1,408 research outputs found
Improvement of Geometric Quality Inspection and Process Efficiency in Additive Manufacturing
Additive manufacturing (AM) has been known for its ability of producing complex geometries in flexible production environments. In recent decades, it has attracted increasing attention and interest of different industrial sectors. However, there are still some technical challenges hindering the wide application of AM. One major barrier is the limited dimensional accuracy of AM produced parts, especially for industrial sectors such as aerospace and biomedical engineering, where high geometric accuracy is required. Nevertheless, traditional quality inspection techniques might not perform well due to the complexity and flexibility of AM fabricated parts. Another issue, which is brought up from the growing demand for large-scale 3D printing in these industry sectors, is the limited fabrication speed of AM processes. However, how to improve the fabrication efficiency without sacrificing the geometric quality is still a challenging problem that has not been well addressed. In this work, new geometric inspection methods are proposed for both offline and online inspection paradigms, and a layer-by-layer toolpath optimization model is proposed to further improve the fabrication efficiency of AM processes without degrading the resolution. First, a novel Location-Orientation-Shape (LOS) distribution derived from 3D scanning output is proposed to improve the offline inspection in detecting and distinguishing positional and dimensional non-conformities of features. Second, the online geometric inspection is improved by a multi-resolution alignment and inspection framework based on wavelet decomposition and design of experiments (DOE). The new framework is able to improve the alignment accuracy and to distinguish different sources of error based on the shape deviation of each layer. In addition, a quickest change point detection method is used to identify the layer where the earliest change of systematic deviation distribution occurs during the printing process. Third, to further improve the printing efficiency without sacrificing the quality of each layer, a toolpath allocation and scheduling optimization model is proposed based on a concurrent AM process that allows multiple extruders to work collaboratively on the same layer. For each perspective of improvements, numerical studies are provided to emphasize the theoretical and practical meanings of proposed methodologies
Recommended from our members
Holoscopic 3D imaging and display technology: Camera/ processing/ display
This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonHoloscopic 3D imaging βIntegral imagingβ was first proposed by Lippmann in 1908. It has become an attractive technique for creating full colour 3D scene that exists in space. It promotes a single camera aperture for recording spatial information of a real scene and it uses a regularly spaced microlens arrays to simulate the principle of Flyβs eye technique, which creates physical duplicates of light field βtrue 3D-imaging techniqueβ.
While stereoscopic and multiview 3D imaging systems which simulate human eye technique are widely available in the commercial market, holoscopic 3D imaging technology is still in the research phase. The aim of this research is to investigate spatial resolution of holoscopic 3D imaging and display technology, which includes holoscopic 3D camera, processing and display.
Smart microlens array architecture is proposed that doubles spatial resolution of holoscopic 3D camera horizontally by trading horizontal and vertical resolutions. In particular, it overcomes unbalanced pixel aspect ratio of unidirectional holoscopic 3D images. In addition, omnidirectional holoscopic 3D computer graphics rendering techniques are proposed that simplify the rendering complexity and facilitate holoscopic 3D content generation.
Holoscopic 3D image stitching algorithm is proposed that widens overall viewing angle of holoscopic 3D camera aperture and pre-processing of holoscopic 3D image filters are proposed for spatial data alignment and 3D image data processing. In addition, Dynamic hyperlinker tool is developed that offers interactive holoscopic 3D video content search-ability and browse-ability.
