Two-level Hierarchical Approach Research Articles

Optimal strategies for large-scale pursuers against one evader: A mean field game-based hierarchical control approach

This paper proposes a two-level hierarchical control approach based on pursuit-evasion game and mean field game for the problem of large-scale pursuers with multi-population against a single evader, which implements that the evader is surrounded by pursuers. At the upper layer, we model the pursuit-evasion game between the centers of the pursuer populations and the single evader, which is formulated as a linear quadratic differential game (LQDG) to obtain the optimal control of each player. Then the optimal trajectories derived from the optimal controls are input to the lower layer as the reference trajectories. At the lower layer, we formulate the tracking of reference trajectories and terminal surrounding to the evader of large-scale pursuers with multi-population as a multi-population mean-field game (MPMFG), which solves the communication and computing difficulties caused by large-scale agents. Then, we derive the variational primal–dual formulation of the proposed MPMFG model and solve it with CA-Net, a coupled alternating neural network approach. Finally, simulation experiments are performed under various pursuit-evasion scenarios, and it is verified that the proposed game-based two-level hierarchical control approach is feasible and effective.

Hierarchical Flatness-Based Control for Velocity Trajectory Tracking of the “DC/DC Boost Converter–DC Motor” System Powered by Renewable Energy

In this investigation, a tracking control is designed for the angular velocity of the DC/DC Boost converter–DC motor system. To this end, the dynamics of the power supply, generated through a renewable energy power source, is considered in both the mathematical model and the designed control. This latter is proposed by using a two-level hierarchical approach, where the dynamics of the DC/DC Boost converter and the one associated with the DC motor not only are treated as two independent subsystems, but also they exploit their differential flatness property. For the DC/DC Boost converter, an alternative mathematical model of first order is obtained for designing the low-level voltage control. Whereas, the well known second order mathematical model of the DC motor is used for developing the high-level angular velocity control. The robustness and performance of the hierarchical tracking control are verified via realistic numerical simulations and experimental results by using Matlab-Simulink, a prototype of the system, the DS1104 board, and the renewable energy emulator TDK-Lambda G100-17. The results demonstrate and validate the effectiveness of the proposed approach.

VNF Orchestration and Power-Disjoint Traffic Flow Routing for Optimal Communication Robustness in Smart Grid With Cyber-Physical Interdependence

We explore the use of software-defined networking (SDN) technology in building a communication network for smart grid. With cyber-physical interdependence, such communication network may suffer from cross-network cascading failures. To prevent the failures, we perform virtual network function (VNF) orchestration jointly with power-disjoint routing. Our work is novel in proposing an efficient scheme to find power-disjoint routes at the same time of performing VNF orchestration. We formulate an optimization to maximize the ratio of power-disjoint route count to VNF orchestration cost. The optimization has a non-linear non-convex objective function. We propose a two-level hierarchical solution approach. At higher level, the scheme converts the problem into a fractional maximum flow circulation, which can be solved using simplex method to find the maximum number of power-disjoint routes. Given a higher level solution, the lower level aims to minimize the VNF orchestration cost while satisfying VNF chaining and placement requirements. This lower level hierarchy uses the Dijkstra’s algorithm in building a sequence of minimum spanning trees, each roots at the current VNF hosting node in a VNF chain. Extensive simulation results confirm that the proposed scheme can find the maximum number of power-disjoint routes and minimize the cost within a second, for a system with 120 communication nodes. The results show that the number of power-disjoint routes can be increased by increasing either the number of nodes or node degree, but only the node degree can keep the cost flat. Therefore, one should build a robust software-defined smart grid communication network by enhancing node connectivity.

Controlling of payload swinging of an overhead crane

Abstract A new two-level hierarchical approach to control the trolley position and payload swinging of an overhead crane is proposed. At the first level, a simple mathematical pendulum model is investigated considering the time delay due to the use of a vision system. In the second level, a chain model is developed, extending the previous pendulum model considering the vibration of the suspending chain. The relative displacement of the payload is measured with a vision sensor, and the rest of the state-space variables are determined by a collocated observer. The gain parameters related to the state variables of the chain vibration are determined by the use of a pole placement method. The proposed controller is verified by numerical simulation and experimentally on a laboratory test bench.

