Modeling strategy for EMI filter and flyback transformer

“The switch-mode power supply is key to miniaturizing power adapters. However, the switching nature of the circuit introduces issues in conducted emissions. In a flyback converter, the transformer serves as the path for common mode current flowing from the primary side to the secondary side. Different winding technologies have been invented and implemented to reduce the capacitance between the primary side and the secondary side. But the repeatability of the winding is still poor due to the fluctuations of the winding machine. Thus, the resulting conducted emission has a fluctuation that can lead to failure in the compliance tests. EMI filter is another module implemented to reduce the conducted emissions. Due to the miniaturization, the components inside a filter are closely placed, therefore, strong mutual parasitics. These parasitics degrade the performance of the EMI filter. Overall, it would be beneficial if the performance of the EMI filter and the fluctuation of the transformer can both be analyzed through pre-design simulation. In this dissertation, a model strategy for EMI filters is developed and validated through comparison with measurement. The strategy covers different types of film capacitors, common mode chokes, and circuit topologies. This dissertation also provides an approach to asserting the parasitic capacitance of transformers through 2D analysis. Contradictory to the existing models that relate the parasitic capacitance and conducted emissions, the best-performance capacitance is found not zero. A simplified circuit model is developed to associate the conducted emissions with the parasitic capacitance of the transformer. This circuit model leads to an analytical formulation for evaluating the best-performance parasitic capacitance of the transformer, and its prediction matches with the observed relationship in the measurement. In conclusion, the research in this dissertation clarified the procedure for utilizing computer-aided simulation to guide the design of EMI filters and flyback converters in compact designs”--Abstract, page iv

Efficient POMDP Forward Search by Predicting the Posterior Belief Distribution

Online, forward-search techniques have demonstrated promising results for solving problems in partially observable environments. These techniques depend on the ability to efficiently search and evaluate the set of beliefs reachable from the current belief. However, enumerating or sampling action-observation sequences to compute the reachable beliefs is computationally demanding; coupled with the need to satisfy real-time constraints, existing online solvers can only search to a limited depth. In this paper, we propose that policies can be generated directly from the distribution of the agent's posterior belief. When the underlying state distribution is Gaussian, and the observation function is an exponential family distribution, we can calculate this distribution of beliefs without enumerating the possible observations. This property not only enables us to plan in problems with large observation spaces, but also allows us to search deeper by considering policies composed of multi-step action sequences. We present the Posterior Belief Distribution (PBD) algorithm, an efficient forward-search POMDP planner for continuous domains, demonstrating that better policies are generated when we can perform deeper forward search

Ganging up on Jolly Roger in Asia : International cooperation and maritime piracy

Thesis (S.M. and S.B.)--Massachusetts Institute of Technology, Dept. of Political Science, 2008.Includes bibliographical references (p. 151-156).As non-traditional security threats such as terrorism and organized transnational crime gain greater prominence around the globe, the need for international cooperation against these non-state actors has consequently acquired greater urgency. Due to the cross-boundary nature of these activities, international cooperation is particularly critical for eradicating these threats. This thesis analyzes a particular instance of a non-state threat, maritime piracy, and uses it as a probe for understanding the nature of international cooperation vis-a-vis non-state actors. I observe a somewhat surprising trend while collating a database of all instances of international cooperation against maritime piracy throughout the world - Asia, and in particular Southeast Asia, has been the source of a disproportionately high level of international cooperation that is focused on eradicating the piracy problem. Furthermore, this trend has occurred even though Asia is often regarded as lacking the conditions necessary for international cooperation in the traditional security domain - binding multilateral institutions that can facilitate the institutionalization of cooperation agreements, as well as a hegemonic power with the ability to enforce cooperation. What has enabled international cooperation against maritime piracy to flourish in Asia, and what does this imply about non-traditional forms of security cooperation? I propose that non-traditional security cooperation has thrived in Asia for at least two unconventional reasons - the ability of non-binding institutions such as ASEAN to facilitate and promote non-traditional security cooperation, as well as the effective use of national coast guard agencies to avoid the political sensitivities that often result from security cooperation in the traditional domain.(cont.) I make these arguments by relying on evidence post-processed from two international datasets that I have collated - one on the instances of international cooperation against maritime piracy, and another on the geographical distribution of piracy attacks over time. It is hoped that these datasets, made publicly available for the first time, will be expanded upon for further research by the academic community. Finally, non-traditional forms of security threats appear to greater enable national governments to leverage off their commonality of interests to promote cooperation, and may even be an important confidence building measure for generating greater cooperation in the traditional security domain in future.by Ruijie He.S.M.and S.B

