Variety Of Topologies Research Articles

A Generalized Method for Comprehension of Switched-Capacitor High Step-Up Converters Including Coupled Inductors and Voltage Multiplier Cells

High step-up converters are crucial in many power electronic interfaces, including for renewable energy sources. As the result of topological variation of high step-up converters, many topologies share similar characteristics. In order to have a clear understanding of an optimized design that makes the best use of components to achieve high gain, it is necessary to devise a generalized comprehension method for high step-up converters. This article presents a novel generalized method for analyzing single-switch step-up converters that can include switched capacitor (SC) cells, a coupled inductor (CI), and/or voltage multiplier cells (VMCs). The proposed method is neither dependent on the position of the CI nor the structure of the VMC, and is not tied to a specific topology. Thus, the proposed generalized method uniquely reveals the unifying theory underlying high step-up converters with any variation of SC/CI/VMC. In order to verify the theoretical analysis, many examples from the literature are investigated. Then, using design tips from the generalized method, a new high step-up converter is designed. A 150-W prototype of the converter shows 97.5% peak efficiency. The proposed converter also compares favorably to other topologies in both a power loss breakdown analysis and a component stress factor analysis.

Density-Based Topology Optimization for a Defined External State of Stress in Individualized Endoprosthesis

AbstractEndoprosthesis are exposed to the risk of aseptic loosening. The design of the prosthesis shaft to achieve physiological force application is therefore of great importance. Additive manufacturing offers the potential to fabricate highly variable topologies, but challenges the designer with a large number of design variables. In this work, a method is developed to determine an optimized density topology that approximates a given mechanical stress state in the bone after implantation. For this purpose, a topology optimization of the density distribution of the implant is performed.

Generative Design by Reinforcement Learning: Enhancing the Diversity of Topology Optimization Designs

Generative design refers to computational design methods that can automatically conduct design exploration under constraints defined by designers. Among many approaches, topology optimization-based generative designs aim to explore diverse topology designs, which cannot be represented by conventional parametric design approaches. Recently, data-driven topology optimization research has started to exploit artificial intelligence, such as deep learning or machine learning, to improve the capability of design exploration. This study proposes a reinforcement learning (RL) based generative design process, with reward functions maximizing the diversity of topology designs. We formulate generative design as a sequential problem of finding optimal design parameter combinations in accordance with a given reference design. Proximal Policy Optimization is used as the learning framework, which is demonstrated in the case study of an automotive wheel design problem. To reduce the heavy computational burden of the wheel topology optimization process required by our RL formulation, we approximate the optimization process with neural networks. With efficient data preprocessing/augmentation and neural architecture, the neural networks achieve a generalized performance and symmetricity-reserving characteristics. We show that RL-based generative design produces a large number of diverse designs within a short inference time by exploiting GPU in a fully automated manner. It is different from the previous approach using CPU which takes much more processing time and involving human intervention.

Topology optimization of the vibrating structure for fused deposition modelling of parts considering a hybrid deposition path pattern

ABSTRACT Frequency optimization plays a vital role in designing machines and structures to avoid destructive responses caused by external excitation. In the current study, most frequency optimization research focuses on algorithm innovation to pursue better numerical results. However, with the development of additive manufacturing, increasingly more organic structures produced by topology optimization can be physically fabricated. Therefore, the combination of topology optimization and additive manufacturing is promising and widely investigated. This paper proposes a concurrent topology optimization method for maximizing the natural frequency of Fused Deposition Modelling (FDM) parts printed by a Hybrid Deposition Path (HDP) pattern. The proposed algorithm concurrently optimizes the shape of the structure and the raster directions of the substrate domain, wherein the method of solid orthotropic materials with penalization (SOMP) with double layers of smoothing and projection (DSP) is adopted. A dedicated sensitivity analysis is performed on both the topology and direction variables. Several numerical results are studied to show the effectiveness of the proposed method is efficient and to disclose the influence of raster direction on the vibration model. This work would be instructive to design for FDM printing.