A New Algorithm for Land-Cover Classification Using PolSAR and InSAR Data and Its Application to Surface Roughness Mapping Along the Gulf Coast

During a flooding event, the ability of the terrain to dissipate water flow energy depends on its land-cover type and the associated surface roughness. In this study, we developed a new land-cover classification algorithm using repeat-pass polarimetric synthetic aperture radar (PolSAR) and interferometric synthetic aperture radar (InSAR) data. Through a two-level hierarchical approach, we classified nine land-cover types with distinct surface roughness coefficients (Manning’s <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> ). We demonstrated the performance of this algorithm using available L-band ALOS PALSAR scenes acquired between April 2007 and April 2011 over the Houston area. The radar-based surface roughness estimates show a good agreement with those independently derived from NOAA’s 22-class Coastal Change Analysis Program (C-CAP) 2010 land-cover classification data. Our algorithm is robust, and the randomly selected training sets only account for 0.3% of the total multilooked radar pixels (30-m spacing). Furthermore, we were able to accurately map surface roughness over the New Orleans area using available ALOS PALSAR scenes without selecting any new training sets. We note that NOAA’s C-CAP data are currently used for estimating surface roughness in the operational storm-surge models, and a new version is typically released every five to six years. With the launch of the L-band NASA-ISRO Synthetic Aperture Radar (NISAR) mission in the near future, our algorithm can be used to fill the temporal gaps of the existing C-CAP-based surface roughness database and improve the accuracy of near real-time hydrodynamic modeling.

A hierarchical approach for discrete-event model identification incorporating expert knowledge

Abstract Recently, a new technique for identification of Discrete-Event Systems (DES) with the aim of fault detection has been proposed in the literature, where a model is obtained from the observation of the fault-free system behavior. In some cases, the system may execute different tasks in a sequential order to perform the complete operation of the system. In these cases, the number of observed paths representing the complete system operation may grow exponentially with the number of tasks. In addition, by using black-box identification methods, it is possible that the sequential order that the tasks must be performed is not represented in the model, reducing the fault detection capability or delaying the fault detection. In this paper, a two-level hierarchical approach for DES identification is proposed. In the higher level of the model hierarchy, the system is described by using some basic knowledge of its functioning provided by an expert, and in the lower level of the hierarchy, the behavior is described by black-box identified models for the system tasks. The modeling framework proposed in this paper reduces the number of observed paths needed for system identification and increases the fault detection capability. A practical example is used to illustrate the results of the paper.

Fast finite-difference time-domain (FDTD) method of two dimensional target scattering calculation by two-level hierarchical approach

This paper presents a fast finite-difference time-domain (FDTD) method to calculate the scattering of heterogeneous target by utilizing effective electrical properties of the material in local areas. The heterogeneous target sometimes can be a really large model, which may be time-consuming and requires significant computer resources to finish the scattering computation using FDTD method. For complex target, the grid must be very small to fit the detail of the material's distribution. To overcome this issue, we propose a two-level hierarchical approach to reduce the grid number of the model in scattering calculation. In level one, the model is partitioned to many small grids to fit the target's heterogeneous characteristics. Each grid is assigned a value of electrical property at that position. In level two, the model is then partitioned to big grids, each of which covers several small grids. The electrical property in each big grid is treated as homogeneous and the electrical value is obtained with the values of the small grids within it. These big grids are then applied in FDTD method to calculate the scattering of the target. Finite-difference method (FDM) and Bruggeman formula are utilized to calculate the effective property in each big grid. FDM can give the anisotropic properties of the material, while Bruggeman formula can only yield isotropic ones. Numerical experiment of a human head slice is conducted and the results show the accuracy and efficiency of the two-level hierarchical method.

