308 research outputs found
Structure and motion design of a mock circulatory test rig
Mock circulatory test rig (MCTR) is the essential and indispensable facility in the cardiovascular in vitro studies. The system configuration and the motion profile of the MCTR design directly influence the validity, precision, and accuracy of the experimental data collected. Previous studies gave the schematic but never describe the structure and motion design details of the MCTRs used, which makes comparison of the experimental data reported by different research groups plausible but not fully convincing. This article presents the detailed structure and motion design of a sophisticated MCTR system, and examines the important issues such as the determination of the ventricular motion waveform, modelling of the physiological impedance, etc., in the MCTR designing. The study demonstrates the overall design procedures from the system conception, cardiac model devising, motion planning, to the motor and accessories selection. This can be used as a reference to aid researchers in the design and construction of their own in-house MCTRs for cardiovascular studies
Hybrid improved brain storm optimization with support vector machine for cardiovascular diseases classification
Most existing classification algorithms for cardiovascular disease are limited to specific diseases and cannot categorize the severity of the diseases. These algorithms still need to be improved in terms of accuracy and generalizability. Therefore, a hybrid Improved Brain Storm Optimization with Support Vector Machine (IBSO-SVM) for cardiovascular disease classification is proposed. In this study, a knowledge-driven intelligent initialization method is proposed to enhance the optimization capability of IBSO and the accuracy of IBSOSVM. Experimental evaluations are conducted on multiple real-world datasets, and the results demonstrate the superior performance of IBSO-SVM in cardiac disease datasets. The accuracy of BSO-SVM reached 100% on the Heart Failure and Heart Disease datasets, and the accuracy of IBSO-SVM reached 99% on the Stroke dataset and 88% on the Cardiovascular disease dataset
Optimization Models and Algorithms for Prototype Vehicle Test Scheduling
Automotive makers conduct a series of tests at pre-production phases of each new vehicle model development program. The main goal of those tests is to ensure that the vehicle models meet all design requirements by the time they reach the production phase. These tests target different vehicle components or functions, such as powertrain systems, electrical systems, safety aspects, etc. However, one big issue is that the cost of the resources, mainly prototype vehicles, invested in the testing process is exceedingly expensive. An individual prototype vehicle can cost over 5 times its counterpart’s price in the commercial market because many of the parts and the prototype vehicles themselves are highly customized and produced in small batches. Parts needed often require months of lead time, which constrains when vehicle builds can start. That, combined with inflexible time-window constraints for completing tests on those prototypes introduces significant time pressure, an unavoidable and challenging reality. What makes the problem even more difficult is that in addition to the prototype vehicle resources, there are other constrained supporting resources involved during the execution of those tests, such as testing facilities, instruments and equipment like cameras and sensors, human-power availability, etc.
An efficient way to conquer the problem is to develop test plans with tight schedules that combine multiple tests on vehicles to fully utilize all available time while balancing the loads of other supporting resources. There are many challenges that need to be overcome in implementing this approach, including complex compatibility relationships between the tests and destructive nature of, e.g., crash tests.
In this thesis, we show how to mathematically model these test scheduling problems as optimization problems. We develop corresponding solution approaches that enable quick generation of an efficient schedule to execute all tests while respecting all constraints. Our models and algorithms save test planners’ and engineers’ time, increase q their ability to quickly react to program changes, and save resources by ensuring maximal vehicle utilization.PHDIndustrial & Operations EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/137071/1/yuhuishi_1.pd
Artificial bee colony algorithm with time-varying strategy
Artificial bee colony (ABC) is one of the newest additions to the class of swarm intelligence. ABC algorithm has been shown to be competitive with some other population-based algorithms. However, there is still an insufficiency that ABC is good at exploration but poor at exploitation. To make a proper balance between these two conflictive factors, this paper proposed a novel ABC variant with a time-varying strategy where the ratio between the number of employed bees and the number of onlooker bees varies with time. The linear and nonlinear time-varying strategies can be incorporated into the basic ABC algorithm, yielding ABC-LTVS and ABC-NTVS algorithms, respectively. The effects of the added parameters in the two new ABC algorithms are also studied through solving some representative benchmark functions. The proposed ABC algorithm is a simple and easy modification to the structure of the basic ABC algorithm. Moreover, the proposed approach is general and can be incorporated in other ABC variants. A set of 21 benchmark functions in 30 and 50 dimensions are utilized in the experimental studies. The experimental results show the effectiveness of the proposed time-varying strategy
Automated Design of Metaheuristic Algorithms: A Survey
Metaheuristics have gained great success in academia and practice because
their search logic can be applied to any problem with available solution
representation, solution quality evaluation, and certain notions of locality.
