scholarly journals An Algorithm for the Broad Evaluation of Potential Matches between Actuator Concepts and Heavy-Duty Mobile Applications

Actuators ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 111
Author(s):  
David Fassbender ◽  
Tatiana Minav
Keyword(s):  
Energy Efficiency ◽  
Solution Space ◽  
Heavy Duty ◽  
Large Solution ◽  
Evaluation Algorithm ◽  
Actuator Application ◽  
Novel Concept ◽  
Hydraulic Valve ◽  
Performance Demonstration ◽  
Modular Nature

In recent years, a variety of novel actuator concepts for the implements of heavy-duty mobile machines (HDMMs) has been proposed by industry and academia. Mostly, novel concepts aim at improving the typically low energy efficiency of state-of-the-art hydraulic valve-controlled actuators. However, besides energy-efficiency, many aspects that are crucial for a successful concept integration are often neglected in studies. Furthermore, most of the time, a specific HDMM is focused as an application while other HDMM types can show very different properties that might make a novel concept less suitable. In order to take more aspects and HDMM types into account when evaluating actuator concepts, this paper proposes a novel evaluation algorithm, which calculates so-called mismatch values for each potential actuator-application match, based on different problem aspects that can indicate a potential mismatch between a certain actuator concept and an HDMM. The lower the mismatch value, which depends on actuator characteristics as well as HDMM attributes, the more potential is the match. At the same time, the modular nature of the algorithm allows to evaluate a large number of possible matches at once, with low effort. For the performance demonstration of the algorithm, 36 potential matches formed out of six actuator concepts and six HDMM types are exemplarily evaluated. The resulting actuator concept ratings for the six different HDMMs are in line with general reasoning and confirm that the evaluation algorithm is a powerful tool to get a first, quick overview of a large solution space of actuator-HDMM matches. However, analyzing the limitations of the algorithm also shows that it cannot replace conventional requirements engineering and simulation studies if detailed and reliable results are required.

scholarly journals Finding the Perfect Match: Different Heavy-Duty Mobile Applications Call for Different Actuators

Proceedings ◽  
2020 ◽  
Vol 64 (1) ◽  
pp. 22
Author(s):  
David Fassbender ◽  
Tatina Minav
Keyword(s):  
Mobile Applications ◽  
State Of The Art ◽  
Perfect Match ◽  
The Novel ◽  
Hydraulic Systems ◽  
Heavy Duty ◽  
Different Types ◽  
Evaluation Algorithm ◽  
Novel Concept ◽  
Valve Control

For the longest time, valve-controlled, centralized hydraulic systems have been the state-of-the-art technology to actuate heavy-duty mobile machine (HDMM) implements. Due to the typically low energy efficiency of those systems, a high number of promising, more-efficient actuator concepts has been proposed by academia as well as industry over the last decades as potential replacements for valve control—e.g., independent metering, displacement control, different types of electro-hydraulic actuators (EHAs), electro-mechanic actuators, or hydraulic transformers. This paper takes a closer look on specific HDMM applications for these actuator concepts to figure out where which novel concept can be a better alternative to conventional actuator concepts, and where novel concepts might fail to improve. For this purpose, a novel evaluation algorithm for actuator–HDMM matches is developed based on problem aspects that can indicate an unsuitable actuator–HDMM match. To demonstrate the functionality of the match evaluation algorithm, four actuator concepts and four HDMM types are analyzed and rated in order to form 16 potential actuator–HDMM matches that can be evaluated by the novel algorithm. The four actuator concepts comprise a conventional valve-controlled concept and three different types of EHAs. The HDMM types are excavator, wheel loader, backhoe, and telehandler. Finally, the evaluation of the 16 matches results in 16 mismatch values, of which the lowest indicates the “perfect match”. Low mismatch values could be found in general for EHAs in combination with most HDMMs but also for a valve-controlled actuator concept in combination with a backhoe. Furthermore, an analysis of the concept limitations with suggestions for improvement is included.

