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关键词： Mechanical engineering   Computer science   Artificial intelligence  

摘要： Cyber-physical systems (CPS) have gained a lot of attention recently in design and control and have many applications including healthcare, transportation, etc. Over the past few decades, the adoption of machine learning (ML)-enabled cyber-physical systems are becoming prevalent in various sectors of modern society. Although machine (deep) learning systems have been very beneficial in many applications and made significant improvements possible in CPS, it is important to investigate the resilience and scalability of these systems as they are being used extensively in various industries and have shown failures under malicious attacks or when they are scaled up to be run at large scales. Recent focus on the robustness of deep learning algorithms to adversarial attacks produced a large variety of algorithms for training robust models. Most of the effective algorithms involve solving the min-max optimization problem for training robust models (min step) under worst-case attacks (max step). However, they often suffer from high computational costs. Therefore, it becomes difficult to readily apply such algorithms for moderate to large size real-world data sets. To alleviate this, this report proposes a novel discrete-time dynamical system-based algorithm that aims to find the saddle point of a min-max optimization problem in the presence of uncertainties. Based on that, a fast robust training algorithm is devised which is applicable to deep neural networks. Although such training involves highly non-convex robust optimization problems, empirical results show that the algorithm can achieve significant robustness compared to other state-of-the-art robust models on benchmark data sets. The effect of adversarial attacks in Reinforcement Learning (RL) environments is also explored in this thesis. As this area has become a new center of attention for research in machine (deep) learning, it is imperative to study the performance of RL systems under malicious state and actuator at

关键词： 教学参考资料   高等学校   关系数据库系统  

ISBN: (纸本)9787115557841

摘要： 本书是《Access数据库实用教程（微课版第3版）》一书的配套教材，全书分为“习题解析篇”“实验指导篇”“实验安排篇”和“模拟试卷篇”4个部分。“习题解析篇”提供了各章习题解析、自测题和自测题参考答案；“实验指导篇”提供了各章实验解析，包括实验内容、实验分析和操作步骤；“实验安排篇”介绍了实验要求，并提供了各章实验练习，分为基础性实验和综合性实验两个层次；“模拟试卷篇”提供了两套模拟试题，包括理论知识和实际操作两部分，理论知识试题配有参考答案。本书自测题目和实验内容覆盖了主教材各章节的知识点。全书实验题目均使用“成绩管理”数据库，可以帮助读者体验使用Access建立数据库应用系统的全过程。本书结构清晰，习题解析详尽，实验操作步骤具体，既可作为读者自学教材，也可作为高等院校数据库技术与应用等相关课程的教材或参考书，还可作为社会各类学校的培训教材。

关键词： Computer science   Artificial intelligence  

关键词： Engineering   Computer science   Artificial intelligence   Information technology  

摘要： Quality control (QC) in manufacturing processes is critical to ensuring consumers receive products with proper functionality and reliability. Faulty products can lead to additional costs for the manufacturer and damage trust in a brand. A growing trend in QC is the use of machine vision (MV) systems because of their noncontact inspection, high repeatability, and efficiency. This thesis presents a robust MV system developed to perform comparative dimensional inspection on diversely shaped samples. Perimeter, area, rectangularity, and circularity are determined in the dimensional inspection algorithm for a base item and test items. A score determined with the four obtained parameter values provides the likeness between the base item and a test item. Additionally, a surface defect inspection is offered capable of identifying scratches, dents, and markings. The dimensional and surface inspections are used in a QC industrial case study. The case study examines the existing QC system for an electric motor manufacturer and proposes the developed QC system to increase product inspection count and efficiency while maintaining accuracy and reliability. Finally, the QC system is integrated in a simulated product inspection line consisting of a robotic arm and conveyor belts. The simulated product inspection line could identify the correct defect in all tested items and demonstrated the system’s automation capabilities.

关键词： Evolution and Development   Ecology   Computer science   Design   Science history   Biology  

摘要： Evolution is a complex process with a simple recipe. Evolutionary change involves three essential “ingredients” interacting over many generations: adaptation (selection), chance (random variation), and history (inheritance). In 1989’s Wonderful Life, the late paleontologist Stephen Jay Gould advocated for the importance of historical contingency—the way unique events throughout history influence future possibilities—using a clever thought experiment of “replaying life’s tape”. But not everyone was convinced. Some believed that chance was the primary driver of evolutionary change, while others insisted that natural selection was the most powerful influence. Since then, “replaying life’s tape” has become a core method in experimental evolution for measuring the relative contributions of adaptation, chance, and history. In this dissertation, I focus on the effects associated with history in evolving populations of digital organisms—computer programs that self-replicate, mutate, compete, and evolve in virtual environments. In Chapter 1, I discuss the philosophical significance of Gould’s thought experiment and its influence on experimental methods. I argue that his thought experiment was a challenge to anthropocentric reasoning about natural history that is still popular, particularly outside of the scientific community. In this regard, it was his way of advocating for a “radical” view of evolution. In Chapter 2—Richard Lenski, Charles Ofria, and I describe a two-phase, virtual, “long-term” evolution experiment with digital organisms using the Avida software. In Phase I, we evolved 10 replicate populations, in parallel, from a single genotype for around 65,000 generations. This part of the experiment is similar to the design of Lenski’s E. coli Long-term Evolution Experiment (LTEE). We isolated the dominant genotype from each population around 3,000 generations (shallow history) into Phase I and then again at the end of Phase I (deep history). In Phase II, we evolved 10

