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neural network-based build time estimation for additive manufacturing: a performance comparison

JOURNAL OF COMPUTATIONAL dESIGN ANd ENGINEERING 2021年第5期8卷 1243-1256页

作者： Oh, Yosep Sharp, Michael Sprock, Timothy Kwon, Soonjo Kyonggi Univ Dept Ind & Management Engn Suwon 16227 Gyeonggi Do South Korea Natl Inst Stand & Technol Engn Lab Gaithersburg MD 20899 USA Kumoh Natl Inst Technol Dept Mech Syst Engn Gumi Si 39177 Gyeongsangbuk D South Korea Univ Maryland Appl Res Lab Intelligence & Secur ARLIS College Pk MD 20742 USA

Additive manufacturing (AM) has brought positive opportunities with phenomenal changes to traditional manufacturing. Consistent efforts and novel studies into AM use have resolved critical issues in manufacturing and broadened technical boundaries. Build time estimation is one of the critical issues in AM that still needs attention. Accurate build time estimation is key for feasibility studies, preliminary design, and process/production planning. Recent studies have provided the possibility of neural network (NN)-based build time estimation. In particular, traditional artificial NN (ANN)- and convolutional NN (CNN)-based methods have been demonstrated. However, very little has been done on the performance comparison for build time estimation among the different types of NNs. This study is aimed at filling this gap by designing various NNs for build time estimation and comparing them. Two types of features are prepared as inputs for the NNs by processing three-dimensional (3d) models: (1) representative features (RFs) including dimensions, part volume, and support volume;and (2) the set of voxels generated from designating the cells occupied by the workpiece in a mesh grid. With the combination of NN types and input feature types, we design three NNs: (1) ANN with RFs;(2) ANN with voxels;and (3) CNN with voxels. To obtain large enough label data for reliable training, we consider simulation build time from commercial slicing applications rather than actual build time. The simulation build time is calculated based on a material extrusion process. To address various cases for input models, two design factors (scale and rotation) are considered by controlling the size and build orientation of 3d models. In computational experiments, we reveal that the CNN-based estimation is often more accurate than others. Furthermore, the design factors affect the performance of build time estimation. In particular, the CNN-based estimation is strongly influenced by changing the size

关键词： additive manufacturing artificial neural network 3d convolutional neural network build time estimation 3d printing

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detection of Alzheimer's disease from temporal lobe grey matter slices using 3d CNN

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IMAGING SCIENCE JOURNAL 2022年第8期70卷 578-587页

作者： divya, R. Kumari, R. Shantha Selva Alzheimers dis Neuroimaging Initiat Mepco Schlenk Engn Coll Dept Elect & Commun Engn Sivakasi India Mepco Schlenk Engn Coll Dept Elect & Commun Engn Sivakasi 626005 Tamil Nadu India

Magnetic Resonance Imaging helps in detecting brain atrophy caused by Alzheimer's disease (Ad). Morphological changes start to occur in the temporal lobe of the brain in persons affected by Ad. A 3d convolutional neural network is proposed that focuses on the temporal lobe of the brain for Ad classification from normal controls. By investigating the temporal region of the grey matter in the brain instead of the entire brain region high performance is obtained with an accuracy of 88.06%, a sensitivity of 94.03%, and a specificity of 82.09% in classifying the MRI volumes of Alzheimer's disease Neuroimaging Initiative (AdNI-2) dataset. When Apolipoprotein epsilon 4 allele information and mini-mental state examination scores are used with the MRI volumes, an accuracy of 89.55%, a sensitivity of 82.09% and a specificity of 97.01% for AdNI-2 MRI volumes are achieved. When tested on AdNI-3 MRI volumes the proposed method resulted in an accuracy of 90.14%, a sensitivity of 79.17% and a specificity of 92.37%.

关键词： Magnetic resonance imaging Alzheimer's disease 3d convolutional neural network grey matter temporal lobe Apolipoprotein Mini-Mental State Examination Brain Atrophy

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Interpretable and Lightweight 3-d deep Learning Model for Automated ACL diagnosis

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IEEE JOURNAL OF BIOMEdICAL ANd HEALTH INFORMATICS 2021年第7期25卷 2388-2397页

作者： Jeon, Young Seok Yoshino, Kensuke Hagiwara, Shigeo Watanabe, Atsuya Quek, Swee Tian Yoshioka, Hiroshi Feng, Mengling Natl Univ Singapore Saw Swee Hock Sch Publ Hlth Singapore 119077 Singapore Natl Univ Singapore Inst Data Sci Singapore 119077 Singapore Natl Univ Singapore Dept Diagnost Imaging Singapore 119077 Singapore Chiba Univ Dept Orthopaed Surg Chiba 2638522 Japan Univ Calif Irvine Dept Radiol Sci Irvine CA 92697 USA

