatial transcriptomics ６２ 4.3. Applications of OPENchip and future perspectives with SLACS ６５ CHAPTER 5. CONCLUSION AND DISCUSSION ７９ 5.1. Summary of dissertation ８０ 5.2. Comparison with previous technology ８３ 5.3. Limit of the platform ８４ 5.4. Future work ８６ BIBLIOGRAPHY ８８ 국문 초록 ９５Docto