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- 2018
- Conditional Random Fields (CRF)
- Convolutional Neural Network (CNN)
- Fine Classification
- Airborne hyperspectral
- Optoelectronic Information Science and Engineering
- Wuhan University
- Central South University
- Hubei University
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Detection of powder bed defects in selective laser sintering using convolutional neural network
摘要： The presence of defects in a powder bed fusion (PBF) process can lead to the formation of flaws in consolidated parts. Powder bed defects (PBDs) have different sizes and shapes and occur in different locations in the built area. Those variations pose great challenges to their detection. In this study, a deep convolution neural network was applied to detect three typical types of PBDs in a selective laser sintering (SLS) process, namely warpage, part shifting, and short feed, which were intentionally generated by varying the process conditions. Images of the powder bed were captured using a digital camera, which were split into three single-channel images corresponding to the color channels in the color image. A deep residual neural network was then used to extract multiscale features, and a region proposal network was adopted to detect the object-level defect bounding box. In the final stage, a fully convolutional neural network was proposed to generate instance-level defect regions in the bounding box. Our results demonstrated that this method had higher accuracy and efficiency and was able to cope with geometrical distortion and image blurring, in comparison to the defect detection methods reported previously. Also, the detection system was cost-effective and could be easily installed outside the chamber of a PBF system. This study lays the groundwork for the development of a variety of automated technologies for additive manufacturing, such as real-time powder layer quality inspection and 3D quality certificate generation for finish parts.
关键词： Defect,Neural network,Selective laser sintering,Powder bed fusion,Detection
更新于2025-09-23 15:21:01
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Mesh-Structure-Enabled Programmable Multi-Task Photonic Signal Processor on a Silicon Chip
摘要： Photonic integrated circuits (PIC) have recently attracted extensive attention in advanced photonic signal processing to meet the ever-increasing demands on high-speed and ultra-compact data signal management. However, programmable and multi-task photonic signal processing is still full of challenges, especially the scalable photonic integration solution. Here, we design, fabricate and demonstrate a mesh-structure-enabled programmable multi-task photonic signal processor on a silicon chip. It relies on a scalable 2D mesh structure network with a number of hexagonal unit cells formed by building blocks of tunable Mach-Zehnder interferometers (MZIs). We study several simple and complex optical filtering functions using configured single ring resonator, cascaded ring resonators, ring assisted MZI, cascaded MZIs, reconfigurable and tunable comb filter and (de)interleaver, and dual-injection ring resonators. For the proof-of-concept demonstration of on-chip programmable multi-task photonic signal processing, a monolithically integrated silicon chip with four hexagonal unit cells is fabricated with greatly reduced geometric dimension. By appropriately adjusting the thermo-optic phase shifters of MZIs, versatile programmable multi-task photonic signal processing functions are demonstrated in the experiment with impressive performance, including single ring resonator, cascaded ring resonators, asymmetric MZI, ring assisted MZI, optical delay line, multi-port router, N×N optical switch, and optical neural network (ONN) enabled self-configurable router and switch and their practical applications in fiber-optic communication systems. The demonstrations may open up new perspectives for on-chip solutions to ultra-compact, reconfigurable, programmable and multi-functional data signal management in advanced optical communication networks.
关键词： programmable multi-task photonic signal processor,optical neural network,multi-port router,Silicon photonics,photonic integrated circuits,optical switch
更新于2025-09-23 15:21:01
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The application of image analysis technology in the extraction of human body feature parameters
摘要： With the improvement of living standards, personalized clothing customization has become a trend of people’s apparel demand. The key factor in personalized clothing customization is a three-dimensional human modeling. With the development of image analysis technology, it is possible to use image analysis technology to extract human characteristics. In this paper, two-dimensional human feature regions and characteristic parameter extraction methods of images are used. The backpropagation neural network (BP neural network) is used to curve the three-dimensional human characteristics, and the neck, chest, waist, and buttocks of 22 subjects are verified. The results show that the use of this method can well achieve the extraction of human characteristic parameters.
关键词： Human characteristics,BP neural network,Image analysis,Feature extraction,Computer-aided design (CAD)
更新于2025-09-23 15:21:01
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Short-Term Photovoltaic Power Forecasting Using a Convolutional Neural Networka??Salp Swarm Algorithm
摘要： The high utilization of renewable energy to manage climate change and provide green energy requires short-term photovoltaic (PV) power forecasting. In this paper, a novel forecasting strategy that combines a convolutional neural network (CNN) and a salp swarm algorithm (SSA) is proposed to forecast PV power output. First, the historical PV power data and associated weather information are classified into five weather types, such as rainy, heavy cloudy, cloudy, light cloudy and sunny. The CNN classification is then used to determine the prediction for the next day’s weather type. Five models of CNN regression are established to accommodate the prediction for different weather types. Each CNN regression is optimized using a salp swarm algorithm (SSA) to tune the best parameter. To evaluate the performance of the proposed method, comparisons were made to the SSA based support vector machine (SVM-SSA) and long short-term memory neural network (LSTM-SSA) methods. The proposed method was tested on a PV power generation system with a 500 kWp capacity located in south Taiwan. The results showed that the proposed CNN-SSA could accommodate the actual generation pattern better than the SVM-SSA and LSTM-SSA methods.