Efficient planning under uncertainty with macro-actions

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2010.Cataloged from PDF version of thesis.Includes bibliographical references (p. 163-168).Planning in large, partially observable domains is challenging, especially when good performance requires considering situations far in the future. Existing planners typically construct a policy by performing fully conditional planning, where each future action is conditioned on a set of possible observations that could be obtained at every timestep. Unfortunately, fully-conditional planning can be computationally expensive, and state-of-the-art solvers are either limited in the size of problems that can be solved, or can only plan out to a limited horizon. We propose that for a large class of real-world, planning under uncertainty problems, it is necessary to perform far-lookahead decision-making, but unnecessary to construct policies that condition all actions on observations obtained at the previous timestep. Instead, these problems can be solved by performing semi conditional planning, where the constructed policy only conditions actions on observations at certain key points. Between these key points, the policy assumes that a macro-action - a temporally-extended, fixed length, open-loop action sequence, comprising a series of primitive actions, is executed. These macro-actions are evaluated within a forward-search framework, which only considers beliefs that are reachable from the agent's current belief under different actions and observations; a belief summarizes an agent's past history of actions and observations. Together, semi-conditional planning in a forward search manner restricts the policy space in exchange for conditional planning out to a longer-horizon. Two technical challenges have to be overcome in order to perform semi-conditional planning efficiently - how the macro-actions can be automatically generated, as well as how to efficiently incorporate the macro action into the forward search framework. We propose an algorithm which automatically constructs the macro-actions that are evaluated within a forward search planning framework, iteratively refining the macro actions as more computation time is made available for planning. In addition, we show that for a subset of problem domains, it is possible to analytically compute the distribution over posterior beliefs that result from a single macro-action. This ability to directly compute a distribution over posterior beliefs enables us to enjoy computational savings when performing macro-action forward search. Performance and computational analysis for the algorithms proposed in this thesis are presented, as well as simulation experiments that demonstrate superior performance relative to existing state-of-the-art solvers on large planning under uncertainty domains. We also demonstrate our planning under uncertainty algorithms on target-tracking applications for an actual autonomous helicopter, highlighting the practical potential for planning in real-world, long-horizon, partially observable domains.by Ruijie He.Ph.D

Planning in information space for a quadrotor helicopter in a GPS-denied environment

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2008.Includes bibliographical references (leaves 85-87).Unmanned Air Vehicles (UAVs) have thus far had limited success in flying autonomously indoors, with the exception of specially instrumented locations. In indoor environments, accurate global positioning information is unavailable, and the vehicle has to rely on onboard sensors to detect environmental features and infer its position. Given that a vehicle small enough to fly indoors can only carry a limited sensor payload, the vehicle's ability to localize itself varies across different environments, since different surroundings provide varying degrees of sensor information. Therefore, a vehicle that plans a path without regard to how well it can localize itself along that path runs the risk of becoming lost. My research focuses on how path-planning can be performed to minimize localization uncertainty, and works towards developing a motion-planning algorithm for a quadrotor helicopter. As a starting point, I apply the Belief Roadmap (BRM) algorithm, an information-theoretic extension of the Probabilistic Roadmap algorithm, incorporating sensing during the path-planning process. I make two theoretical contributions in this research. First, I extend the original BRM to use non-linear state inference via the Unscented Kalman Filter, providing better approximation of the non-linearities of laser sensing onboard the UAV. Second, I develop a sampling strategy for the BRM, minimizing the number of samples required to find a good path. Finally, I demonstrate the BRM path-planning algorithm on a quadrotor helicopter, navigating the vehicle autonomously in an indoor environment.by Ruijie He.S.M

Novel Class Discovery for Long-tailed Recognition

While the novel class discovery has recently made great progress, existing methods typically focus on improving algorithms on class-balanced benchmarks. However, in real-world recognition tasks, the class distributions of their corresponding datasets are often imbalanced, which leads to serious performance degeneration of those methods. In this paper, we consider a more realistic setting for novel class discovery where the distributions of novel and known classes are long-tailed. One main challenge of this new problem is to discover imbalanced novel classes with the help of long-tailed known classes. To tackle this problem, we propose an adaptive self-labeling strategy based on an equiangular prototype representation of classes. Our method infers high-quality pseudo-labels for the novel classes by solving a relaxed optimal transport problem and effectively mitigates the class biases in learning the known and novel classes. We perform extensive experiments on CIFAR100, ImageNet100, Herbarium19 and large-scale iNaturalist18 datasets, and the results demonstrate the superiority of our method. Our code is available at https://github.com/kleinzcy/NCDLR.Comment: TMLR2023, Final versio

Machine Learning-Based Detection for Cyber Security Attacks on Connected and Autonomous Vehicles