Novel pixel mapping techniques are proposed that improves spatial resolution and visual definition in space. For instance, 4D-DSPM enhances 3D pixels per inch from 44 3D-PPIs to 176 3D-PPIs horizontally and achieves spatial resolution of 1365 Γ 384 3D-Pixels whereas the traditional spatial resolution is 341 Γ 1536 3D-Pixels. In addition distributed pixel mapping is proposed that improves quality of holoscopic 3D scene in space by creating RGB-colour channel elemental images
The Revisiting Problem in Simultaneous Localization and Mapping: A Survey on Visual Loop Closure Detection
Where am I? This is one of the most critical questions that any intelligent
system should answer to decide whether it navigates to a previously visited
area. This problem has long been acknowledged for its challenging nature in
simultaneous localization and mapping (SLAM), wherein the robot needs to
correctly associate the incoming sensory data to the database allowing
consistent map generation. The significant advances in computer vision achieved
over the last 20 years, the increased computational power, and the growing
demand for long-term exploration contributed to efficiently performing such a
complex task with inexpensive perception sensors. In this article, visual loop
closure detection, which formulates a solution based solely on appearance input
data, is surveyed. We start by briefly introducing place recognition and SLAM
concepts in robotics. Then, we describe a loop closure detection system's
structure, covering an extensive collection of topics, including the feature
extraction, the environment representation, the decision-making step, and the
evaluation process. We conclude by discussing open and new research challenges,
particularly concerning the robustness in dynamic environments, the
computational complexity, and scalability in long-term operations. The article
aims to serve as a tutorial and a position paper for newcomers to visual loop
closure detection.Comment: 25 pages, 15 figure
Multimedia
The nowadays ubiquitous and effortless digital data capture and processing capabilities offered by the majority of devices, lead to an unprecedented penetration of multimedia content in our everyday life. To make the most of this phenomenon, the rapidly increasing volume and usage of digitised content requires constant re-evaluation and adaptation of multimedia methodologies, in order to meet the relentless change of requirements from both the user and system perspectives. Advances in Multimedia provides readers with an overview of the ever-growing field of multimedia by bringing together various research studies and surveys from different subfields that point out such important aspects. Some of the main topics that this book deals with include: multimedia management in peer-to-peer structures & wireless networks, security characteristics in multimedia, semantic gap bridging for multimedia content and novel multimedia applications
Robust Mobile Visual Recognition System: From Bag of Visual Words to Deep Learning
