arXiv subjects

Mogens Graf Plessen

Publications and source records attributed to Mogens Graf Plessen.

14 recordsLinked to original sources

arXiv2023

A posteriori Trading-inspired Model-free Time Series Segmentation

Within the context of multivariate time series segmentation this paper proposes a method inspired by a posteriori optimal trading. After a normalization step time series are treated channel-wise as surrogate stock prices that can be traded optimally a posteriori in a virtual portfolio holding either stock or cash. Linear transaction costs are interpreted as hyperparameters for noise filtering. Resulting trading signals as well as resulting trading signals obtained on the reversed time series are used for unsupervised labeling, before a consensus over channels is reached that determines segmentation time instants. The method is model-free such that no model prescriptions for segments are made. Benefits of proposed approach include simplicity, adaptability to a wide range of different shapes of time series, and in particular computational efficiency that make it suitable for big data. Performance is demonstrated on synthetic and real-world data, including a large-scale dataset comprising a multivariate time series of dimension 1000 and length 2709. Proposed method is compared to a popular model-based bottom-up approach fitting piecewise affine models and to a state-of-the-art model-based top-down approach fitting Gaussian models, and found to be consistently faster while producing more intuitive results.

stat.ML↗

arXiv2020

GPU-accelerated Logistics Optimisation for Biomass Production with Multiple Simultaneous Harvesters Tours, Fields and Plants

Within the context of biomass production, this paper proposes a method for logistics optimisation. Starting from a headquarter multiple tours are to be executed simultaneously by groups of harvesting units (HUs) and support units (SUs) to first harvest biomass from multiple agricultural fields, before supplying the biomass to multiple biogas plants (BPs) via shuttling SUs. This problem is relevant on a larger scale in particular for contractors. This problem is complex since there are three interconnected optimisation levels: (i) the assignment of BPs to tours and the ordering of BPs assigned to each tour, (ii) the assignment of fields to BPs and the ordering of fields assigned to each BP, and (iii) determining the number of HUs and SUs assigned to each tour, whereby different HUs and SUs may in general have different working rates and loading capacities. Problem modeling and a solution method are discussed. For the latter, a GPU-accelerated heuristic search algorithm is proposed. For the former, an optimisation criterion minimising both total accumulated path length and the maximum completion time over all harvesters tours, an embedded local minimisation for the assignment of SUs to tours, and demand fulfilment constraints for BPs are discussed. In stochastic simulation experiments it is found that permitting unconstrained assignment of any available field that a contractor services to any available BP, independent of their ownerships, is crucial (i) to attain maximum path length savings, and (ii) also to best balance and minimise uniform completion times over all harvesters tours such that weather-dependent harvesting windows can be exploited optimally.

eess.SY↗

arXiv2020

Integrated Time Series Summarization and Prediction Algorithm and its Application to COVID-19 Data Mining

This paper proposes a simple method to extract from a set of multiple related time series a compressed representation for each time series based on statistics for the entire set of all time series. This is achieved by a hierarchical algorithm that first generates an alphabet of shapelets based on the segmentation of centroids for clustered data, before labels of these shapelets are assigned to the segmentation of each single time series via nearest neighbor search using unconstrained dynamic time warping as distance measure to deal with non-uniform time series lenghts. Thereby, a sequence of labels is assigned for each time series. Completion of the last label sequence permits prediction of individual time series. Proposed method is evaluated on two global COVID-19 datasets, first, for the number of daily net cases (daily new infections minus daily recoveries), and, second, for the number of daily deaths attributed to COVID-19 as of April 27, 2020. The first dataset involves 249 time series for different countries, each of length 96. The second dataset involves 264 time series, each of length 96. Based on detected anomalies in available data a decentralized exit strategy from lockdowns is advocated.

stat.AP↗

arXiv2019

Freeform Path Fitting for the Minimisation of the Number of Transitions between Headland Path and Interior Lanes within Agricultural Fields

Within the context of in-field path planning this paper discusses freeform path fitting for the minimisation of the number of transitions between headland path and interior lanes within agricultural fields. This topic is motivated by two observations. Due to crossings of tyre traces such transitions in practice often cause an increase of compacted area. Furthermore, for very tight angles between headland path and interior lanes undesired hairpin turns may become necessary due to the limited agility of in-field operating tractors. By minimising the number of interior lanes both detrimental effects can be mitigated. The potential of minimising the number of interior lanes by freeform path fitting is evaluated on 10 non-convex real-world fields including obstacle areas, and compared to the more common technique of fitting straight interior lanes.

eess.SY↗

arXiv2019

Optimal In-field Routing for Full and Partial Field Coverage with Arbitrary Non-Convex Fields and Multiple Obstacle Areas