We propose an interpretable and lightweight 3d deep neural network model that diagnoses anterior cruciate ligament (ACL) tears from a knee MRI exam. Previous works focused primarily on achieving better diagnostic accuracy but paid less attention to practical aspects such as explainability and model size. They mainly relied on ImageNet pre-trained 2d deep neural network backbones, such as AlexNet or ResNet, which are computationally expensive. Some of them tried to interpret the models using post-inference visualization tools, such as CAM or Grad-CAM, which lack in generating accurate heatmaps. Our work addresses the two limitations by understanding the characteristics of ACL tear diagnosis. We argue that the semantic features required for classifying ACL tears are locally confined and highly homogeneous. We harness the unique characteristics of the task by incorporating: 1) attention modules and Gaussian positional encoding to reinforce the seeking of local features;2) squeeze modules and fewer convolutional filters to reflect the homogeneity of the features. As a result, our model is interpretable: our attention modules can precisely highlight the ACL region without any location information given to them. Our model is extremely lightweight: consisting of only 43 K trainable parameters and 7.1 G of Floating-point operations per second (FLOPs), that is 225 times smaller and 91 times lesser than the previous state-of-the-art, respectively. Our model is accurate: our model outperforms the previous state-of-the-art with the average ROC-AUC of 0.983 and 0.980 on the Chiba and Stanford knee datasets, respectively.

关键词： Computational modeling Artificial intelligence Task analysis Biological system modeling Solid modeling neural networks Bioinformatics ACL tear classification interpretation small deep neural network 3d convolutional neural network

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Multi-Modal Feature Fusion-Based Multi-Branch Classification network for Pulmonary Nodule Malignancy Suspiciousness diagnosis

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JOURNAL OF dIGITAL IMAGING 2023年第2期36卷 617-626页

作者： Yuan, Haiying Wu, Yanrui dai, Mengfan Beijing Univ Technol Beijing Peoples R China

detecting and identifying malignant nodules on chest computed tomography (CT) plays an important role in the early diagnosis and timely treatment of lung cancer, which can greatly reduce the number of deaths worldwide. In view of the existing methods in pulmonary nodule diagnosis, the importance of clinical radiological structured data (laboratory examination, radiological data) is ignored for the accuracy judgment of patients' condition. Hence, a multi-modal fusion multi-branch classification network is constructed to detect and classify pulmonary nodules in this work: (1) Radiological data of pulmonary nodules are used to construct structured features of length 9. (2) A multi-branch fusion-based effective attention mechanism network is designed for 3d CT Patch unstructured data, which uses 3d ECA-ResNet to dynamically adjust the extracted features. In addition, feature maps with different receptive fields from multi-layer are fully fused to obtain representative multi-scale unstructured features. (3) Multi-modal feature fusion of structured data and unstructured data is performed to distinguish benign and malignant nodules. Numerous experimental results show that this advanced network can effectively classify the benign and malignant pulmonary nodules for clinical diagnosis, which achieves the highest accuracy (94.89%), sensitivity (94.91%), and F1-score (94.65%) and lowest false positive rate (5.55%).

关键词： Pulmonary nodule classification Multi-modal feature fusion 3d ECA-ResNet Multi-branch classification 3d convolutional neural network

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OFF-ViNet: Optical Flow-Based Feature Warping ViNet for Video Saliency Prediction Considering Future Prediction

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IEEE ACCESS 2024年 12卷 66921-66930页

作者： Ikenoya, Reita Tashiro, Tomonori Ohashi, Gosuke Shizuoka Univ Dept Elect & Elect Engn Hamamatsu Shizuoka 4328561 Japan

Active studies have been conducted on video saliency prediction, which predicts human visual attention toward videos. Most deep learning-based video saliency prediction models implicitly learn features that contribute to video saliency prediction, greatly improving accuracy. This study proposes a model called optical flow-based feature warping ViNet (OFF-ViNet). This model explicitly adds a Warping module, which is a mechanism that considers future predictions based on object motion in addition to implicitly learned features. The Warping module spatially warps the hierarchical features extracted by the 3d convolutional backbone based on the optical flow to obtain a feature representation that predicts the future. Compared with existing models, OFF-ViNet achieves better and competitive accuracy with state-of-the-art models on video saliency prediction datasets, particularly on UCF-Sports, which contains several videos with moving objects.