Mathematical Modeling and Multivariable Optimization Design of SUV Chassis Structure Based on SFE CONCEPT

The car chassis design concept is an important part of the car design process. Analyzing the car chassis structure in the conceptual design process is of great significance for improving the efficiency and accuracy of car chassis design. This paper takes SUV models as the research object and uses SFE CONCEPT software to realize the mathematical modeling and multivariate optimization of the SUV chassis structure. The concept of “implicit” parameterization and the concept of “CAE analysis-driven design” are adopted to quickly realize large-scale geometric transformation and consistency of interconnection, while achieving automated production and grid parameters. Through this research, it can be found that in the concept development stage of the vehicle chassis structure, the CAE performance analysis is introduced into the early development stage as soon as possible; combined with the implicit parametric design method, it can quickly and effectively realize the exploration and optimization of a larger range of structures. In addition, the SFE CONCEPT software used in this article can create parametric structural models, and combined with automatic meshing technology, it can realize continuous changes of topology variables, shape variables, size variables, and material variables, and compare the design results with the detailed design results to confirm the feasibility of the design. In addition, the experimental test shows that the success rate of SFE CONCEPT and other CAD software docking reaches 100%.

IoT threat mitigation engine empowered by artificial intelligence multi-objective optimization

From smart homes to smart cities and Industry 4.0 to Transportation Systems, Internet of Things (IoT) is a domain which promises incredible growth coupled with great impact, in numerous fields. IoT networks are composed of numerous different Things, arranged in diverse topologies with diverse needs. This diversity is partially due to the numerous areas where IoT applications are utilized, which at their entirety can be referred as the IoT ecosystem. The IoT ecosystem suffers from numerous vulnerabilities, due to reasons such as design flows, hardware limitation or simply human error and is subject to various attacks targeting IoT services, platforms and networks. These attacks can have significant consequences such as economic losses, service disruption or data leaks. Cyber-attacks are an unavoidable and must be faced in tandem with the global growth of IoT networks. An approach that can assist in developing robust, intelligent Cyber-security tools for IoT is using Artificial Intelligence. In the following paper, a mechanism is presented that automatically selects appropriate mitigation actions in an optimal way to countermeasure attacks faced by IoT networks. This is achieved by using an novel Artificial Intelligence mechanism based on a Deep Neural Architecture called Pointer Networks to optimize security-related KPIs. Experimental results, show that the proposed method produces equal or better Pareto optimal solutions, performs faster compared to state-of-the-art (SoA) algorithms and scales better.

How to Register a Live onto a Liver ? Partial Matching in the Space of Varifolds

Partial shapes correspondences is a problem that often occurs in computer vision (occlusion, evolution in time...). In medical imaging, data may come from different modalities and be acquired under different conditions which leads to variations in shapes and topologies. In this paper we use an asymmetric data dissimilarity term applicable to various geometric shapes like sets of curves or surfaces, assessing the embedding of a shape into another one without relying on correspondences. It is designed as a data attachment for the Large Deformation Diffeomorphic Metric Mapping (LDDMM) framework, allowing to compute a meaningful deformation of one shape onto a subset of the other. We refine it in order to control the resulting non-rigid deformations and provide consistent deformations of the shapes along with their ambient space. We show that partial matching can be used for robust multi-modal liver registration between a Computed Tomography (CT) volume and a Cone Beam Computed Tomography (CBCT) volume. The 3D imaging of the patient CBCT at point of care that we call __live__ is truncated while the CT pre-intervention provides a full visualization of the __liver__. The proposed method allows the truncated surfaces from CBCT to be aligned non-rigidly, yet realistically, with surfaces from CT with an average distance of $2.6mm(+/- 2.2)$. The generated deformations extend consistently to the liver volume, and are evaluated on points of interest for the physicians, with an average distance of $5.8mm (+/- 2.7)$ for vessels bifurcations and $5.13mm (+/- 2.5)$ for tumors landmarks. Such multi-modality volumes registrations would help the physicians in the perspective of navigating their tools in the patient's anatomy to locate structures that are hardly visible in the CBCT used during their procedures.<br>Our code is available at <a href='https://github.com/plantonsanti/PartialMatchingVarifolds'>https://github.com/plantonsanti/PartialMatchingVarifolds</a>.

Network Coding-based Resilient Routing for Maintaining Data Security and Availability in Software-Defined Networks

Software-Defined Networking (SDN) improves network performance by flexible traffic control. Data security and data availability are two main concerns for designing a resilient routing algorithm in SDN. Existing algorithms such as the MPT algorithm consider joint data security and availability, but they cannot make a good trade-off. In this paper, we propose a Network Coding-based Resilient Routing algorithm named NCRR to jointly achieve data security and availability. NCRR is a heuristic algorithm that computes routing decisions in three scenarios based on the number of disjoint paths. Specifically, the scenario of three disjoint paths is enough to ensure joint security and availability for a flow when three or more disjoint paths can be used for forwarding this flow. However, due to topological diversity, we cannot always find three disjoint paths for each flow. Thus, the scenarios of two disjoint paths and one path are used to ensure joint security and availability if only two disjoint paths and one path can be used. To evaluate the performance of NCRR, simulations have been conducted using two real-world network topologies. Simulation results show that NCRR improves the joint data security and availability performance by approximately 6.23% on AttMpls topology and 21.34% on Cernet topology, compared with existing MPT.

