22 research outputs found
Distributed fuzzy decision making for production schedulling
In production systems, input materials (educts) pass through multiple
sequential stages until they become a product. The production stages
consist of different machines with various dynamic characteristics. The
coupling of those machines is a non-linear distributed system. With a
distributed control system based on a multi-agent approach, the produc-
tion system can achieve (almost) maximum output, where lot size and lot
sequence are the most important control variables. In most production
processes high throughput and low stock are conflicting goals. In order to
compare and compensate between these multiple goals, a fuzzy decision
making approach is employed here that decides about the material flow
and machine states, based on variables like working load or order queue
length
TinyReptile: TinyML with Federated Meta-Learning
Tiny machine learning (TinyML) is a rapidly growing field aiming to
democratize machine learning (ML) for resource-constrained microcontrollers
(MCUs). Given the pervasiveness of these tiny devices, it is inherent to ask
whether TinyML applications can benefit from aggregating their knowledge.
Federated learning (FL) enables decentralized agents to jointly learn a global
model without sharing sensitive local data. However, a common global model may
not work for all devices due to the complexity of the actual deployment
environment and the heterogeneity of the data available on each device. In
addition, the deployment of TinyML hardware has significant computational and
communication constraints, which traditional ML fails to address. Considering
these challenges, we propose TinyReptile, a simple but efficient algorithm
inspired by meta-learning and online learning, to collaboratively learn a solid
initialization for a neural network (NN) across tiny devices that can be
quickly adapted to a new device with respect to its data. We demonstrate
TinyReptile on Raspberry Pi 4 and Cortex-M4 MCU with only 256-KB RAM. The
evaluations on various TinyML use cases confirm a resource reduction and
training time saving by at least two factors compared with baseline algorithms
with comparable performance.Comment: Accepted by The International Joint Conference on Neural Network
(IJCNN) 202
Batch Reinforcement Learning on the Industrial Benchmark: First Experiences
The Particle Swarm Optimization Policy (PSO-P) has been recently introduced
and proven to produce remarkable results on interacting with academic
reinforcement learning benchmarks in an off-policy, batch-based setting. To
further investigate the properties and feasibility on real-world applications,
this paper investigates PSO-P on the so-called Industrial Benchmark (IB), a
novel reinforcement learning (RL) benchmark that aims at being realistic by
including a variety of aspects found in industrial applications, like
continuous state and action spaces, a high dimensional, partially observable
state space, delayed effects, and complex stochasticity. The experimental
results of PSO-P on IB are compared to results of closed-form control policies
derived from the model-based Recurrent Control Neural Network (RCNN) and the
model-free Neural Fitted Q-Iteration (NFQ). Experiments show that PSO-P is not
only of interest for academic benchmarks, but also for real-world industrial
applications, since it also yielded the best performing policy in our IB
setting. Compared to other well established RL techniques, PSO-P produced
outstanding results in performance and robustness, requiring only a relatively
low amount of effort in finding adequate parameters or making complex design
decisions
A Benchmark Environment Motivated by Industrial Control Problems
In the research area of reinforcement learning (RL), frequently novel and
promising methods are developed and introduced to the RL community. However,
although many researchers are keen to apply their methods on real-world
problems, implementing such methods in real industry environments often is a
frustrating and tedious process. Generally, academic research groups have only
limited access to real industrial data and applications. For this reason, new
methods are usually developed, evaluated and compared by using artificial
software benchmarks. On one hand, these benchmarks are designed to provide
interpretable RL training scenarios and detailed insight into the learning
process of the method on hand. On the other hand, they usually do not share
much similarity with industrial real-world applications. For this reason we
used our industry experience to design a benchmark which bridges the gap
between freely available, documented, and motivated artificial benchmarks and
properties of real industrial problems. The resulting industrial benchmark (IB)
has been made publicly available to the RL community by publishing its Java and
Python code, including an OpenAI Gym wrapper, on Github. In this paper we
motivate and describe in detail the IB's dynamics and identify prototypic
experimental settings that capture common situations in real-world industry
control problems
A Benchmark Environment Motivated by Industrial Control Problems
In the research area of reinforcement learning (RL), frequently novel and
promising methods are developed and introduced to the RL community. However,
although many researchers are keen to apply their methods on real-world
problems, implementing such methods in real industry environments often is a
frustrating and tedious process. Generally, academic research groups have only
limited access to real industrial data and applications. For this reason, new
methods are usually developed, evaluated and compared by using artificial
software benchmarks. On one hand, these benchmarks are designed to provide
interpretable RL training scenarios and detailed insight into the learning
process of the method on hand. On the other hand, they usually do not share
much similarity with industrial real-world applications. For this reason we
used our industry experience to design a benchmark which bridges the gap
between freely available, documented, and motivated artificial benchmarks and
properties of real industrial problems. The resulting industrial benchmark (IB)
has been made publicly available to the RL community by publishing its Java and
Python code, including an OpenAI Gym wrapper, on Github. In this paper we
motivate and describe in detail the IB's dynamics and identify prototypic
experimental settings that capture common situations in real-world industry
control problems
Type reduction operators for interval type–2 defuzzification
Fuzzy sets are an important approach to model uncertainty. Defuzzification maps fuzzy sets to non–fuzzy (crisp) values. Type–2 fuzzy sets model uncertainty in the degree of membership in a fuzzy set. Type–2 defuzzification maps type–2 fuzzy sets to non–fuzzy values. Type reduction maps type–2 fuzzy sets to type–1 fuzzy sets, in order to make type–2 defuzzification easier and to implement more efficient type–2 defuzzification algorithms. This paper is a first step towards a theoretical foundation of the emerging field of type reduction. Five mathematical properties of type reduction are defined, and two existing type reduction methods (Nie–Tan and uncertainty weight) are examined with respect to our five properties. Furthermore, two new type reduction methods are proposed: consistent linear type reduction and consistent quadratic type reduction. All our five properties are satisfied by consistent quadratic type reduction
SeLoC-ML: Semantic Low-Code Engineering for Machine Learning Applications in Industrial IoT
Internet of Things (IoT) is transforming the industry by bridging the gap
between Information Technology (IT) and Operational Technology (OT). Machines
are being integrated with connected sensors and managed by intelligent
analytics applications, accelerating digital transformation and business
operations. Bringing Machine Learning (ML) to industrial devices is an
advancement aiming to promote the convergence of IT and OT. However, developing
an ML application in industrial IoT (IIoT) presents various challenges,
including hardware heterogeneity, non-standardized representations of ML
models, device and ML model compatibility issues, and slow application
development. Successful deployment in this area requires a deep understanding
of hardware, algorithms, software tools, and applications. Therefore, this
paper presents a framework called Semantic Low-Code Engineering for ML
Applications (SeLoC-ML), built on a low-code platform to support the rapid
development of ML applications in IIoT by leveraging Semantic Web technologies.
SeLoC-ML enables non-experts to easily model, discover, reuse, and matchmake ML
models and devices at scale. The project code can be automatically generated
for deployment on hardware based on the matching results. Developers can
benefit from semantic application templates, called recipes, to fast prototype
end-user applications. The evaluations confirm an engineering effort reduction
by a factor of at least three compared to traditional approaches on an
industrial ML classification case study, showing the efficiency and usefulness
of SeLoC-ML. We share the code and welcome any contributions.Comment: Accepted by the 21st International Semantic Web Conference (ISWC2022