Study on energy efficiency characteristics of the heavy-duty manipulator driven by electro-hydraulic hybrid active-passive system

2021 ◽  
Vol 125 ◽  
pp. 103646
Author(s):  
Zepeng Li ◽  
Chengwen Wang ◽  
Long Quan ◽  
Yunxiao Hao ◽  
Lei Ge ◽  
...  
Keyword(s):  
Energy Efficiency ◽  
Heavy Duty ◽  
Passive System ◽  
Hydraulic Hybrid

A Novel Concept for Energy-Optimal, Independent-Phase Control of Brushless Motor Drivers

2021 ◽  
pp. 1-11
Author(s):  
Amin Ghorbanpour ◽  
Hanz Richter
Keyword(s):  
Energy Efficiency ◽  
Energy Consumption ◽  
Robot Motion ◽  
Control Law ◽  
Outer Loop ◽  
Energy Regeneration ◽  
Brushless Motor ◽  
Control Scheme ◽  
Novel Concept ◽  
Constrained Quadratic Optimization

Abstract In this work, a new drive concept for brushless direct current (BLDC) motors is introduced. Energy regeneration is optimally managed with the aim of improving the energy efficiency of robot motion controls. The proposed scheme has three independent regenerative drives interconnected in a wye configuration. An augmented model of the robot, joint mechanisms, and BLDC motors is formed, and then a voltage-based control scheme is developed. The control law is obtained by specifying an outer-loop torque controller followed by minimization of power consumption via online constrained quadratic optimization. An experiment is conducted to assess the performance of the proposed concept against an off-the-shelf driver. It is shown that, in terms of energy regeneration and consumption, the developed driver has better performance. Furthermore, the proposed concept showed a reduction of 15% energy consumption for the conditions of the study.

scholarly journals Summarizing the solution space in tumor phylogeny inference by multiple consensus trees

2019 ◽  
Vol 35 (14) ◽  
pp. i408-i416 ◽  
Author(s):  
Nuraini Aguse ◽  
Yuanyuan Qi ◽  
Mohammed El-Kebir
Keyword(s):  
Solution Space ◽  
Simulated Data ◽  
Exact Algorithm ◽  
Real Data ◽  
Supplementary Information ◽  
Mixed Integer ◽  
Consensus Tree ◽  
Large Solution ◽  
Consensus Trees ◽  
Topological Features

Abstract Motivation Cancer phylogenies are key to studying tumorigenesis and have clinical implications. Due to the heterogeneous nature of cancer and limitations in current sequencing technology, current cancer phylogeny inference methods identify a large solution space of plausible phylogenies. To facilitate further downstream analyses, methods that accurately summarize such a set T of cancer phylogenies are imperative. However, current summary methods are limited to a single consensus tree or graph and may miss important topological features that are present in different subsets of candidate trees. Results We introduce the Multiple Consensus Tree (MCT) problem to simultaneously cluster T and infer a consensus tree for each cluster. We show that MCT is NP-hard, and present an exact algorithm based on mixed integer linear programming (MILP). In addition, we introduce a heuristic algorithm that efficiently identifies high-quality consensus trees, recovering all optimal solutions identified by the MILP in simulated data at a fraction of the time. We demonstrate the applicability of our methods on both simulated and real data, showing that our approach selects the number of clusters depending on the complexity of the solution space T. Availability and implementation https://github.com/elkebir-group/MCT. Supplementary information Supplementary data are available at Bioinformatics online.

Constraining solution space to improve generalization

1997 ◽  
Vol 20 (1) ◽  
pp. 67-68
Author(s):  
John A. Bullinaria
Keyword(s):  
Learning Algorithms ◽  
Solution Space ◽  
Local Information ◽  
Learning Processes ◽  
Point Of View ◽  
Heavy Duty ◽  
Hidden Regularities ◽  
Evolutionary Point

I suggest that the difficulties inherent in discovering the hidden regularities in realistic (type-2) problems can often be resolved by learning algorithms employing simple constraints (such as symmetry and the importance of local information) that are natural from an evolutionary point of view. Neither “heavy-duty nativism” nor “representational recoding” appear to offer totally appropriate descriptions of such natural learning processes.