Balancing Message Criticality and Timeliness in IoT Networks

We study the problem of balancing timeliness and criticality when gathering data from multiple sources using a two-level hierarchical approach. The devices that generate the data transmit them to a local hub. A central decision maker then has to decide which local hubs to allocate bandwidth to and the local hubs have to prioritize the messages they transmit when given the opportunity to do so. Whereas an optimal policy does exist for this problem such a policy would require global knowledge of messages at each local hub, rendering such a scheme impractical. We propose a distributed reinforcement-learning-based approach that accounts for both the timeliness requirements and criticality of messages. We evaluate our solution using a criticality-weighted deadline miss ratio as the performance metric. The performance analysis is done by simulating the behavior of the proposed policy as well as that of several natural policies under a wide range of system conditions. The results show that the proposed policy outperforms all the other policies - except for the optimal but impractical policy - under the range of system conditions studied and that in many cases it performs close (3% to 12% lower performance depending on the condition) to the optimal policy.

Automatic deformable PET/MRI registration for preclinical studies based on B-splines and non-linear intensity transformation.

PET images deliver functional data, whereas MRI images provide anatomical information. Merging the complementary information from these two modalities is helpful in oncology. Alignment of PET/MRI images requires the use of multi-modal registration methods. Most of existing PET/MRI registration methods have been developed for humans and few works have been performed for small animal images. We proposed an automatic tool allowing PET/MRI registration for pre-clinical study based on a two-level hierarchical approach. First, we applied a non-linear intensity transformation to the PET volume to enhance. The global deformation is modeled by an affine transformation initialized by a principal component analysis. A free-form deformation based on B-splines is then used to describe local deformations. Normalized mutual information is used as voxel-based similarity measure. To validate our method, CT images acquired simultaneously with the PET on tumor-bearing mice were used. Results showed that the proposed algorithm outperformed affine and deformable registration techniques without PET intensity transformation with an average error of 0.72 ± 0.44mm. The optimization time was reduced by 23% due to the introduction of robust initialization. In this paper, an automatic deformable PET-MRI registration algorithm for small animals is detailed and validated. Graphical abstract ᅟ.

A Storage Integrated Modular Power Electronic Interface for Higher Power Distribution Availability

This paper proposes active power distribution nodes (APDNs) as an effective interface that can improve availability of power distribution networks. APDNs can be used as a modular interface block for critical power networks, such as secure facilities and vehicular platforms. By having bidirectional power flow capability and embedded energy storage, APDNs are able to increase both operational flexibility and availability of power networks. The embedded energy storage can minimize potential power interruptions experienced by the load. In addition, modularity is also inherent in the configuration of APDNs. The advantages of using APDNs as a connection interface inside a power network are discussed from an availability point of view in a quantitative manner by performing a comparison using Markov-based availability models. The configuration, characteristics, and operation states of the APDN are introduced, and a two-level hierarchical control approach that regulates the voltage/current level and performs energy management is discussed. The design is verified with simulation and experiments.

Hierarchical scheduling and disturbance management in the process industry

The integration of scheduling and control in the process industry is a topic that has been frequently discussed during the recent years, but many challenges remain in order to obtain integrated solutions that may be implemented at large-scale industrial sites. This paper introduces a general framework for production scheduling (PS) and detailed production scheduling (DPS) using a two-level hierarchical approach. The PS activity generates a monthly production schedule based on information on orders and forecasts, and the DPS activity handles disturbances in production on an hourly basis. The focus is on disturbances in the supply of utilities, which often cause great losses at process industrial sites. The research has been conducted in close collaboration with Perstorp, a world-leading company within several sectors of the specialty chemicals market. A specification list provided by Perstorp has been used as a starting point for formulating the PS and DPS activities as optimization problems. An example that is inspired by a real industrial site is presented to show how the PS and DPS may operate and how the integration of these two functions behaves.