With billions of images captured by mobile users everyday, automatically recognizing contents in such images has become a particularly important feature for various mobile apps, including augmented reality, product search, visual-based authentication etc. Traditionally, a client-server architecture is adopted such that the mobile client sends captured images/video frames to a cloud server, which runs a set of task-specific computer vision algorithms and sends back the recognition results. However, such scheme may cause problems related to user privacy, network stability/availability and device energy.In this dissertation, we investigate the problem of building a robust mobile visual recognition system that achieves high accuracy, low latency, low energy cost and privacy protection. Generally, we study two broad types of recognition methods: the bag of visual words (BOVW) based retrieval methods, which search the nearest neighbor image to a query image, and the state-of-the-art deep learning based methods, which recognize a given image using a trained deep neural network. The challenges of deploying BOVW based retrieval methods include: size of indexed image database, query latency, feature extraction efficiency and re-ranking performance. To address such challenges, we first proposed EMOD which enables efficient on-device image retrieval on a downloaded context-dependent partial image database. The efficiency is achieved by analyzing the BOVW processing pipeline and optimizing each module with algorithmic improvement.Recent deep learning based recognition approaches have been shown to greatly exceed the performance of traditional approaches. We identify several challenges of applying deep learning based recognition methods on mobile scenarios, namely energy efficiency and privacy protection for real-time visual processing, and mobile visual domain biases. Thus, we proposed two techniques to address them, (i) efficiently splitting the workload across heterogeneous computing resources, i.e., mobile devices and the cloud using our Moca framework, and (ii) using mobile visual domain adaptation as proposed in our collaborative edge-mediated platform DeepCham. Our extensive experiments on large-scale benchmark datasets and off-the-shelf mobile devices show our solutions provide better results than the state-of-the-art solutions
How to exploit the Social Internet of Things: Query Generation Model and Device Profilesβ Dataset
The future Internet of Things (IoT) will be characterized by an increasing number of object-to-object interactions for the implementation of distributed applications running in smart environments. The Social IoT (SIoT) is one of the possible paradigms that is proposed to make the objectsβ interactions easier by facilitating the search of services and the management of objectsβ trustworthiness. In this scenario, we address the issue of modeling the queries that are generated by the objects when fulfilling applicationsβ requests that could be provided by any of the peers in the SIoT. To this, the defined model takes into account the objectsβ major features in terms of typology and associated functionalities, and the characteristics of the applications. We have then generated a dataset, by extracting objectsβ information and positions from the city of Santander in Spain. We have classified all the available devices according to the FIWARE Data Models, so as to enable the portability of the dataset among different platforms. The dataset and the proposed query generation model are made available to the research community to study the navigability of the SIoT network, with an application also to other IoT networks. Experimental analyses have also been conducted, which give some key insights on the impact of the query model parameters on the average number of hops needed for each search