Analysis of Pavement Management Activities Programming by Genetic Algorithms

1998 ◽  
Vol 1643 (1) ◽  
pp. 1-6 ◽  
Author(s):  
T. F. Fwa ◽  
W. T. Chan ◽  
K. Z. Hoque
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Solution Space ◽  
Pavement Management ◽  
Practical Significance ◽  
Optimization Analysis ◽  
Large Solution ◽  
Pavement Management System ◽  
Pavement Maintenance ◽  
Operational Characteristics

The application of genetic algorithms to programming of pavement maintenance activities at the network level is demonstrated. The operational characteristics of the genetic algorithm technique and its relevance to solving the programming problem in a Pavement Management System (PMS) are discussed. The robust search capability of genetic algorithms enables them to effectively handle the highly constrained problem of pavement management activities programming, which has an extremely large solution space of astronomical scale. Examples are presented to highlight the versatility of genetic algorithms in accommodating different objective function forms. This versatility makes the algorithms an effective tool for planning in PMS. It is also demonstrated that composite objective functions that combine two or more different objectives can be easily considered without having to reformulate the genetic algorithm computer program. Another useful feature of genetic algorithm solutions is the availability of near-optimal solutions besides the "best" solution. This has practical significance as it gives the users the flexibility to examine the suitability of each solution when practical constraints and factors not included in the optimization analysis are considered.

Modeling Individuals’ Activity-Travel Rescheduling Heuristics: Theory and Numerical Experiments

2002 ◽  
Vol 1807 (1) ◽  
pp. 16-25 ◽  
Author(s):  
Chang-Hyeon Joh ◽  
Theo Arentze ◽  
Harry Timmermans
Keyword(s):  
Decision Making ◽  
Numerical Experiments ◽  
Solution Space ◽  
Combinatorial Problem ◽  
Face Validity ◽  
Decision Making Process ◽  
Large Solution ◽  
Numerical Illustration ◽  
The Face ◽  
Actual Decision

Previously, a theory of activity-travel rescheduling decisions was developed. This theory left open the problem of how individuals deal with the combinatorial problem of a very large solution space. Based on the argument that an appropriate algorithm should also be interpreted as a representation of an actual decision-making process, such an algorithm for activity-travel rescheduling is proposed here. Details are described, and a numerical illustration is provided to explore the face validity of the proposed algorithm.

scholarly journals DDPG-Based Energy-Efficient Flow Scheduling Algorithm in Software-Defined Data Centers

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Zan Yao ◽  
Ying Wang ◽  
Luoming Meng ◽  
Xuesong Qiu ◽  
Peng Yu
Keyword(s):  
Energy Consumption ◽  
Energy Saving ◽  
Network Performance ◽  
Data Centers ◽  
Scheduling Algorithm ◽  
Rapid Development ◽  
Solution Space ◽  
Large Solution ◽  
Scheduling Scheme ◽  
Saving Effect

With the rapid development of data centers, the energy consumption brought by more and more data centers cannot be underestimated. How to intelligently manage software-defined data center networks to reduce network energy consumption and improve network performance is becoming an important research subject. In this paper, for the flows with deadline requirements, we study how to design the rate-variable flow scheduling scheme to realize energy-saving and minimize the mean completion time (MCT) of flows based on meeting the deadline requirement. The flow scheduling optimization problem can be modeled as a Markov decision process (MDP). To cope with a large solution space, we design a DDPG-EEFS algorithm to find the optimal scheduling scheme for flows. The simulation result reveals that the DDPG-EEFS algorithm only trains part of the states and gets a good energy-saving effect and network performance. When the traffic intensity is small, the transmission time performance can be improved by sacrificing a little energy efficiency.

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