Fault section location in urban distribution network based on fault marking

The goal is to propose an effective method for locating a fault segment in urban power distribution networks. Urban distribution networks have multiple outgoing lines, switches with multiple connections and have variable topology characteristics. It is found that the fault location method based on matrix algorithm has low adaptive capability and fault tolerance when dealing with complex and variable topology. Therefore, this paper proposes an efficient fault segment location method based on special fault indicators, which can significantly improve the accuracy and reliability of fault location. Accordingly, to improve the accuracy and reliability of fault location, a fault segment location method based on fault marking is proposed. The approach proposed in the paper relies on the analysis of the incident matrix, which describes the relationship between nodes and branches, and allows the use of graph theory. The branch state vector is added to obtain the adjacency matrix, which allows to describe the state of change in the dynamics of the distribution network topology. In the next step, a set of nodes and branches, which reflect the incoming and outgoing interconnections of the nodes, is established based on the selected direction of the network binding. According to the direction of the node fault current, the suspicious branches are identified and labeled to indicate the fault. By cumulative calculation and analysis of the labels, the target branches are screened out and the faulty sections of the city power supply network are identified. The results of the case study conducted in the paper show that the proposed method has good adaptability to the variable topology and increases the fault tolerance and accuracy of the developed matrix algorithm. The topological operating state of the network can be changed by controlling the switches to optimize the operation and improve the reliability of the power supply. Thus, the algorithm for fast and accurate fault location is of great importance for improving the safety and quality of urban power supply.

Spatiotemporal Input Control: Leveraging Temporal Variation in Network Dynamics

The number of available control sources is a limiting factor to many network control tasks. A lack of input sources can result in compromised controllability and/or sub-optimal network performance, as noted in engineering applications such as the smart grids. The mechanism can be explained by a linear time-invariant model, where structural controllability sets a lower bound on the number of required sources. Inspired by the ubiquity of time-varying topologies in the real world, we propose the strategy of spatiotemporal input control to overcome the source-related limit by exploiting temporal variation of the network topology. We theoretically prove that under this regime, the required number of sources can always be reduced to 2. It is further shown that the cost of control depends on two hyperparameters, the numbers of sources and intervals, in a trade-off fashion. As a demonstration, we achieve controllability over a complex network resembling the nervous system of Caenorhabditis elegans using as few as 6% of the sources predicted by a static control model. This example underlines the potential of utilizing topological variation in complex network control problems.

An Enhanced Differential Protection Scheme for LVDC Microgrid

The low-voltage dc (LVDC) microgrid possesses numerous benefits and their penetration in the power system has increased rapidly in recent years. However, the detection of faults in the LVDC microgrid is a challenging issue due to the large magnitude of fault currents and fault-level variation in the microgrid. The performance of the recent current and its derivative-based protection scheme is limited in case of faults in the islanding mode of operation, different microgrid topologies, varying distributed generations (DGs) penetration, and against the measurement noise. This article presents an enhanced differential protection scheme for LVDC microgrid integrated with multiple DGs and storage. The differential current and its first derivative are processed through the decision tree (DT) algorithm for fault detection and the K-nearest neighbor (KNN) technique is utilized for fault classification. The robustness of the proposed protection scheme is tested for different fault types and fault conditions with variation in microgrid topology and operating conditions. The impact of the intermittent and volatile nature of the DGs, the presence of measurement noise, and assessment during external faults and critical no-fault cases have been investigated. The proposed scheme is tested on the MATLAB/ SIMULINK and validated on the Typhoon HIL platform for the assessment of real-time performance. The test results show that the proposed scheme can detect and classify faults with high accuracy and faster response time and, thus, can be a potential candidate for providing dependable protection measures for LVDC microgrids.

A structural discrete size and topology optimization method with extended approximation concepts

This work focused on the discrete size and topology optimization problems for the structures that could be composed of bars, beams, plates, or a combination of them. A corresponding optimization method was put forward based on the approximate concepts that were extended from the one applied in the previous engineering method. In the proposed method, the primal problem was firstly transformed into a series of explicitly approximate problems involving both 0/1 topology and discrete size variables with the extended approximate concepts. 0/1 topology variables were determined with a genetic algorithm (GA), and the corresponding discrete size variables were optimized after determining 0/1 topology variables. In size optimization, the variables were firstly supposed as continuous and determined, as {overline{mathbf{X}}}^{{left( {mathbf{p}} right)}}, with a dual method through the second-level approximate problems. Then the available discrete size values adjacent to {overline{mathbf{X}}}^{{left( {mathbf{p}} right)}} were selected from the original discrete set, and the size variables could be further determined from these values with a GA. Structural analyses were only conducted before establishing the approximate problems in iteration cycles. Numerical examples were given to illustrate the performance of this method, and the results indicate that this method is quite efficient for the discrete size and topology optimization problem.