λμ©λ λ°μ΄ν° νμμ μν μ μ§μ μκ°ν μμ€ν μ€κ³
νμλ
Όλ¬Έ(λ°μ¬)--μμΈλνκ΅ λνμ :곡과λν μ»΄ν¨ν°κ³΅νλΆ,2020. 2. μμ§μ±.Understanding data through interactive visualization, also known as visual analytics, is a common and necessary practice in modern data science. However, as data sizes have increased at unprecedented rates, the computation latency of visualization systems becomes a significant hurdle to visual analytics. The goal of this dissertation is to design a series of systems for progressive visual analytics (PVA)βa visual analytics paradigm that can provide intermediate results during computation and allow visual exploration of these resultsβto address the scalability hurdle. To support the interactive exploration of data with billions of records, we first introduce SwiftTuna, an interactive visualization system with scalable visualization and computation components. Our performance benchmark demonstrates that it can handle data with four billion records, giving responsive feedback every few seconds without precomputation. Second, we present PANENE, a progressive algorithm for the Approximate k-Nearest Neighbor (AKNN) problem. PANENE brings useful machine learning methods into visual analytics, which has been challenging due to their long initial latency resulting from AKNN computation. In particular, we accelerate t-Distributed Stochastic Neighbor Embedding (t-SNE), a popular non-linear dimensionality reduction technique, which enables the responsive visualization of data with a few hundred columns. Each of these two contributions aims to address the scalability issues stemming from a large number of rows or columns in data, respectively. Third, from the users' perspective, we focus on improving the trustworthiness of intermediate knowledge gained from uncertain results in PVA. We propose a novel PVA concept, Progressive Visual Analytics with Safeguards, and introduce PVA-Guards, safeguards people can leave on uncertain intermediate knowledge that needs to be verified. We also present a proof-of-concept system, ProReveal, designed and developed to integrate seven safeguards into progressive data exploration. Our user study demonstrates that people not only successfully created PVA-Guards on ProReveal but also voluntarily used PVA-Guards to manage the uncertainty of their knowledge. Finally, summarizing the three studies, we discuss design challenges for progressive systems as well as future research agendas for PVA.νλ λ°μ΄ν° μ¬μ΄μΈμ€μμ μΈν°λν°λΈν μκ°νλ₯Ό ν΅ν΄ λ°μ΄ν°λ₯Ό μ΄ν΄νλ κ²μ νμμ μΈ λΆμ λ°©λ² μ€ νλμ΄λ€. κ·Έλ¬λ, μ΅κ·Ό λ°μ΄ν°μ ν¬κΈ°κ° νλ°μ μΌλ‘ μ¦κ°νλ©΄μ λ°μ΄ν° ν¬κΈ°λ‘ μΈν΄ λ°μνλ μ§μ° μκ°μ΄ μΈν°λν°λΈν μκ°μ λΆμμ ν° κ±Έλ¦Όλμ΄ λμλ€. λ³Έ μ°κ΅¬μμλ μ΄λ¬ν νμ₯μ± λ¬Έμ λ₯Ό ν΄κ²°νκΈ° μν΄ μ μ§μ μκ°μ λΆμ(Progressive Visual Analytics)μ μ§μνλ μΌλ ¨μ μμ€ν
μ λμμΈνκ³ κ°λ°νλ€. μ΄λ¬ν μ μ§μ μκ°μ λΆμ μμ€ν
μ λ°μ΄ν° μ²λ¦¬κ° μμ ν λλμ§ μλλΌλ μ€κ° λΆμ κ²°κ³Όλ₯Ό μ¬μ©μμκ² μ 곡ν¨μΌλ‘μ¨ λ°μ΄ν°μ ν¬κΈ°λ‘ μΈν΄ λ°μνλ μ§μ° μκ° λ¬Έμ λ₯Ό μνν μ μλ€. 첫째λ‘, μμμ΅ κ±΄μ νμ κ°μ§λ λ°μ΄ν°λ₯Ό μκ°μ μΌλ‘ νμν μ μλ SwiftTuna μμ€ν
μ μ μνλ€. λ°μ΄ν° μ²λ¦¬ λ° μκ°μ ννμ νμ₯μ±μ λͺ©νλ‘ κ°λ°λ μ΄ μμ€ν
μ, μ½ 40μ΅ κ±΄μ νμ κ°μ§ λ°μ΄ν°μ λν μκ°νλ₯Ό μ μ²λ¦¬ μμ΄ μ μ΄λ§λ€ μ
λ°μ΄νΈν μ μλ κ²μΌλ‘ λνλ¬λ€. λμ§Έλ‘, κ·Όμ¬μ k-μ΅κ·Όμ μ (Approximate k-Nearest Neighbor) λ¬Έμ λ₯Ό μ μ§μ μΌλ‘ κ³μ°νλ PANENE μκ³ λ¦¬μ¦μ μ μνλ€. κ·Όμ¬μ k-μ΅κ·Όμ μ λ¬Έμ λ μ¬λ¬ κΈ°κ³ νμ΅ κΈ°λ²μμ μ°μμλ λΆκ΅¬νκ³ μ΄κΈ° κ³μ° μκ°μ΄ κΈΈμ΄μ μΈν°λν°λΈν μμ€ν