Connected and Autonomous Vehicle (CAV)-related initiatives have become some of the fastest expanding in recent years, and have started to affect the daily lives of people. More and more companies and research organizations have announced their initiatives, and some have started CAV road trials. Governments around the world have also introduced policies to support and accelerate the deployments of CAVs. Along these, issues such as CAV cyber security have become predominant, forming an essential part of the complications of CAV deployment. There is, however, no universally agreed upon or recognized framework for CAV cyber security. In this paper, following the UK CAV cyber security principles, we propose a UML (Unified Modeling Language)-based CAV cyber security framework, and based on which we classify the potential vulnerabilities of CAV systems. With this framework, a new CAV communication cyber-attack data set (named CAV-KDD) is generated based on the widely tested benchmark data set KDD99. This data set focuses on the communication-based CAV cyber-attacks. Two classification models are developed, using two machine learning algorithms, namely Decision Tree and Naive Bayes, based on the CAV-KDD training data set. The accuracy, precision and runtime of these two models when identifying each type of communication-based attacks are compared and analysed. It is found that the Decision Tree model requires a shorter runtime, and is more appropriate for CAV communication attack detection

The effects of filling patterns on the powder–binder separation in powder injection molding

AbstractThe powder–binder separation is a common difficulty during injection molding, which leads to the inhomogeneity in the debinding and sintering stages. Previous studies focus on the relationship between “final results” and “initial conditions”, while the dynamic filling process of feedstock and the evolution of powder–binder separation were ignored. This work investigated the effects of filling patterns on the powder–binder separation during powder injection molding. The mold filling model of PIM has been developed, based on the multiphase fluid theory and the viscosity model of feedstock. Parameters of the viscosity model were modified by the experimental data. Numerical simulations were compared with experiments with the same process parameters. The powder–binder separation phenomena in green bodies were detected by X-Ray computed tomography (CT). The experimental phenomena were explained clearly by the evolution of powder–binder separation obtained with numerical simulation method. A typical compacting filling pattern of PIM and filling mobility variable of the feedstock were proposed. A proper filling pattern was helpful to ensure the mobility of feedstock and the homogeneity of green body

Influence of Conformal Coatings on the Emc Performance of a Printed Circuit Board

Conformal coatings are often applied to printed circuit boards to protect the board and its components from environmental factors like moisture, chemicals, and vibration. The impact of a conformal coating on crosstalk and radiated emissions was studied in the following paper. Two coating materials were characterized in terms of their permittivity and permeability. The impact of the conformal coating was evaluated based on the crosstalk between microstrip traces, the radiated emissions from a switch-mode power supply (SMPS), and on coupling from an EMI filter to nearby components. The coatings increased crosstalk between microstrip traces by up to 5 ~ 6 dB, and increased radiated emissions from the SMPS by up to 8 dB. While the coating did not affect the performance of the EMI filter, a 5.5 dB increase in coupling was observed from the filter to nearby components. These effects should be considered if pre-compliance testing is performed before the coatings are applied

Mass distribution for single-lined hot subdwarf stars in LAMOST

Masses for 664 single-lined hot subdwarf stars identified in LAMOST were calculated by comparing synthetic fluxes from spectral energy distribution (SED) with observed fluxes from virtual observatory service. Three groups of hot subdwarf stars were selected from the whole sample according to their parallax precision to study the mass distributions. We found, that He-poor sdB/sdOB stars present a wide mass distribution from 0.1 to 1.0 $\mathrm{M}_{\odot}$ with a sharp mass peak around at 0.46 $\rm{M}_{\odot}$, which is consistent with canonical binary model prediction. He-rich sdB/sdOB/sdO stars present a much flatter mass distribution than He-poor sdB/sdOB stars and with a mass peak around 0.42 $\mathrm{M}_{\odot}$. By comparing the observed mass distributions to the predictions of different formation scenarios, we concluded that the binary merger channel, including two helium white dwarfs (He-WDs) and He-WD + main sequence (MS) merger, cannot be the only main formation channel for He-rich hot subdwarfs, and other formation channels such as the surviving companions from type Ia supernovae (SNe Ia) could also make impacts on producing this special population, especially for He-rich hot subdwarfs with masses less than 0.44 $\mathrm{M}_{\odot}$. He-poor sdO stars also present a flatter mass distribution with an inconspicuous peak mass at 0.18 $\mathrm{M}_{\odot}$. The similar mass - $\Delta RV_\mathrm{max}$ distribution between He-poor sdB/sdOB and sdO stars supports the scenario that He-poor sdO stars could be the subsequent evolution stage of He-poor sdB/sdOB stars.Comment: 38 pages, 13 figures, 3 tables, accepted for publication in Ap