A Dense Reference Network for Mass-Market Centimeter-Accurate Positioning

The quality of atmospheric corrections provided by a dense reference network for centimeter-accurate carrierphase differential GNSS (CDGNSS) positioning is investigated. A dense reference network (less than 20 km inter-station distance) offers significant benefits for mass-market users, enabling lowcost (including single-frequency) CDGNSS positioning with rapid integer ambiguity resolution. Precise positioning on a massmarket platform would significantly influence the world economy, ushering in a host of consumer-focused applications such as globally-registered augmented and virtual reality and improved all-weather safety and efficiency for intelligent transportation systems, applications which have so far been hampered by the several-meter-level errors in standard GNSS positioning. This contribution examines CDGNSS integer ambiguity resolution performance in terms of network correction uncertainty, and network correction uncertainty, in turn, in terms of network density. It considers the total error in network corrections: a sum of ionospheric, tropospheric, and reference station multipath components. The paper’s primary goal is to identify the network density beyond which mass-market users would see no further significant improvement in ambiguity resolution performance. It finishes by describing development and deployment of a low-cost dense reference network in Austin, Texas.Aerospace Engineering and Engineering Mechanic

Predicting and Recovering Link Failure Localization Using Competitive Swarm Optimization for DSR Protocol in MANET

Portable impromptu organization is a self-putting together, major construction-less, independent remote versatile hub that exists without even a trace of a determined base station or government association. MANET requires no extraordinary foundation as the organization is unique. Multicasting is an urgent issue in correspondence organizations. Multicast is one of the effective methods in MANET. In multicasting, information parcels from one hub are communicated to a bunch of recipient hubs all at once, at a similar time. In this research work, Failure Node Detection and Efficient Node Localization in a MANET situation are proposed. Localization in MANET is a main area that attracts significant research interest. Localization is a method to determine the nodes’ location in the communication network. A novel routing algorithm, which is used for Predicting and Recovering Link Failure Localization using a Genetic Algorithm with Competitive Swarm Optimization (PRLFL-GACSO) Algorithm is proposed in this study to calculate and recover link failure in MANET. The process of link failure detection is accomplished using mathematical modelling of the genetic algorithm and the routing is attained using the Competitive Swarm optimization technique. The result proposed MANET method makes use of the CSO algorithm, which facilitates a well-organized packet transfer from the source node to the destination node and enhances DSR routing performance. Based on node movement, link value, and endwise delay, the optimal route is found. The main benefit of the PRLFL-GACSO Algorithm is it achieves multiple optimal solutions over global information. Further, premature convergence is avoided using Competitive Swarm Optimization (CSO). The suggested work is measured based on the Ns simulator. The presentation metrix are PDR, endwise delay, power consumption, hit ratio, etc. The presentation of the proposed method is almost 4% and 5% greater than the present TEA-MDRP, RSTA-AOMDV, and RMQS-ua methods. After, the suggested method attains greater performance for detecting and recovering link failure. In future work, the hybrid multiway routing protocols are presented to provide link failure and route breakages and liability tolerance at the time of node failure, and it also increases the worth of service aspects, respectively

Offline speaker segmentation using genetic algorithms and mutual information

We present an evolutionary approach to speaker segmentation, an activity that is especially important prior to speaker recognition and audio content analysis tasks. Our approach consists of a genetic algorithm (GA), which encodes possible segmentations of an audio record, and a measure of mutual information between the audio data and possible segmentations, which is used as fitness function for the GA. We introduce a compact encoding of the problem into the GA which reduces the length of the GA individuals and improves the GA convergence properties. Our algorithm has been tested on the segmentation of real audio data, and its performance has been compared with several existing algorithms for speaker segmentation, obtaining very good results in all test problems.This work was supported in part by the Universidad de Alcalá under Project UAH PI2005/078