Numerical study of hemolysis induced by shear stress at the junction between aorta and ventricular assistance device outflow graft

Ventricular assistance devices (VADs) as the effective treatment for heart failure have been paid high attention from researchers for decades. The follow-up complications such as hemolysis and thrombosis require further optimization for this technique. Shear stress has been demonstrated to be significantly related to the hemolysis because of the rupture of red blood cells membrane with a leaking of hemoglobin in the plasma. This issue has already been commonly investigated inside the pump of VAD, but estimations are still lacking regarding hemolysis generation in the aorta itself after VAD implantaion. Thus, the present study aims to evaluate the hemolysis in aorta through establishing the 3D numerical model of aorta with left ventricular assistance device (LVAD). Non-Newtonian Carreau model has been adopted to predict the blood rheology. Comparisons of hemolysis evaluation have been made with two different mathematical models. Moreover, the flow topology and hemodynamic variations have been studied. Two different working conditions of LVAD have been considered corresponding to different heart failure severities. The results reveal a relatively low level of hemolysis risks in aorta. The thrombosis is more prone to occur in the case of severe heart failure condition.

Distributed relay protection for distribution network based on hybrid power method and current method

The distributed power supply is gradually connected to the distribution network, the original single power source radiant network pattern of the distribution network no longer exists. The topology of the distribution network is characterized by variable topology and multi-power supply, and the traditional fault detection and protection actions cannot adapt to the new distribution network topology. Based on failure probability of the highest single phase ground fault in distribution network by the power differential method and differential current method to deal with failure, installed the reclosing device, and through the simulink simulation, verify the effectiveness of the proposed scheme, can effectively deal with the distribution network fault, to deal with instant fault and permanent fault, effectively reduce misoperation. In this paper, the scheme of setting the setting value independently is adopted, which can quickly restore the power supply in non-fault area and improve the continuity of power supply. It has very broad engineering application scenarios.

Multi-Agent Autonomy: Advancements and Challenges in Subterranean Exploration

Artificial intelligence has undergone immense growth and maturation in recent years, though autonomous systems have traditionally struggled when fielded in diverse and previously unknown environments. DARPA is seeking to change that with the Subterranean Challenge, by providing roboticists the opportunity to support civilian and military first responders in complex and high-risk underground scenarios. The subterranean domain presents a handful of challenges, such as limited communication, diverse topology and terrain, and degraded sensing. Team MARBLE proposes a solution for autonomous exploration of unknown subterranean environments in which coordinated agents search for artifacts of interest. The team presents two navigation algorithms in the form of a metric-topological graph-based planner and a continuous frontier-based planner. To facilitate multi-agent coordination, agents share and merge new map information and candidate goal points. Agents deploy communication beacons at different points in the environment, extending the range at which maps and other information can be shared. Onboard autonomy reduces the load on human supervisors, allowing agents to detect and localize artifacts and explore autonomously outside established communication networks. Given the scale, complexity, and tempo of this challenge, a range of lessons was learned, most importantly, that frequent and comprehensive field testing in representative environments is key to rapidly refining system performance.

A Contribution to the Development of High-Voltage dc Circuit Breaker Technologies: A Review of New Considerations

To promote the integration of renewable energy resources into modern energy systems, high-voltage dc (HVdc) and circuit breaker (CB) technologies have become critical to achieving secure and efficient energy transmission. This article reviews the technical development of the related areas, compares diverse breaker concepts and topologies, investigates possible coordination and testing solutions, and points out the remaining challenges as well as future needs. The time-domain simulation and comparative analysis are adopted in this article to analyze and compare the performances of different HVdc CBs. By making use of different selectivity levels of multiterminal HVdc (MTdc) grids, the suitable planning and placement of HVdc CBs can be conducted. Furthermore, by providing insights into the performance of HVdc CBs, the work presented in this article can serve as a useful asset for the upcoming standardization and industrial application process of HVdc grid and CB design and testing.