Production and maintenance planning for a failure-prone deteriorating manufacturing system: a hierarchical control approach

The work presented in this paper examines the joint analysis of the optimal production and maintenance planning policies for a manufacturing system subject to random failures and repairs. When a machine fails, an imperfect corrective maintenance is undertaken. The objective of this study is to minimize a discounted overall cost consisting of preventive and corrective maintenance costs, inventory holding cost, and backlog cost. A two-level hierarchical decision-making approach is proposed, based on the determination of the mean time to failure (first level) and the statement of a joint optimization of production, preventive, and corrective maintenance policies (second level). Hence, the production, preventive, and corrective maintenance rates are determined in the second level, given the failure rates obtained from the first level. In the proposed model, the machine’s failure rate depends on the number of imperfect repairs, and as a result, the control policies of the considered planning problems therefore depend on the number of failures. The structure of the optimal control policies and the usefulness of the proposed approach are illustrated through a numerical example and a sensitivity analysis.

Two-Level Hierarchical Approach to Unit Commitment Using Expert System and Elite PSO

This paper presents a two-level hierarchical approach that combines an expert system (ES) with an elite particle swarm optimization (EPSO) to solve the unit commitment (UC) problem. Existing UC solution methods have the problems of stopping at a local optimum and slow convergence when applied to large-scale, heavily constrained UC applications. In this work, an ES is developed to handle all constraints. The ES is initially used as a pre-dispatch tool to create a robust swarm. Then, the ES and EPSO are combined to seek the optimal solution. All constraints are incorporated and satisfied during the pre-dispatch and the evolution process. Hence, infeasible positions in the solution space will not be visited. The execution time of the proposed approach grows approximately linearly, rather than geometrically, with problem size. This feature is attractive in large-scale systems. The proposed approach is successfully applied to a popular test system of up to 100 units and the real Taipower 40-unit 168-h system. Both solution cost and execution time are superior to those of published methods. Total fuel costs of the proposed approach are several millions of dollars less than those of existing methods in the Taipower case.

Core Routing on Dynamic Time-Dependent Road Networks

Route planning in large-scale time-dependent road networks is an important practical application of the shortest-path problem that greatly benefits from speedup techniques. In this paper, we extend a two-level hierarchical approach for point-to-point shortest-path computations to the time-dependent case. This method, also known as core routing in the literature for static graphs, consists of the selection of a small subnetwork where most of the computations can be carried out, thus reducing the search space. We combine this approach with bidirectional goal-directed search to obtain an algorithm capable of finding shortest paths in a matter of milliseconds on continental-sized networks. Moreover, we tackle the dynamic scenario where the piecewise linear functions that we use to model time-dependent arc costs are not fixed but can have their coefficients updated requiring only a small computational effort.

Piecewise output feedback control for affine systems with disturbances based on linear temporal logic specifications

In the paper, we investigate the problem of finding a piecewise output feedback control law for an uncertain affine system such that the resulting closed-loop output satisfies a desired linear temporal logic (LTL) specification. A two-level hierarchical approach is proposed to solve the problem in a triangularized output space. In the lower level, we explore whether there exists a robust output feedback control law to make the output starting in a simplex either remains in it or leaves via a specific facet. In the higher level, for the triangularization, we construct the transition system according to the reachability relationship obtained in the lower level and search for feasible paths that meet the LTL specification. The control approach is then applied to solve a motion planning problem.

Development of a New Direct Shear Testing Apparatus

Politecnico di Torino and G.D.S. Instruments Ltd, UK has developed an innovative servo controlled direct shear apparatus named High Pressure Back Pressure Shear Apparatus-HPBPSA. The apparatus allows direct shear tests to be performed under closely controlled conditions in terms of axial load and back pressure applied to the specimen. Two shear boxes, built in stainless steel, are available to host cylindrical specimens of 50 or 100 mm diameter and 50 mm height. If a test is to be performed on a joint, different gaps up to a maximum of may be set between the two halves of the shear box. The apparatus is equipped with a number of measuring transducers that allow for testing to be performed in closely controlled conditions. The axial load is applied to the specimen by a transverse steel bar joined both to the loading ram and to the two uprights. The acquisition and control system is based on a two-level hierarchical approach. The novel equipment allows direct shear tests to be performed on either soil or soft rock