Solomon Islands: Malaita Hub scoping report

The CGIAR Research Program (CRP) Aquatic Agricultural Systems (AAS) will target five countries, including Solomon Islands. The proposed hubs for Solomon Islands were to cover most provinces, referencing the Western, Central and Eastern regions. Scoping of the initial ‘Central’ hub was undertaken in Guadalcanal, Malaita and Central Islands provinces and this report details findings from all three. As scoping progressed however, it was agreed that, based on the AAS context and priority needs of each province and the Program’s capacity for full implementation, the Central Hub would be restricted to Malaita Province only and renamed “Malaita Hub”. Consistent in each AAS country, there are four steps in the program rollout: planning, scoping, diagnosis and design. Rollout of the Program in Solomon Islands began with a five month planning phase between August and December 2011, and scoping of the first hub began in January 2012. This report, the second to be produced during rollout, describes the findings from the scoping process between January and June 2012. This report marks the transition from the scoping phase to the diagnosis phase in which output from scoping was used to develop a hub level theory of change for identifying research opportunities. Subsequent reports detail in-depth analyses of gender, governance, nutrition and partner activities and discuss Program engagement with community members to identify grass-roots demand for research

airline revenue management

With the increasing interest in decision support systems and the continuous advance of computer science, revenue management is a discipline which has received a great deal of interest in recent years. Although revenue management has seen many new applications throughout the years, the main focus of research continues to be the airline industry. Ever since Littlewood (1972) first proposed a solution method for the airline revenue management problem, a variety of solution methods have been introduced. In this paper we will give an overview of the solution methods presented throughout the literature.revenue management;seat inventory control;OR techniques;mathematical programming

Large Wind Energy Converter: Growian 3 MW

The final report on the projected application of larger-scale wind turbine on the northern German coast is summarized. The designs of the tower, machinery housing, rotor, and rotor blades are described accompanied various construction materials are examined. Rotor blade adjustment devices auxiliary and accessory equipment are examined

Joint University Program for Air Transportation Research, 1987

The research conducted during 1987 under the NASA/FAA sponsored Joint University Program for Air Transportation Research is summarized. The Joint University Program is a coordinated set of 3 grants sponsored by NASA-Langley and the FAA, one each with the MIT, Ohio Univ., and Princeton Univ. Completed works, status reports, and annotated bibliographies are presented for research topics, which include computer science, guidance and control theory and practice, aircraft performance, flight dynamics, and applied experimental psychology. An overview of the year's activities for each university is also presented

Data Analytics and Techniques: A Review

Big data of different types, such as texts and images, are rapidly generated from the internet and other applications. Dealing with this data using traditional methods is not practical since it is available in various sizes, types, and processing speed requirements. Therefore, data analytics has become an important tool because only meaningful information is analyzed and extracted, which makes it essential for big data applications to analyze and extract useful information. This paper presents several innovative methods that use data analytics techniques to improve the analysis process and data management. Furthermore, this paper discusses how the revolution of data analytics based on artificial intelligence algorithms might provide improvements for many applications. In addition, critical challenges and research issues were provided based on published paper limitations to help researchers distinguish between various analytics techniques to develop highly consistent, logical, and information-rich analyses based on valuable features. Furthermore, the findings of this paper may be used to identify the best methods in each sector used in these publications, assist future researchers in their studies for more systematic and comprehensive analysis and identify areas for developing a unique or hybrid technique for data analysis

