scholarly journals One-dimensional organic artificial multi-synapses enabling electronic textile neural network for wearable neuromorphic applications

2020 ◽  
Vol 6 (28) ◽  
pp. eaba1178 ◽  
Author(s):  
Seonggil Ham ◽  
Minji Kang ◽  
Seonghoon Jang ◽  
Jingon Jang ◽  
Sanghyeon Choi ◽  
...  
Keyword(s):  
Neural Network ◽  
Recognition Accuracy ◽  
Single Layer ◽  
Organic Transistors ◽  
Bending Stress ◽  
One Dimensional ◽  
Synaptic Functions ◽  
Wearable Electronic ◽  
Mechanical Bending ◽  
Electronic Textile

One-dimensional (1D) devices are becoming the most desirable format for wearable electronic technology because they can be easily woven into electronic (e-) textile(s) with versatile functional units while maintaining their inherent features under mechanical stress. In this study, we designed 1D fiber-shaped multi-synapses comprising ferroelectric organic transistors fabricated on a 100-μm Ag wire and used them as multisynaptic channels in an e-textile neural network for wearable neuromorphic applications. The device mimics diverse synaptic functions with excellent reliability even under 6000 repeated input stimuli and mechanical bending stress. Various NOR-type textile arrays are formed simply by cross-pointing 1D synapses with Ag wires, where each output from individual synapse can be integrated and propagated without undesired leakage. Notably, the 1D multi-synapses achieved up to ~90 and ~70% recognition accuracy for MNIST and electrocardiogram patterns, respectively, even in a single-layer neural network, and almost maintained regardless of the bending conditions.

Development of corrosion flaws for the production of realistic test specimens

2020 ◽  
Vol 64 (1) ◽  
pp. 23-28
Author(s):  
J. Hodač ◽  
Z. Fulín ◽  
P. Mareš ◽  
J. Veselá ◽  
O. Chocholatý
Keyword(s):  
Power Plants ◽  
Galvanic Corrosion ◽  
Water Solution ◽  
Erosive Wear ◽  
Corrosion Damage ◽  
Water Mist ◽  
Carbon Steels ◽  
Bending Stress ◽  
Solution Flow ◽  
Mechanical Bending

AbstractTo produce realistic test specimens with realistic flaws, it is necessary to develop appropriate procedure for corrosion flaw production. Tested specimens are made from steels commonly used in power plants, such as carbon steels, stainless steels and their dissimilar weldments. In this study, corrosion damage from NaCl water solution and NaCl water mist are compared. Specimens were tested with and without mechanical bending stress. The corrosion processes produced plane, pitting and galvanic corrosion. On dissimilar weldments galvanic corrosion was observed and resulted to the deepest corrosion damage. Deepest corrosion flaws were formed on welded samples. The corrosion rate was also affected by the solution flow in a contact with the specimens, which results in a corrosion-erosive wear. Produced flaws are suitable as natural crack initiators or as realistic corrosion flaws in test specimens.

scholarly journals Graphene-based 3D XNOR-VRRAM with ternary precision for neuromorphic computing

2021 ◽  
Vol 5 (1) ◽  
Author(s):  
Batyrbek Alimkhanuly ◽  
Joon Sohn ◽  
Ik-Joon Chang ◽  
Seunghyun Lee
Keyword(s):  
Neural Network ◽  
Energy Consumption ◽  
Recognition Accuracy ◽  
Material Selection ◽  
Weighted Sum ◽  
Device Design ◽  
Key Factors ◽  
Neuromorphic Computing ◽  
Device Scaling ◽  
The Impact

AbstractRecent studies on neural network quantization have demonstrated a beneficial compromise between accuracy, computation rate, and architecture size. Implementing a 3D Vertical RRAM (VRRAM) array accompanied by device scaling may further improve such networks’ density and energy consumption. Individual device design, optimized interconnects, and careful material selection are key factors determining the overall computation performance. In this work, the impact of replacing conventional devices with microfabricated, graphene-based VRRAM is investigated for circuit and algorithmic levels. By exploiting a sub-nm thin 2D material, the VRRAM array demonstrates an improved read/write margins and read inaccuracy level for the weighted-sum procedure. Moreover, energy consumption is significantly reduced in array programming operations. Finally, an XNOR logic-inspired architecture designed to integrate 1-bit ternary precision synaptic weights into graphene-based VRRAM is introduced. Simulations on VRRAM with metal and graphene word-planes demonstrate 83.5 and 94.1% recognition accuracy, respectively, denoting the importance of material innovation in neuromorphic computing.