Within the context of optimising the logistics in agriculture this paper relates to optimal in-field routing for full and partial field coverage with arbitrary non-convex fields and multiple obstacle areas. It is distinguished between nine different in-field routing tasks: two for full-field coverage, seven for partial-field coverage and one for shortest path planning between any two vertices of the transition graph. It differentiates between equal or different start and end vertices for a task, coverage of only a subset of vertices, and a subset of edges or combinations. The proposed methods are developed primarily for applying sprays and fertilisers with larger operating widths and with fields where there is unique headland path. Partial field coverage where, e.g., only a specific subset of edges has to be covered is relevant for precision agriculture and also for optimised logistical operation of smaller-sized machinery with limited loading capacities. The result of this research is the proposition of two compatible algorithms for optimal full and partial field coverage path planning, respectively. These are evaluated on three real-world fields to demonstrate their characteristics and computational efficiency.

eess.SY↗

arXiv2019

Coupling of Crop Assignment and Vehicle Routing for Harvest Planning in Agriculture

A method for harvest planning based on the coupling of crop assignment with vehicle routing is presented. Given a setting with multiple fields, a path network connecting these, multiple depots at which a number of harvesters are initially located, the main question addressed is: Which crop out of a set of different crops to assign to each field when accounting for the given setting? It must be answered by every farm manager at the beginning of every work-cycle starting with plant seeding and ending with harvesting. Rather than solving an assignment problem only, it is here also accounted for the connectivity between fields. In practice, fields are often located distant apart. Traveling costs of machinery and limited harvesting windows demand optimised operation and route planning. Therefore, the proposed method outputs crop assignments to fields and simultaneously determines crop-tours, i.e., optimised sequences in which to service fields of the same crop during harvest. It is of particular relevance for larger farms and groups of farms that collaborate and share machinery. Integer programming based exact algorithms are derived. For large numbers of fields, where these algorithms may not be tractable due to computational constraints, elements of clustering and the solution of local Traveling Salesman Problems are added, thereby on the one hand rendering the method heuristic and in general suboptimal, but on the other hand maintaining large-scale applicability.

eess.SY↗

arXiv2019

Online Sampling in the Parameter Space of a Neural Network for GPU-accelerated Motion Planning of Autonomous Vehicles

This paper proposes online sampling in the parameter space of a neural network for GPU-accelerated motion planning of autonomous vehicles. Neural networks are used as controller parametrization since they can handle nonlinear non-convex systems and their complexity does not scale with prediction horizon length. Network parametrizations are sampled at each sampling time and then held constant throughout the prediction horizon. Controls still vary over the prediction horizon due to varying feature vectors fed to the network. Full-dimensional vehicles are modeled by polytopes. Under the assumption of obstacle point data, and their extrapolation over a prediction horizon under constant velocity assumption, collision avoidance reduces to linear inequality checks. Steering and longitudinal acceleration controls are determined simultaneously. The proposed method is designed for parallelization and therefore well-suited to benefit from continuing advancements in hardware such as GPUs. Characteristics of proposed method are illustrated in 5 numerical simulation experiments including dynamic obstacle avoidance, waypoint tracking requiring alternating forward and reverse driving with maximal steering, and a reverse parking scenario.

cs.RO↗

arXiv2018

Encoding Motion Primitives for Autonomous Vehicles using Virtual Velocity Constraints and Neural Network Scheduling

Within the context of trajectory planning for autonomous vehicles this paper proposes methods for efficient encoding of motion primitives in neural networks on top of model-based and gradient-free reinforcement learning. It is distinguished between 5 core aspects: system model, network architecture, training algorithm, training tasks selection and hardware/software implementation. For the system model, a kinematic (3-states-2-controls) and a dynamic (16-states-2-controls) vehicle model are compared. For the network architecture, 3 feedforward structures are compared including weighted skip connections. For the training algorithm, virtual velocity constraints and network scheduling are proposed. For the training tasks, different feature vector selections are discussed. For the implementation, aspects of gradient-free learning using 1 GPU and the handling of perturbation noise therefore are discussed. The effects of proposed methods are illustrated in experiments encoding up to 14625 motion primitives. The capabilities of tiny neural networks with as few as 10 scalar parameters when scheduled on vehicle velocity are emphasized.

cs.RO↗

arXiv2018

Automating Vehicles by Deep Reinforcement Learning using Task Separation with Hill Climbing

Within the context of autonomous driving a model-based reinforcement learning algorithm is proposed for the design of neural network-parameterized controllers. Classical model-based control methods, which include sampling- and lattice-based algorithms and model predictive control, suffer from the trade-off between model complexity and computational burden required for the online solution of expensive optimization or search problems at every short sampling time. To circumvent this trade-off, a 2-step procedure is motivated: first learning of a controller during offline training based on an arbitrarily complicated mathematical system model, before online fast feedforward evaluation of the trained controller. The contribution of this paper is the proposition of a simple gradient-free and model-based algorithm for deep reinforcement learning using task separation with hill climbing (TSHC). In particular, (i) simultaneous training on separate deterministic tasks with the purpose of encoding many motion primitives in a neural network, and (ii) the employment of maximally sparse rewards in combination with virtual velocity constraints (VVCs) in setpoint proximity are advocated.