실세계 그래프 특징을 활용한 랜덤 워크 기반 대규모 그래프 마이닝

학위논문(박사)--서울대학교 대학원 :공과대학 컴퓨터공학부,2020. 2. 강유.Numerous real-world relationships are represented as graphs such as social networks, hyperlink networks, and protein interaction networks. Analyzing those networks is important to understand the real-life phenomena. Among various graph analysis techniques, random walk has been widely used in many applications with satisfactory results. However, various real-world graphs are large and complicated with diverse labels. Traditional random walk based methods require heavy computational cost, and disregards those labels for performing random walks; thus, its utilization has been limited in such large and complicated graphs. In this thesis, I handle the technical challenges of mining large real-world graphs based on random walk. Real-world graphs have distinct structural properties which become a basis to increase the performance of the random walk in terms of speed and quality. Based upon this idea, I develop fast, scalable, and exact methods for node ranking using random walk in large-scale plain networks. I also design accurate models using random walks for node ranking and relational reasoning in labeled graphs such as signed networks and knowledge bases. Through extensive experiments on various real-world graphs, I demonstrate the effectiveness of the methods and models proposed by this thesis. The proposed methods process 100 times larger graphs, and require up to 130 times less memory with up to 9 times faster speed compared to other existing methods, successfully scaling to billion-scale graphs. Also, the proposed models substantially improve the predictive performance of a variety of tasks in labeled graphs such as signed networks and knowledge bases.다양한 실세계 자연 현상에서의 관계들은 소셜 네트워크, 하이퍼링크 네트워크와 단백질 상호작용 네트워크와 같이 정점과 간서의 그래프로 표현된다. 이러한 네트워크를 분석하는 것은 실세계의 현상을 이해하는데 매우 중요하다. 다양한 그래프 분석 기법중에 랜덤 워크라는 기법이 만족스러운 성능과 함께 많은 그래프 마이닝 응용에 널리 활용되어 왔다. 그러나 대다수의 실세계 그래프는 그 규모가 굉장히 크고 다양한 라벨 정보와 함께 복잡하게 표현된다. 전통적인 랜덤 워크 기반의 기법들은 계산량이 많이 요구되고, 랜덤 워크를 하는데 있어서 다양한 라벨 정보를 전혀 고려하지 않아 라벨로 표현되는 그래프의 고유한 특성이 무시되게 된다. 그래서 이와 같이 복잡하면서 대규모 그래프에서는 랜덤 워크의 실질적 활용이 제한되어왔다. 본 학위 논문에서는 랜덤 워크 기반의 대규모 실세계 그래프 분석의 기술적 한계를 해결하고자 한다. 실세계 그래프는 고유한 구조적 특징들을 가지고 있으며 이러한 구조적 특징들은 속도와 품질의 측면에서 랜덤 워크의 성능을 향상시키는데 기반이 될 수 있다. 이러한 아이디어를 활용하여, 대규모의 라벨이 없는 일반적인 네트워크에서 랜덤 워크 기반의 개인화된 정점 랭킹 계산을 빠르고, 확장성 있고 정확하게 구하는 기법을 제안한다. 또한 부호화된 네트워크 또는 지식 베이스와 같은 라벨이 있는 그래프에서 개인화된 정점 랭킹과 관계 추론을 위한 랜덤 워크 기반의 모델을 제안한다. 다양한 실세계 그래프에서 광범위한 실험을 통해 본 학위 논문에 의해 제안된 방법과 모델의 효과성을 보인다. 제안하는 방법은 다른 경쟁 기법들과 비교했을 때 최대 100배 더 큰 그래프를 처리할 수 있고, 최대 130배 적게 메모리를 사용하면서, 최대 9배 빠른 속도를 보이며, 결과적으로 수 십억 규모의 그래프에서 랜덤 워크 기반의 개인화된 정점 랭킹을 성공적으로 구할 수 있다. 또한, 제안하는 랜덤 워크 기반의 모델들은 부호화된 네트워크와 지식 베이스와 같은 라벨이 있는 그래프에서 부호 예측, 간선 예측, 이상 현상 탐지, 관계 추론 등의 다양한 응용에서 다른 경쟁 모델들보다 더 좋은 예측 성능을 보인다.Chapter1 Overview .... 1 1.1 Motivation .... 1 1.2 Research Statement .... 4 1.2.1 Research Goals and Importance .... 4 1.2.2 Technical Challenges .... 6 1.2.3 Main Approaches .... 7 1.2.4 Contributions .... 9 1.2.5 Overall Impact .... 10 1.3 Thesis Organization .... 11 Chapter2 Background .... 12 2.1 Definitions .... 12 2.1.1 Notations on Graphs .... 12 2.1.2 Random Walk with Restart .... 13 2.2 Related Works .... 15 2.2.1 Previous Methods for RWR in Plain Graphs .... 15 2.2.2 Ranking Models in Signed Networks .... 