A Synergic Approach of Deep Learning towards Digital Additive Manufacturing: A Review

Pratap, Ayush; Sardana, Neha; Utomo, Sapdo; John, A; Karthikeyan, P.; Hsiung, Pao-Ann

doi:10.3390/a15120466

Cited by 5 publications

(3 citation statements)

References 172 publications

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“…For machine learning or neural network methods [25][26][27], through literature research, we found that there are relatively few algorithms used for GIS switch opening and closing state recognition based on vibration signals. Consequently, this section mainly reviews algorithms used for vibration signal analysis in various fields.…”

Section: Related Workmentioning

confidence: 99%

Quick Identification of Open/Closed State of GIS Switch Based on Vibration Detection and Deep Learning

Zhang

et al. 2023

Electronics

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The rapid and accurate identification of the opening and closing state of the knife switch in a gas insulated switchgear (GIS) is very important for the timely detection of equipment faults and for the reduction of related accidents. However, existing technologies, such as image recognition, are vulnerable to weather or light intensity, while microswitch, attitude sensing and other methods are unable to induce equipment power failure with sufficient speed, which brings many new challenges to the operation and maintenance of a GIS. Therefore, this research designs a GIS shell vibration detection system for knife switch state discrimination, introduces a deep learning algorithm for knife switch vibration signal analysis, and proposes a fast convolutional neural network (FCNN) to identify the knife switch state. For the designed FCNN, a normalization layer and a nonlinear activation layer are used after each convolution layer to obviously reduce feature quantity and increase algorithm efficiency. In order to test the recognition performance based on the vibration detection system, this study carried out two kinds of knife switch opening and closing experiments. One group with artificial noise was added, the other group did not include artifical noise, and a corresponding data set was constructed. The experimental results show that the recognition accuracy for both datasets reaches 100%, and the FCNN algorithm is better than the five classical algorithms in terms of prediction efficiency. This study shows that the vibration detection technology based on deep learning can be used to effectively identify the opening and closing state of a GIS knife switch, and is expected to be promoted and applied.

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Section: Related Workmentioning

confidence: 99%

Quick Identification of Open/Closed State of GIS Switch Based on Vibration Detection and Deep Learning

Zhang

et al. 2023

Electronics

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“…Using XAI is meant to make it easier to comprehend and diagnose model output, regardless of how accurate the output may be. In conclusion, it will help the user comprehend the results of the system and provide the model's developer insightful input for bettering the model [11,12]. In one study, the diabetes classification framework based on the XAI method was interpreted and designed by taking into account the results obtained from the Shapley method in the explanations of the model [13].…”

Section: Introductionmentioning

confidence: 99%

Explainable Artificial Intelligence Paves the Way in Precision Diagnostics and Biomarker Discovery for the Subclass of Diabetic Retinopathy in Type 2 Diabetics

Yagin,

Yasar,

Gormez

et al. 2023

Metabolites

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Diabetic retinopathy (DR), a common ocular microvascular complication of diabetes, contributes significantly to diabetes-related vision loss. This study addresses the imperative need for early diagnosis of DR and precise treatment strategies based on the explainable artificial intelligence (XAI) framework. The study integrated clinical, biochemical, and metabolomic biomarkers associated with the following classes: non-DR (NDR), non-proliferative diabetic retinopathy (NPDR), and proliferative diabetic retinopathy (PDR) in type 2 diabetes (T2D) patients. To create machine learning (ML) models, 10% of the data was divided into validation sets and 90% into discovery sets. The validation dataset was used for hyperparameter optimization and feature selection stages, while the discovery dataset was used to measure the performance of the models. A 10-fold cross-validation technique was used to evaluate the performance of ML models. Biomarker discovery was performed using minimum redundancy maximum relevance (mRMR), Boruta, and explainable boosting machine (EBM). The predictive proposed framework compares the results of eXtreme Gradient Boosting (XGBoost), natural gradient boosting for probabilistic prediction (NGBoost), and EBM models in determining the DR subclass. The hyperparameters of the models were optimized using Bayesian optimization. Combining EBM feature selection with XGBoost, the optimal model achieved (91.25 ± 1.88) % accuracy, (89.33 ± 1.80) % precision, (91.24 ± 1.67) % recall, (89.37 ± 1.52) % F1-Score, and (97.00 ± 0.25) % the area under the ROC curve (AUROC). According to the EBM explanation, the six most important biomarkers in determining the course of DR were tryptophan (Trp), phosphatidylcholine diacyl C42:2 (PC.aa.C42.2), butyrylcarnitine (C4), tyrosine (Tyr), hexadecanoyl carnitine (C16) and total dimethylarginine (DMA). The identified biomarkers may provide a better understanding of the progression of DR, paving the way for more precise and cost-effective diagnostic and treatment strategies.

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“…In traditional tapping testing, inspectors cannot recognize the nuances of sounds due to the limited frequency range of hearing. Recently, improvements in computer performance and the application of artificial intelligence technology in many fields has provided the possibility to overcome this limitation [ 20 , 21 , 22 ]. Kong et al [ 23 ] proposed a novel percussion-based method to detect bolt looseness.…”

Section: Introductionmentioning

confidence: 99%

Percussion and PSO-SVM-Based Damage Detection for Refractory Materials

Yang

Peng

Zhou³

et al. 2023

Micromachines

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Refractory materials are basic materials widely used in industrial furnaces and thermal equipment. Their microstructure is similar to that of many heterogeneous high-performance materials used in micro/nanodevices. The presence of damage can reduce the mechanical properties and service life of refractory materials and even cause serious safety accidents. In this paper, a novel percussion and particle swarm optimization-support vector machine (PSO-SVM)-based method is proposed to detect damage in refractory materials. An impact is applied to the material and the generated sound is recorded. The percussion-induced sound signals are fed into a mel filter bank to generate time–frequency representations in the form of mel spectrograms. Then, two image descriptors—the local binary pattern (LBP) and histogram of oriented gradient (HOG)—are used to extract the texture information of the mel spectrogram. Finally, combining both HOG and LBP features, the fused features are input to the PSO-SVM algorithm to realize damage detection in refractory materials. The results demonstrated that the proposed method could identify five different degrees of damage of refractory materials, with an accuracy rate greater than 97%. Therefore, the percussion and PSO-SVM-based method proposed in this paper has high potential for field applications in damage detection in refractory material, and also has the potential to be extended to research on damage detection methods for other materials used in micro/nanodevices.

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A Synergic Approach of Deep Learning towards Digital Additive Manufacturing: A Review

Cited by 5 publications

References 172 publications

Quick Identification of Open/Closed State of GIS Switch Based on Vibration Detection and Deep Learning

Quick Identification of Open/Closed State of GIS Switch Based on Vibration Detection and Deep Learning

Explainable Artificial Intelligence Paves the Way in Precision Diagnostics and Biomarker Discovery for the Subclass of Diabetic Retinopathy in Type 2 Diabetics

Percussion and PSO-SVM-Based Damage Detection for Refractory Materials

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