cs.LG↗

arXiv2018

A posteriori multi-stage optimal trading under transaction costs and a diversification constraint

This paper presents a simple method for a posteriori (historical) multi-variate multi-stage optimal trading under transaction costs and a diversification constraint. Starting from a given amount of money in some currency, we analyze the stage-wise optimal allocation over a time horizon with potential investments in multiple currencies and various assets. Three variants are discussed, including unconstrained trading frequency, a fixed number of total admissable trades, and the waiting of a specific time-period after every executed trade until the next trade. The developed methods are based on efficient graph generation and consequent graph search, and are evaluated quantitatively on real-world data. The fundamental motivation of this work is preparatory labeling of financial time-series data for supervised machine learning.

q-fin.PM↗

arXiv2018

Partial Field Coverage Based on Two Path Planning Patterns

This paper presents a path planning method for partial field coverage. Therefore, a specific path planning pattern is proposed. The notion is that lighter machinery with smaller storage tanks can alleviate soil compaction because of a reduced weight, but does not enable full field coverage in a single run because of the smaller storage capacity. This is relevant for spraying applications and related in-field work. Consequently, multiple returns to a mobile or stationary depot located outside of the field are required for storage tank refilling. Therefore, a suitable path planning method is suggested that accounts for the limited turning radii of agricultural vehicles, satisfies compacted area minimisation constraints, and aims at overall path length minimisation. The benefits of the proposed method are illustrated by means of a comparison to a planning method based on the more common AB pattern. It is illustrated how the proposed path planning pattern can also be employed efficiently for single-run field coverage.

eess.SY↗

arXiv2017

Stock Trading via Feedback Control: Stochastic Model Predictive or Genetic?

We seek a discussion about the most suitable feedback control structure for stock trading under the consideration of proportional transaction costs. Suitability refers to robustness and performance capability. Both are tested by considering different one-step ahead prediction qualities, including the ideal case, correct prediction of the direction of change in daily stock prices and the worst-case. Feedback control structures are partitioned into two general classes: stochastic model predictive control (SMPC) and genetic. For the former class three controllers are discussed, whereby it is distinguished between two Markowitz- and one dynamic hedging-inspired SMPC formulation. For the latter class five trading algorithms are disucssed, whereby it is distinguished between two different moving average (MA) based, two trading range (TR) based, and one strategy based on historical optimal (HistOpt) trajectories. This paper also gives a preliminary discussion about how modified dynamic hedging-inspired SMPC formulations may serve as alternatives to Markowitz portfolio optimization. The combinations of all of the eight controllers with five different one-step ahead prediction methods are backtested for daily trading of the 30 components of the German stock market index DAX for the time period between November 27, 2015 and November 25, 2016.

cs.CE↗

arXiv2017

Trajectory Planning of Automated Vehicles in Tube-like Road Segments

This paper presents a method based on linear programming for trajectory planning of automated vehicles, combining obstacle avoidance, time scheduling for the reaching of waypoints and time-optimal traversal of tube-like road segments. System modeling is conducted entirely spatial-based. Kinematic vehicle dynamics as well as time are expressed in a road-aligned coordinate frame with path along the road centerline serving as the dependent variable. We elaborate on control rate constraints in the spatial domain. A vehicle dimension constraint heuristic is proposed to constrain vehicle dimensions inside road boundaries. It is outlined how friction constraints are accounted for. The discussion is extended to dynamic vehicle models. The benefits of the proposed method are illustrated by a comparison to a time-based method.

eess.SY↗

arXiv2017

Trajectory Planning Under Vehicle Dimension Constraints Using Sequential Linear Programming

This paper presents a spatial-based trajectory planning method for automated vehicles under actuator, obstacle avoidance, and vehicle dimension constraints. Starting from a nonlinear kinematic bicycle model, vehicle dynamics are transformed to a road-aligned coordinate frame with path along the road centerline replacing time as the dependent variable. Space-varying vehicle dimension constraints are linearized around a reference path to pose convex optimization problems. Such constraints do not require to inflate obstacles by safety-margins and therefore maximize performance in very constrained environments. A sequential linear programming (SLP) algorithm is motivated. A linear program (LP) is solved at each SLP-iteration. The relation between LP formulation and maximum admissible traveling speeds within vehicle tire friction limits is discussed. The proposed method is evaluated in a roomy and in a tight maneuvering driving scenario, whereby a comparison to a semi-analytical clothoid-based path planner is given. Effectiveness is demonstrated particularly for very constrained environments, requiring to account for constraints and planning over the entire obstacle constellation space.

eess.SY↗