μ μ μ©νκΈ° νλ νκ³κ° μμλ€. PANENE μκ³ λ¦¬μ¦μ μ΄λ¬ν κΈ΄ μ΄κΈ° κ³μ° μκ°μ νκΈ°μ μΌλ‘ κ°μ νμ¬ λ€μν κΈ°κ³ νμ΅ κΈ°λ²μ μκ°μ λΆμμ νμ©ν μ μλλ‘ νλ€. νΉν, μ μ©ν λΉμ νμ μ°¨μ κ°μ κΈ°λ²μΈ t-λΆν¬ νλ₯ μ μλ² λ©(t-Distributed Stochastic Neighbor Embedding)μ κ°μνμ¬ μλ°± κ°μ μ°¨μμ κ°μ§λ λ°μ΄ν°λ₯Ό λΉ λ₯Έ μκ° λ΄μ μ¬μν μ μλ€. μμ λ μμ€ν
κ³Ό μκ³ λ¦¬μ¦μ΄ λ°μ΄ν°μ ν λλ μ΄μ κ°μλ‘ μΈν νμ₯μ± λ¬Έμ λ₯Ό ν΄κ²°νκ³ μ νλ€λ©΄, μΈ λ²μ§Έ μμ€ν
μμλ μ μ§μ μκ°μ λΆμμ μ λ’°λ λ¬Έμ λ₯Ό κ°μ νκ³ μ νλ€. μ μ§μ μκ°μ λΆμμμ μ¬μ©μμκ² μ£Όμ΄μ§λ μ€κ° κ³μ° κ²°κ³Όλ μ΅μ’
κ²°κ³Όμ κ·Όμ¬μΉμ΄λ―λ‘ λΆνμ€μ±μ΄ μ‘΄μ¬νλ€. λ³Έ μ°κ΅¬μμλ μΈμ΄νκ°λλ₯Ό μ΄μ©ν μ μ§μ μκ°μ λΆμ(Progressive Visual Analytics with Safeguards)μ΄λΌλ μλ‘μ΄ κ°λ
μ μ μνλ€. μ΄ κ°λ
μ μ¬μ©μκ° μ μ§μ νμμμ λ§μ£Όνλ λΆνμ€ν μ€κ° μ§μμ μΈμ΄νκ°λλ₯Ό λ¨κΈΈ μ μλλ‘ νμ¬ νμμμ μ»μ μ§μμ μ νλλ₯Ό μΆν κ²μ¦ν μ μλλ‘ νλ€. λν, μ΄λ¬ν κ°λ
μ μ€μ λ‘ κ΅¬ννμ¬ νμ¬ν ProReveal μμ€ν
μ μκ°νλ€. ProRevealλ₯Ό μ΄μ©ν μ¬μ©μ μ€νμμ μ¬μ©μλ€μ μΈμ΄νκ°λλ₯Ό μ±κ³΅μ μΌλ‘ λ§λ€ μ μμμ λΏλ§ μλλΌ, μ€κ° μ§μμ λΆνμ€μ±μ λ€λ£¨κΈ° μν΄ μΈμ΄νκ°λλ₯Ό μλ°μ μΌλ‘ μ΄μ©νλ€λ κ²μ μ μ μμλ€. λ§μ§λ§μΌλ‘, μ μΈ κ°μ§ μ°κ΅¬μ κ²°κ³Όλ₯Ό μ’
ν©νμ¬ μ μ§μ μκ°μ λΆμ μμ€ν
μ ꡬνν λμ λμμΈμ λμ μ ν₯ν μ°κ΅¬ λ°©ν₯μ λͺ¨μνλ€.CHAPTER1. Introduction 2
1.1 Background and Motivation 2
1.2 Thesis Statement and Research Questions 5
1.3 Thesis Contributions 5
1.3.1 Responsive and Incremental Visual Exploration of Large-scale Multidimensional Data 6
1.3.2 ProgressiveComputation of Approximate k-Nearest Neighbors and Responsive t-SNE 7
1.3.3 Progressive Visual Analytics with Safeguards 8
1.4 Structure of Dissertation 9
CHAPTER2. Related Work 11
2.1 Progressive Visual Analytics 11
2.1.1 Definitions 11
2.1.2 System Latency and Human Factors 13
2.1.3 Users, Tasks, and Models 15
2.1.4 Techniques, Algorithms, and Systems. 17
2.1.5 Uncertainty Visualization 19
2.2 Approaches for Scalable Visualization Systems 20
2.3 The k-Nearest Neighbor (KNN) Problem 22
2.4 t-Distributed Stochastic Neighbor Embedding 26
CHAPTER3. SwiTuna: Responsive and Incremental Visual Exploration of Large-scale Multidimensional Data 28
3.1 The SwiTuna Design 31
3.1.1 Design Considerations 32
3.1.2 System Overview 33
3.1.3 Scalable Visualization Components 36
3.1.4 Visualization Cards 40
3.1.5 User Interface and Interaction 42
3.2 Responsive Querying 44
3.2.1 Querying Pipeline 44
3.2.2 Prompt Responses 47
3.2.3 Incremental Processing 47
3.3 Evaluation: Performance Benchmark 49
3.3.1 Study Design 49
3.3.2 Results and Discussion 52
3.4 Implementation 56
3.5 Summary 56
CHAPTER4. PANENE:AProgressive Algorithm for IndexingandQuerying Approximate k-Nearest Neighbors 58
4.1 Approximate k-Nearest Neighbor 61
4.1.1 A Sequential Algorithm 62
4.1.2 An Online Algorithm 63
4.1.3 A Progressive Algorithm 66
4.1.4 Filtered AKNN Search 71
4.2 k-Nearest Neighbor Lookup Table 72
4.3 Benchmark. 78
4.3.1 Online and Progressive k-d Trees 78
4.3.2 k-Nearest Neighbor Lookup Tables 83
4.4 Applications 85
4.4.1 Progressive Regression and Density Estimation 85
4.4.2 Responsive t-SNE 87
4.5 Implementation 92
4.6 Discussion 92
4.7 Summary 93
CHAPTER5. ProReveal: Progressive Visual Analytics with Safeguards 95
5.1 Progressive Visual Analytics with Safeguards 98
5.1.1 Definition 98
5.1.2 Examples 101
5.1.3 Design Considerations 103
5.2 ProReveal 105
5.3 Evaluation 121
5.4 Discussion 127
5.5 Summary 130
CHAPTER6. Discussion 132
6.1 Lessons Learned 132
6.2 Limitations 135
CHAPTER7. Conclusion 137
7.1 Thesis Contributions Revisited 137
7.2 Future Research Agenda 139
7.3 Final Remarks 141
Abstract (Korean) 155
Acknowledgments (Korean) 157Docto
- β¦