2D shape morphing via automatic feature matching and hierarchical interpolation

The paper presents a new method to interpolate a pair of 2D shapes that are represented by piecewise linear curves. The method addresses two key problems in 2D shape morphing process: feature correspondence and path interpolation. First, a robust feature metric is defined to measure the similarity of a pair of 2D shapes in terms of visual appearance, orientation and relative size. Based on the metric, an optimal problem is defined and solved to associate the features on the source shape with the corresponding ones on the target shape. Then, a two-level hierarchical approach is proposed to solve the corresponding features interpolation trajectory problem. The algorithm decomposes the input shapes into a pair of corresponding coarse polygons and several pairs of corresponding features. Then the corresponding coarse polygons are interpolated in an as-rigid-as-possible plausible way; meanwhile the corresponding features are interpolated using the intrinsic method. Thus interior distortions of the intermediate shapes could be avoided and the feature details on the input shapes could be well preserved. Experimental results show that the method can generate smooth, natural and visually pleasing 2D shape morphing effects.

Connectivity independent protein-structure alignment: a hierarchical approach.

BackgroundProtein-structure alignment is a fundamental tool to study protein function, evolution and model building. In the last decade several methods for structure alignment were introduced, but most of them ignore that structurally similar proteins can share the same spatial arrangement of secondary structure elements (SSE) but differ in the underlying polypeptide chain connectivity (non-sequential SSE connectivity).ResultsWe perform protein-structure alignment using a two-level hierarchical approach implemented in the program GANGSTA. On the first level, pair contacts and relative orientations between SSEs (i.e. α-helices and β-strands) are maximized with a genetic algorithm (GA). On the second level residue pair contacts from the best SSE alignments are optimized. We have tested the method on visually optimized structure alignments of protein pairs (pairwise mode) and for database scans. For a given protein structure, our method is able to detect significant structural similarity of functionally important folds with non-sequential SSE connectivity. The performance for structure alignments with strictly sequential SSE connectivity is comparable to that of other structure alignment methods.ConclusionAs demonstrated for several applications, GANGSTA finds meaningful protein-structure alignments independent of the SSE connectivity. GANGSTA is able to detect structural similarity of protein folds that are assigned to different superfamilies but nevertheless possess similar structures and perform related functions, even if these proteins differ in SSE connectivity.

Hierarchical constraint transformation based on genetic optimization for analog system synthesis

In a top–down analog system design methodology, the task of translating high-level performance specifications and constraints into component level parameters is termed as constraint transformation. In this paper, we presented a genetic optimization based approach to constraint transformation. The salient features of this are a search space profiling technique and a hierarchical two-level genetic optimization engine. Performance estimation for profiling and hierarchical optimization uses both the performance equations embedded in an analog performance estimation module as well as detailed SPICE simulation. We described the constraint transformation method, and each of its constituents. The effectiveness of the two-level hierarchical approach was established by comparing it against a flat non-hierarchical method. Application of the constraint transformation method in the synthesis of design examples was also presented in the paper.

Cockle formation in coated magazine paper production

In this work, a two level hierarchical approach was proposed and used to analyze cockle phenomena found in the paper manufacturing process. The hierarchy is general for process that produces 3D data sets. The first level performs as a 3D decomposition (Tucker3) while the second level employs a 2D analysis (PLS) of the loadings supplied by the first level. The analysis showed that the important sections of the paper machine that contributed to cockle were Kraft refiners, etc. These are the locations that are expected to contribute to cockle from process knowledge. A calibration model was then developed. The predictive nature of the model was shown to be satisfactory to predict cockle index for reel data that were not a part of the calibration data. Online monitoring and detection applications are possible with this two-level hierarchical structure. It is necessary to use PARAFAC to detect when to recalibrate because the core is unique and every new decomposition is similar to a projection onto the same core. With respect to the use of two-level hierarchical approach to similar processes, such as fiber spinning or continuous casting. It is believed that the two-level hierarchy may be used successfully, at least for diagnosis. However, it should be carefully assessed if the assumptions made in the present case hold in other cases. Additional research is needed to confirm the two-level hierarchy for other processes.