scholarly journals Application of Deep Learning in Fault Diagnosis of Rotating Machinery

Processes ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 919
Author(s):  
Wanlu Jiang ◽  
Chenyang Wang ◽  
Jiayun Zou ◽  
Shuqing Zhang
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Fault Diagnosis ◽  
Rotating Machinery ◽  
Generative Adversarial Networks ◽  
Extraction Ability ◽  
One Dimensional ◽  
Adversarial Networks ◽  
Diagnosis Model ◽  
The One

The field of mechanical fault diagnosis has entered the era of “big data”. However, existing diagnostic algorithms, relying on artificial feature extraction and expert knowledge are of poor extraction ability and lack self-adaptability in the mass data. In the fault diagnosis of rotating machinery, due to the accidental occurrence of equipment faults, the proportion of fault samples is small, the samples are imbalanced, and available data are scarce, which leads to the low accuracy rate of the intelligent diagnosis model trained to identify the equipment state. To solve the above problems, an end-to-end diagnosis model is first proposed, which is an intelligent fault diagnosis method based on one-dimensional convolutional neural network (1D-CNN). That is to say, the original vibration signal is directly input into the model for identification. After that, through combining the convolutional neural network with the generative adversarial networks, a data expansion method based on the one-dimensional deep convolutional generative adversarial networks (1D-DCGAN) is constructed to generate small sample size fault samples and construct the balanced data set. Meanwhile, in order to solve the problem that the network is difficult to optimize, gradient penalty and Wasserstein distance are introduced. Through the test of bearing database and hydraulic pump, it shows that the one-dimensional convolution operation has strong feature extraction ability for vibration signals. The proposed method is very accurate for fault diagnosis of the two kinds of equipment, and high-quality expansion of the original data can be achieved.

scholarly journals Design framework for a seamless smart glove using a digital knitting system

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Yewon Song ◽  
Seulah Lee ◽  
Yuna Choi ◽  
Sora Han ◽  
Hyuna Won ◽  
...  
Keyword(s):  
Systematic Approach ◽  
Performance Criteria ◽  
Wearable Electronics ◽  
Design Development ◽  
Performance Requirements ◽  
Development Framework ◽  
Promising Strategy ◽  
Cad Cam ◽  
Wearable Electronic ◽  
Electronic Textile

AbstractThe wearable electronics integrated with textile-based devices is a promising strategy to meet the requirements of human comfort as well as electrical performances. This research presents a design and development framework for a seamless glove sensor system using digital knitting fabrication. Based on the performance requirements of glove sensors for controlling a prosthetic hand, desirable design components include electrical conductivity, comfort, formfit, electrical sensitivity, and customizable design. These attributes are determined and achieved by applying appropriate materials and fabrication technologies. In this study, a digital knitting CAD/CAM system is utilized to meet the desired performance criteria, and two prototypes of the seamless glove sensor systems are successfully developed for the detection of both human and robotic finger motions. This digital knitting system will provide considerable potential for customized design development as well as a sustainable production process. This structured, systematic approach could be adapted in the future development of wearable electronic textile systems.

scholarly journals Methods for Preventing Visual Attacks in Convolutional Neural Networks Based on Data Discard and Dimensionality Reduction

2021 ◽  
Vol 11 (11) ◽  
pp. 5235
Author(s):  
Nikita Andriyanov
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Network Inference ◽  
Recognition Accuracy ◽  
Network Architectures ◽  
Reduced Dimensions ◽  
Different Types ◽  
Rectangular Area ◽  
Noise Impulse ◽  
Selection Of

The article is devoted to the study of convolutional neural network inference in the task of image processing under the influence of visual attacks. Attacks of four different types were considered: simple, involving the addition of white Gaussian noise, impulse action on one pixel of an image, and attacks that change brightness values within a rectangular area. MNIST and Kaggle dogs vs. cats datasets were chosen. Recognition characteristics were obtained for the accuracy, depending on the number of images subjected to attacks and the types of attacks used in the training. The study was based on well-known convolutional neural network architectures used in pattern recognition tasks, such as VGG-16 and Inception_v3. The dependencies of the recognition accuracy on the parameters of visual attacks were obtained. Original methods were proposed to prevent visual attacks. Such methods are based on the selection of “incomprehensible” classes for the recognizer, and their subsequent correction based on neural network inference with reduced image sizes. As a result of applying these methods, gains in the accuracy metric by a factor of 1.3 were obtained after iteration by discarding incomprehensible images, and reducing the amount of uncertainty by 4–5% after iteration by applying the integration of the results of image analyses in reduced dimensions.

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