关键词： Video saliency prediction optical flow 3d convolutional neural network

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deep learning-based interpretation of basal/acetazolamide brain perfusion SPECT leveraging unstructured reading reports

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EUROPEAN JOURNAL OF NUCLEAR MEdICINE ANd MOLECULAR IMAGING 2020年第9期47卷 2186-2196页

作者： Ryoo, Hyun Gee Choi, Hongyoon Lee, dong Soo Seoul Natl Univ Hosp Dept Nucl Med 28 Yongon Dong Seoul 110744 South Korea Seoul Natl Univ Dept Mol Med & Biopharmaceut Sci Grad Sch Convergence Sci & Technol Seoul South Korea Seoul Natl Univ Coll Med Seoul South Korea Seoul Natl Univ Coll Pharm Seoul South Korea

Purpose Basal/acetazolamide brain perfusion single-photon emission computed tomography (SPECT) has been used to evaluate functional hemodynamics in patients with carotid artery stenosis. We aimed to develop a deep learning model as a support system for interpreting brain perfusion SPECT leveraging unstructured text reports. Methods In total, 7345 basal/acetazolamide brain perfusion SPECT images and their text reports were retrospectively collected. A long short-term memory (LSTM) network was trained using 500 randomly selected text reports to predict manually labeled structured information, including abnormalities of basal perfusion and vascular reserve for each vascular territory. Using this trained LSTM model, we extracted structured information from the remaining 6845 text reports to develop a deep learning model for interpreting SPECT images. The model was based on a 3d convolutional neural network (CNN), and the performance was tested on the other 500 cases by measuring the area under the receiver-operating characteristic curve (AUC). We then applied the model to patients who underwent revascularization (n = 33) to compare the estimated output of the CNN model for pre- and post-revascularization SPECT and clinical outcomes. Results The AUC of the LSTM model for extracting structured labels was 1.00 for basal perfusion and 0.99 for vascular reserve for all 9 brain regions. The AUC of the CNN model designed to identify abnormal perfusion was 0.83 for basal perfusion and 0.89 for vascular reserve. The output of the CNN model was significantly improved according to the revascularization in the target vascular territory, and its changes in brain territories were concordant with clinical outcomes. Conclusion We developed a deep learning model to support the interpretation of brain perfusion SPECT by converting unstructured text reports into structured labels. This model can be used as a support system not only to identify perfusion abnormalities but also to provide q

关键词： deep learning Brain perfusion SPECT Vascular reserve 3d convolutional neural network Long short-term memory

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Gesture recognition system based on cross-domain CSI extracted from Wi-Fi devices combined with the 3d CNN

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SIGNAL IMAGE ANd VIdEO PROCESSING 2023年第6期17卷 3201-3209页

作者： Bulugu, Isack Univ Dar Es Salaam Dept Elect & Telecommun Dar Es Salaam Tanzania

Gesture recognition offers a wide range of applications in human-computer interaction. Wi-Fi devices have been deployed almost everywhere in recent years, thanks to the rapid expansion of wireless communication, the Internet of Things, and the emergence of data about Wi-Fi channel state information(CSI). Currently, most existing CSI gesture recognition studies solely focus on gesture recognition in a known domain. In the case of an unknown domain, new data from unknown scenes must be added for additional learning and training;otherwise, recognition accuracy will be significantly reduced, limiting practicality. To address this problem, a CSI cross-domain gesture recognition approach based on 3d convolutional neural networks is proposed. The method realizes cross-scene gesture recognition by extracting domain-independent features, and combining such features with the 3d convolutional neural network learning model. The experiment uses public datasets to verify the approach. The findings demonstrate that the technique achieves 89.67% recognition accuracy in known scenes and 86.34% recognition accuracy in unknown scenes, indicating that it can recognize cross-scene gestures recognition.

关键词： Gesture recognition Human-computer interaction Wi-Fi Channel state information Cross-domain 3d convolutional neural network

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STAM: A SpatioTemporal Attention Based Memory for Video Prediction

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IEEE TRANSACTIONS ON MULTIMEdIA 2023年 25卷 2354-2367页

作者： Chang, Zheng Zhang, Xinfeng Wang, Shanshe Ma, Siwei Gao, Wen Chinese Acad Sci Inst Comp Technol Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Comp Sci & Technol Beijing 100871 Peoples R China Peking Univ Natl Engn Lab Video Technol Beijing 100871 Peoples R China