17 2.2.3 Relational Reasoning Models in Edge-labeled Graphs .... 19 Chapter 3 Fast and Scalable Ranking in Large-scale Plain Graphs .... 21 3.1 Introduction .... 21 3.2 Preliminaries .... 23 3.2.1 Iterative Methods for RWR .... 24 3.2.2 Preprocessing Methods for RWR .... 25 3.3 Proposed Method .... 26 3.3.1 Overview .... 26 3.3.2 BePI-B: Exploiting Graph Characteristics for Node Reordering and Block Elimination .... 28 3.3.3 BePI-B: Incorporating an Iterative Method into Block Elimination .... 32 3.3.4 BePI-S: Sparsifying the Schur Complement .... 34 3.3.5 BePI: Preconditioning a Linear System for the Iterative Method .... 36 3.4 Theoretical Results .... 39 3.4.1 Time Complexity .... 39 3.4.2 Space Complexity .... 40 3.4.3 Accuracy Bound .... 41 3.4.4 Lemmas and Proofs .... 43 3.5 Experiments .... 48 3.5.1 Experimental Settings .... 49 3.5.2 Preprocessing Cost .... 51 3.5.3 Query Cost .... 53 3.5.4 Scalability .... 53 3.5.5 Effects of Sparse Schur Complement and Preconditioning .... 54 3.5.6 Effects of the Hub Selection Ratio .... 57 3.5.7 Accuracy .... 58 3.5.8 Comparison with the-State-of-the-Art Method .... 59 3.6 Summary .... 60 Chapter 4 Personalized Ranking in Signed Graphs .... 61 4.1 Introduction .... 61 4.2 Problem Definition .... 65 4.3 Proposed Method .... 65 4.3.1 Signed Random Walk with Restart Model .... 66 4.3.2 SRWR-Iter: Iterative Algorithm for Signed Random Walk with Restart .... 76 4.3.3 SRWR-Pre: Preprocessing Algorithm for Signed Random Walk with Restart .... 82 4.4 Experiments .... 93 4.4.1 Experimental Settings .... 94 4.4.2 Link Prediction Task .... 96 4.4.3 User Preference Preservation Task .... 99 4.4.4 Troll Identification Task .... 100 4.4.5 Sign Prediction Task .... 104 4.4.6 Effectiveness of Balance Attenuation Factors .... 109 4.4.7 Performance of SRWR-Pre .... 110 4.5 Summary .... 113 Chapter 5 Relational Reasoning in Edge-labeled Graphs .... 114 5.1 Introduction .... 114 5.2 Preliminary .... 116 5.3 Proposed Method .... 118 5.3.1 Label Transition Observation .... 120 5.3.2 Learning Label Transition Probabilities .... 121 5.3.3 Multi-Labeled Random Walk with Restart .... 123 5.3.4 Formulation for MuRWR .... 125 5.3.5 Algorithm for MuRWR .... 127 5.4 Theoretical Results .... 131 5.4.1 Lemma for Solution of Label Transition Probabilities and Convexity .... 131 5.4.2 Lemma for Recursive Equation of MuRWR Score Matrix .... 134 5.4.3 Lemma for Spectral Radius in Convergence Theorem .... 136 5.4.4 Lemma for Complexity Analysis .... 137 5.5 Experiment .... 138 5.5.1 Experimental Settings .... 139 5.5.2 Relation Inference Task .... 140 5.5.3 Effects of Label Weights in MuRWR .... 142 5.5.4 Effects of Restart Probability in MuRWR .... 143 5.5.5 Convergence of MuRWR .... 144 5.6 Summary .... 145 Chapter6 Future Works .... 146 6.1 Fast and Accurate Pseudoinverse Computation .... 146 6.2 Fast and Scalable Signed Network Generation .... 147 6.3 Disk-based Algorithms for Random Walk .... 147 Chapter7 Conclusion .... 149 References .... 151 Appendix .... 166 A.1 Hub-and-Spoke Reordering Method .... 166 A.2 Time Complexity of Sparse Matrix Multiplication .... 167 A.3 Details of Preconditioned GMRES .... 167 A.4 Detailed Description of Evaluation Metrics .... 170 A.4.1 Link Prediction .... 170 A.4.2 Troll Identification .... 171 A.5 Discussion on Relative Trustworthiness of SRWR .... 173 Abstract in Korean .... 176Docto