Tunable Bandgap Design of Soft Phononic Crystals Using Topology Optimization

AbstractSoft phononic crystals (PnCs) have significant advantages for tuning bandgaps and undergoing reversible large deformations. However, the difficulty of the design arises from the presence of material and geometrical nonlinearities coupled with the energy band performance. This study presents an effective topological optimization method to realize tunable bandgap designs of mechanism‐driven soft PnCs composed of two hyperelastic materials. Based on the material‐field series‐expansion method, the topology optimization model with a small number of topological design variables is defined as maximizing the bandgaps of PnCs before and/or after stretching. The optimization problem is then solved by a gradient‐free algorithm, which eliminates the need for the complex sensitivity analysis. Numerical examples show that using tensile deformation to switch between different acoustic functions can achieve the maintenance, opening, and closing of arbitrary order bandgaps, and the proposed method provides an effective way to design soft PnCs with rapid tunability of bandgaps.

Individual variation in brain network topology is linked to course of illness in major depressive disorder.

Major depressive disorder (MDD) is a chronic and highly recurrent disorder. The functional connectivity in depression is affected by the cumulative effect of course of illness. However, previous neuroimaging studies on abnormal functional connection have not mainly focused on the disease duration, which is seen as a secondary factor. Here, we used a data-driven analysis (multivariate distance matrix regression) to examine the relationship between the course of illness and resting-state functional dysconnectivity in MDD. This method identified a region in the anterior cingulate cortex, which is most linked to course of illness. Specifically, follow-up seed analyses show this phenomenon resulted from the individual differences in the topological distribution of three networks. In individuals with short-duration MDD, the connection to the default mode network was strong. By contrast, individuals with long-duration MDD showed hyperconnectivity to the ventral attention network and the frontoparietal network. These results emphasized the centrality of the anterior cingulate cortex in the pathophysiology of the increased course of illness and implied critical links between network topography and pathological duration. Thus, dissociable patterns of connectivity of the anterior cingulate cortex is an important dimension feature of the disease process of depression.

Cellular synthesis of protein pretzelanes

Topology has been recognized as a unique dimension in molecular engineering, yet the topological diversity remains largely untapped, especially in macromolecules. Herein, we report the molecular design, cellular synthesis, and detailed characterization of protein pretzelanes with a chemical topology of a bridged Hopf link. The synergy between the intramolecular chain entwining guided by the p53dim (X) domains and the genetically encoded side-chain coupling by SpyTag(A)-SpyCatcher(B) reaction facilitates the direct synthesis of the model protein pretzelane BXA-BXA in Escherichia coli. The approach tolerates the insertion of various proteins-of-interest, such as elastin-like protein (ELP), superfolder green fluorescent protein (GFP) and dihydrofolate reductase (DHFR), at the bridge region between two rings, giving rise to three protein pretzelanes BXA-ELP-BXA, BXA-GFP-BXA, and BXA-DHFR-BXA. Their topology has been verified by combined techniques of MALDI-TOF mass spectrometry, ion mobility-mass spectrometry, site-specific mutation, and orthogonal proteolytic digestion experiments. Not only are the fluorescent properties of GFP and the catalytic properties of DHFR fully retained, the pretzelane topology also renders BXA-DHFR-BXA more thermally resilient than the wild-type DHFR. These results expand the topological diversity of proteins and demonstrate protein stabilization as a potential functional benefit for the pretzelane topology.

Parametric Analysis for Performance Optimization of Line-Start Synchronous Motor with Interior Asymmetric Permanent Magnet Array Rotor Topology

Line-start synchronous motors have attracted researchers’ interest as suitable replacements of asynchronous motors due to their high efficiency, which has been provoked by strict regulations regarding applicable efficiency classes of motors in the EU market. The research becomes even more challenging as it takes into consideration the diverse rotor topologies with different magnet locations for this type of motor. The rotor configuration with an interior asymmetric permanent magnet (PM) array rotor was chosen for analysis and optimization in this paper as this specific configuration is particularly challenging in terms of placing the magnets with adequate dimensions into the existing rotor of the asynchronous motor with a squirrel cage winding, in order simultaneously to obtain good operational characteristics such as high efficiency and power factor, good overloading capability and low material consumption. Therefore, an optometric analysis is performed in order to find the best configuration of the air gap length, magnet thickness, magnet width and number of conductors per slot, along with modifications of the rotor slot. The motor outer dimensions remained unchanged compared with the starting model of the line-start motor derived from the asynchronous motor, which is a product of the company Končar. The optimized model obtained higher efficiency, power factor and overloading capability than the starting model, along with good starting and synchronization capabilities.