Video prediction has always been a very challenging problem in video representation learning due to the complexity in spatial structure and temporal variation. However, existing methods mainly predict videos by employing language-based memory structures from the traditional Long Short-Term Memories (LSTMs) or Gated Recurrent Units (GRUs), which may not be powerful enough to model the long-term dependencies in videos, consisting of much more complex spatiotemporal dynamics than sentences. In this paper, we propose a SpatioTemporal Attention based Memory (STAM), which can efficiently improve the long-term spatiotemporal memorizing capacity by incorporating the global spatiotemporal information in videos. In the temporal domain, the proposed STAM aims to observe temporal states from a wider temporal receptive field to capture accurate global motion information. In the spatial domain, the proposed STAM aims to jointly utilize both the high-level semantic spatial state and the low-level texture spatial states to model a more reliable global spatial representation for videos. In particular, the global spatiotemporal information is extracted with the help of an Efficient SpatioTemporal Attention Gate (ESTAG), which can adaptively apply different levels of attention scores to different spatiotemporal states according to their importance. Moreover, the proposed STAM are built with 3d convolutional layers due to their advantages in modeling spatiotemporal dynamics for videos. Experimental results show that the proposed STAM can achieve state-of-the-art performance on widely used datasets by leveraging the proposed spatiotemporal representations for videos.

关键词： Global spatiotemporal information spatio temporal receptive field 3d convolutional neural network spatiotemporal attention sequence learning video prediction

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Strain measurement during tensile testing using deep learning-based digital image correlation

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MEASUREMENT SCIENCE ANd TECHNOLOGY 2020年第1期31卷 015014-015014页

作者： Min, Hyeon-Gyu On, Han-Ik Kang, dong-Joong Park, Jun-Hyub Pusan Natl Univ Sch Mech Engn 2Busandachak Ro 63Beon Gil Busan 46241 South Korea Kyungsung Univ Dept Mech & Automot Engn 309 Suyeong Ro Busan 48434 South Korea

This paper describes a novel non-contact strain measurement method defined as deep learning based on digital image correlation (ddIC). In particular, it is very difficult to measure directly displacement of gauge length during tensile testing of thin films. Therefore, we obtained the image data continuously to observe the behavior of the material during tensile testing. The sequential image data obtained at a specific position is assigned to a multi-channel input to train the deep neural network. As a result, the multi-channel image is composed of sequential images obtained along the time domain. Since these images have a correlation with each other along the time domain in each pixel, the neural network learns displacement, including temporal information. The ddIC method originates from a 3d convolutional neural network, which can extract both the spatial and the temporal domain features at the same time. A 3d convolutional filter is used as the feature extraction part of the network in order to effectively learn the input data. The deep learning is an end-to-end learning method and the deformation between two images can be measured regardless of the parameters for the nonlinear deformation of the images. An elastic modulus of 118 GPa, 0.2% yield strength of 941 MPa, ultimate tensile strength of 1108 MPa and fracture strain of 0.02414 are estimated by applying the ddIC method during a tensile test of BeCu thin film. The results of the ddIC method are compared with the displacement sensor data and digital image correlation data.

关键词： tensile test strain measurement non-contact measurement BeCu thin film 3d convolutional neural network machine vision

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Spatiotemporal Representation Learning for Video Anomaly detection

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IEEE ACCESS 2020年 8卷 25531-25542页

作者： Li, Zhaoyan Li, Yaoshun Gao, Zhisheng Xihua Univ Sch Comp & Software Engn Chengdu 610039 Peoples R China

Video-based anomalous human behavior detection is widely studied in many fields such as security, medical care, education, and energy. However, there are still some open problems in anomalous behavior detection, such as the large and complicated model is difficult to train, the accuracy of anomalous behavior detection is not high enough and the speed is not fast enough. A spatiotemporal representation learning model is proposed in this paper. Firstly, the spatial-temporal features of the video are extracted by the constructed multi-scale 3d convolutional neural network. Then the scene background is modeled by the high-dimensional mixed Gaussian model and used for anomaly detection. Finally, the accurate position of anomalous behavior in the video data is achieved by calculating the position of the last output feature, that is, the position of the receptive field. The proposed model does not require specific training. Moreover, the proposed method has the advantages of high versatility, fast calculation speed and high detection accuracy. We validated the proposed algorithm on two representative surveillance scene datasets, the Subway and the UCSdSped2. Results show that proposed algorithm has achieved the detection rate of 18 FPS under the condition of common computing resources, and meet the real-time requirements. Moreover, compared the similar methods, the proposed method has achieved the competitive results in both frame-level accuracy and pixel-level accuracy.

关键词： Spatiotemporal representation learning anomaly detection 3d convolutional neural network mixed Gaussian model

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