关键词： Electrical engineering   Artificial intelligence   Computer science  

摘要： There are three parts to this thesis. In the first part, we study the channel estimation problem in frequency-selective multi-user (MU) multi-cell massive multiple-input multiple-output (MIMO) systems, where, a time-domain semi-blind channel estimation technique is proposed. Compared to frequency-domain, the time-domain channel estimation requires fewer parameters be estimated. Importantly, the time-domain estimation has enough samples for an accurate channel estimate. Given this many samples in the time-domain, the proposed channel estimation technique obtains a better estimate of the channel. Here, there is no assumption on orthogonality of users' channels, knowledge of large-scale fading coefficients, and the orthogonality between the training symbols of the users in all cells. The second part of the thesis studies the channel estimation problem in correlated massive MIMO systems with a reduced number of radio-frequency (RF) chains. Leveraging the knowledge of channel correlation matrices, we propose to estimate the channel entries in its eigen-domain. Due to the limited number of RF chains, channel estimation is typically performed in multiple time slots. Using the minimum mean squared error (MMSE) criterion, the optimal precoder and combiner in each time slot are aligned to transmitter and receiver eigen-directions, respectively. Meanwhile, the optimal power allocation for each training time slots is obtained via a waterfilling-type expression. In the final part, we study the downlink channel estimation for frequency-division-duplex (FDD) massive MIMO systems. Acquiring downlink channel state information in these systems is challenging due to the large training and feedback overhead. Motivated by the partial reciprocity of uplink and downlink channels, we first estimate the frequency-independent channel parameters, i.e., the path gains, delays, angles-of-arrivals (AoAs) and angles-of-departures (AoDs), via uplink training, since these parameters are common in b

关键词： Computer science   Industrial engineering   Mathematics   Mechanical engineering   Mechanics   Optics   Physics   Polymer chemistry  

摘要： Extrusion-based 3D printing of thermoplastic polymer composites manufactures parts that have nonhomogenous, orthotropic, and process-dependent macro-scale material properties. As a part of the dissertation, research works were carried out to: • improve the interlayer mechanical properties and reduce the orthotropy, • use experimentally homogenized orthotropic material properties to numerically model the mechanical behavior of the non-homogenous orthotropic 3D printed parts, • create an efficient numerical thermal model to predict the process-dependent thermal history of the 3D printed part, and • aid the manufacturing process by selecting a suitable set of processing parameters based on a simplified sequentially coupled thermomechanical model. The dissertation presents four studies that improve the understanding of the mechanical behavior and aid the manufacturing process of the 3D printed thermoplastic polymer composites. Three journal publications that resulted from the research work carried out are listed below: • Bhandari, S., Lopez-Anido, R. A., & Gardner, D. J. (2019). Enhancing the interlayer tensile strength of 3D printed short carbon fiber reinforced PETG and PLA composites via annealing. Additive Manufacturing, 30, 100922. • Bhandari, S., Lopez-Anido, R.A., Wang, L. et al. (2020). Elasto-Plastic Finite Element Modeling of Short Carbon Fiber Reinforced 3D Printed Acrylonitrile Butadiene Styrene Composites. JOM 72, 475– 484. • Bhandari, S.; Lopez-Anido, R.A. (2020). Discrete-Event Simulation Thermal Model for Extrusion-Based Additive Manufacturing of PLA and ABS. Materials, 13, 4985.

关键词： Bioinformatics   Computational chemistry   Computer science   Biomedical engineering  

摘要： Since severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is highly contagious and mortal, finding a treatment is time critical. Drug repurposing is probably the quickest and safest approach in our arsenal. However, testing every drug in a brute force manner would require a lot of resources, and a more sophisticated method is required to filter possible candidates. Since several molecules have already been shown to be effective against SARS-CoV-2 in wet-lab experiments, choosing drugs with similar characteristics would increase our chances of success. In this study, we compare the molecular docking results of FDA-approved drugs from the ZINC database against the molecules with positive experimental results. AutoDock Vina was used to dock the molecules against the SARS-CoV-2 spike receptor bound to the ACE2 receptor (6M0J). Results were pre-filtered to 50 candidates according to their binding affinities and the 10 most promising molecules that have similar interactions with the experimental drugs were identified. Then, the 10 molecules were docked against B.1.1.7, B.1.351, and P.1 variants, and their inhibition potentials were discussed. According to the results, we conclude that some molecules that inhibit the wild type also have the potential to inhibit the variants as well. However, further experimental and clinical studies are needed.

关键词： Bioinformatics   Artificial intelligence   Computer science  

摘要： There is intense interest in adopting computer-aided diagnosis (CAD) systems, particularly those developed based on deep learning algorithms, for applications in a number of medical specialties. However, success of these CAD systems relies heavily on large annotated datasets; otherwise, deep learning often results in algorithms that perform poorly and lack generalizability. Therefore, this dissertation seeks to address this critical problem: How to develop efficient and effective deep learning algorithms for medical applications where large annotated datasets are unavailable. In doing so, we have outlined three specific aims: (1) acquiring necessary annotations efficiently from human experts; (2) utilizing existing annotations effectively from advanced architecture; and (3) extracting generic knowledge directly from unannotated images. Our extensive experiments indicate that, with a small part of the dataset annotated, the developed deep learning methods can match, or even outperform those that require annotating the entire dataset. The last part of this dissertation presents the importance and application of imaging in healthcare, elaborating on how the developed techniques can impact several key facets of the CAD system for detecting pulmonary embolism. Further research is necessary to determine the feasibility of applying these advanced deep learning technologies in clinical practice, particularly when annotation is limited. Progress in this area has the potential to enable deep learning algorithms to generalize to real clinical data and eventually allow CAD systems to be employed in clinical medicine at the point of care.

关键词： Computer science   Artificial intelligence