Manually designing metaheuristic algorithms for solving a target problem is
criticized for being laborious, error-prone, and requiring intensive
specialized knowledge. This gives rise to increasing interest in automated
design of metaheuristic algorithms. With computing power to fully explore
potential design choices, the automated design could reach and even surpass
human-level design and could make high-performance algorithms accessible to a
much wider range of researchers and practitioners. This paper presents a broad
picture of automated design of metaheuristic algorithms, by conducting a survey
on the common grounds and representative techniques in terms of design space,
design strategies, performance evaluation strategies, and target problems in
this field
Toward multi-target self-organizing pursuit in a partially observable Markov game
The multiple-target self-organizing pursuit (SOP) problem has wide
applications and has been considered a challenging self-organization game for
distributed systems, in which intelligent agents cooperatively pursue multiple
dynamic targets with partial observations. This work proposes a framework for
decentralized multi-agent systems to improve intelligent agents' search and
pursuit capabilities. We model a self-organizing system as a partially
observable Markov game (POMG) with the features of decentralization, partial
observation, and noncommunication. The proposed distributed algorithm: fuzzy
self-organizing cooperative coevolution (FSC2) is then leveraged to resolve the
three challenges in multi-target SOP: distributed self-organizing search (SOS),
distributed task allocation, and distributed single-target pursuit. FSC2
includes a coordinated multi-agent deep reinforcement learning method that
enables homogeneous agents to learn natural SOS patterns. Additionally, we
propose a fuzzy-based distributed task allocation method, which locally
decomposes multi-target SOP into several single-target pursuit problems. The
cooperative coevolution principle is employed to coordinate distributed
pursuers for each single-target pursuit problem. Therefore, the uncertainties
of inherent partial observation and distributed decision-making in the POMG can
be alleviated. The experimental results demonstrate that distributed
noncommunicating multi-agent coordination with partial observations in all
three subtasks are effective, and 2048 FSC2 agents can perform efficient
multi-target SOP with almost 100% capture rates
Continual Task Allocation in Meta-Policy Network via Sparse Prompting
How to train a generalizable meta-policy by continually learning a sequence
of tasks? It is a natural human skill yet challenging to achieve by current
reinforcement learning: the agent is expected to quickly adapt to new tasks
(plasticity) meanwhile retaining the common knowledge from previous tasks
(stability). We address it by "Continual Task Allocation via Sparse Prompting
(CoTASP)", which learns over-complete dictionaries to produce sparse masks as
prompts extracting a sub-network for each task from a meta-policy network. By
optimizing the sub-network and prompts alternatively, CoTASP updates the
meta-policy via training a task-specific policy. The dictionary is then updated
to align the optimized prompts with tasks' embedding, thereby capturing their
semantic correlations. Hence, relevant tasks share more neurons in the
meta-policy network via similar prompts while cross-task interference causing
forgetting is effectively restrained. Given a trained meta-policy with updated
dictionaries, new task adaptation reduces to highly efficient sparse prompting
and sub-network finetuning. In experiments, CoTASP achieves a promising
plasticity-stability trade-off without storing or replaying any past tasks'
experiences and outperforms existing continual and multi-task RL methods on all
seen tasks, forgetting reduction, and generalization to unseen tasks.Comment: Accepted by ICML 202
- …