Cyclic Graph Dynamic Multilayer Perceptron for Periodic Signals
We propose a feature extraction for periodic signals. Virtually every mechanized transportation vehicle, power generation, industrial machine, and robotic system contains rotating shafts. It is possible to collect data about periodicity by mea- suring a shaft’s rotation. However, it is difficult to perfectly control the collection timing of the measurements. Imprecise timing creates phase shifts in the resulting data. Although a phase shift does not materially affect the measurement of any given data point collected, it does alter the order in which all of the points are col- lected. It is difficult for classical methods, like multi-layer perceptron, to identify or quantify these alterations because they depend on the order of the input vectors’ components. This paper proposes a robust method for extracting features from phase shift data by adding a graph structure to each data point and constructing a suitable machine learning architecture for graph data with cyclic permutation. Simulation and experimental results illustrate its effectiveness.
In the paper, the authors proposed a novel method adding graph architecture for collected data points to utilize not only features but also relative information. This helps reduce time and costs to collect a huge amount of data from industrial machines and improve accuracy. Although the paper idea is very interesting when presenting a new learning model for graph data, explanations and experiments are not convincing. For explanations, the authors did not provide sufficient related works or references to prove that the problem the paper wants to solve is important. Also, for some approaches using deep learning mentioned there are no references. For experiments, the results presented in Table 1 are good but there are no official baselines (e.g. from some prior works) to make the comparison more reliable. Base on the arguments mentioned above, the paper is not convincing and reliable. Small suggestion revision: More analysis of prior works to show that the problem is important and need-to-solve The introduction section should more references. The experiments should be rigorous cause it lacks reliable baselines for comparison.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Cyclic Graph Dynamic Multilayer Perceptron for Periodic Signals
We propose a feature extraction for periodic signals. Virtually every mechanized transportation vehicle, power generation, industrial machine, and robotic system contains rotating shafts. It is possible to collect data about periodicity by mea- suring a shaft’s rotation. However, it is difficult to perfectly control the collection timing of the measurements. Imprecise timing creates phase shifts in the resulting data. Although a phase shift does not materially affect the measurement of any given data point collected, it does alter the order in which all of the points are col- lected. It is difficult for classical methods, like multi-layer perceptron, to identify or quantify these alterations because they depend on the order of the input vectors’ components. This paper proposes a robust method for extracting features from phase shift data by adding a graph structure to each data point and constructing a suitable machine learning architecture for graph data with cyclic permutation. Simulation and experimental results illustrate its effectiveness.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



In the paper, the authors proposed a novel method adding graph architecture for collected data points to utilize not only features but also relative information. This helps reduce time and costs to collect a huge amount of data from industrial machines and improve accuracy. Although the paper idea is very interesting when presenting a new learning model for graph data, explanations and experiments are not convincing. For explanations, the authors did not provide sufficient related works or references to prove that the problem the paper wants to solve is important. Also, for some approaches using deep learning mentioned there are no references. For experiments, the results presented in Table 1 are good but there are no official baselines (e.g. from some prior works) to make the comparison more reliable. Base on the arguments mentioned above, the paper is not convincing and reliable. Small suggestion revision: More analysis of prior works to show that the problem is important and need-to-solve The introduction section should more references. The experiments should be rigorous cause it lacks reliable baselines for comparison.
assistant
In the paper, the authors proposed a novel method adding graph architecture for collected data points to utilize not only features but also relative information. This helps reduce time and costs to collect a huge amount of data from industrial machines and improve accuracy. Although the paper idea is very interesting when presenting a new learning model for graph data, explanations and experiments are not convincing. For explanations, the authors did not provide sufficient related works or references to prove that the problem the paper wants to solve is important. Also, for some approaches using deep learning mentioned there are no references. For experiments, the results presented in Table 1 are good but there are no official baselines (e.g. from some prior works) to make the comparison more reliable. Base on the arguments mentioned above, the paper is not convincing and reliable. Small suggestion revision: More analysis of prior works to show that the problem is important and need-to-solve The introduction section should more references. The experiments should be rigorous cause it lacks reliable baselines for comparison.