关键词： convolutional neural network,salp swarm algorithm,renewable energy,day ahead forecasting,PV power forecasting
更新于2025-09-23 15:21:01
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[Lecture Notes in Computer Science] Pattern Recognition and Computer Vision Volume 11256 (First Chinese Conference, PRCV 2018, Guangzhou, China, November 23-26, 2018, Proceedings, Part I) || Automatic Measurement of Cup-to-Disc Ratio for Retinal Images
摘要： Glaucoma is a chronic eye disease which results in irreversible vision loss, and the optic cup-to-disc ratio (CDR) is an essential clinical indicator in diagnosing glaucoma, which means precise optic disc (OD) and optic cup (OC) segmentation become an important task. In this paper, we propose an automatic CDR measurement method. The method includes three stages: OD localization and ROI extraction, simultaneous segmentation of OD and OC, and CDR calculation. In the ?rst stage, the morphological operation and the sliding window are combined to ?nd the OD location and extract the ROI region. In the second stage, an improved deep neural network, named U-Net+CP+FL, which consists of U-shape convolutional architecture, a novel concatenating path and a multi-label fusion loss function, is adopted to simultaneously segment the OD and OC. Based on the segmentation results, the CDR value can be calculated in the last stage. Experimental results on the retinal images from public databases demonstrate that the proposed method can achieve comparable performance with ophthalmologist and superior performance when compared with other existing methods. Thus, our method can be a suitable tool for automated glaucoma analysis.
关键词： OD&OC segmentation,Glaucoma diagnosis,Deep neural network,OD localization,Cup-to-disc ratio (CDR)
更新于2025-09-23 15:21:01
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Noncontact detection of concrete flaws by neural network classification of laser doppler vibrometer signals
摘要： This study aimed to develop a non-contact and high-speed damage detection technology for use on concrete structures. A laser Doppler vibrometer was used to obtain the vibrations of a concrete structure at a high signal-to-noise ratio. The observed vibration data were transformed into frequency spectra by Fourier transform. Using the simulation by the finite element method, it was predicted that the characteristic spectrum appeared in the low frequency region for the cracked part. However, the experimental results did not show such a difference clearly. In contrast, in the high-frequency region of the experimental data, a spectrum peculiar to the cracked part tended to appear. Nonetheless, the difference was so small that it was often buried by variations in hammering strength. Therefore, it was difficult to manually determine the signal of the cracked part. Machine learning using a convolutional neural network was carried out in order to judge the location and dimensions of a cracked part with high accuracy. As a result, cracks in the concrete were detected with a high accuracy of more than 90%.
关键词： convolutional neural network,laser doppler vibrometer,concrete,non-destructive inspection
更新于2025-09-23 15:21:01
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Image-based relighting using image segmentation and bootstrap strategy
摘要： Image-based relighting technologies enable us to recover the illumination effects of modeled scenes under new light conditions without complicated geometrical information. However, most of them are troubled by specialized devices and tedious sampling work. In this study, we propose an efficient and accurate image-based relighting method for the estimation of the light transport matrix of modeled scene, starting from a small number of images acquired with a fixed viewpoint and with lighting sampled over a uniform 2D grid. Especially, the image space is segmented based on the position and average color value of each pixel using K-means. The local coherence among the pixels can be considered to associate with pixel position and pixels’ albedo. The pixels of each cluster can be trained by several neural networks and the training scene datasets can be chosen using the bootstrap strategy. These tricks improve the regression performance. We validate our method with light transport data of several scenes containing complex lighting effects. The obtained results show that the proposed method is useful for practical applications and we can get more plausible rendered images with fewer input images in comparison to related techniques.
关键词： Image-based relighting,Image segmentation,K-means,Neural network,Bootstrap strategy
更新于2025-09-23 15:21:01
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A Photovoltaic Array Fault Diagnosis Method Considering the Photovoltaic Output Deviation Characteristics
摘要： There are a large number of photovoltaic (PV) arrays in large-scale PV power plants or regional distributed PV power plants, and the output of different arrays fluctuates with the external conditions. The deviation and evolution information of the array output are easily covered by the random fluctuations of the PV output, which makes the fault diagnosis of PV arrays difficult. In this paper, a fault diagnosis method based on the deviation characteristics of the PV array output is proposed. Based on the current of the PV array on the DC (direct current) side, the deviation characteristics of the PV array output under different arrays and time series are analyzed. Then, the deviation function is constructed to evaluate the output deviation of the PV array. Finally, the fault diagnosis of a PV array is realized by using the probabilistic neural network (PNN), and the effectiveness of the proposed method is verified. The main contributions of this paper are to propose the deviation function that can extract the fault characteristics of PV array and the fault diagnosis method just using the array current which can be easily applied in the PV plant.
关键词： photovoltaic array,deviation characteristics,fault diagnosis,probabilistic neural network
更新于2025-09-23 15:21:01
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Mechanisms for Enhanced State Retention and Stability in Redox-Gated Organic Neuromorphic Devices
摘要： Recent breakthroughs in artificial neural networks (ANNs) have spurred interest in efficient computational paradigms where the energy and time costs for training and inference are reduced. One promising contender for efficient ANN implementation is crossbar arrays of resistive memory elements that emulate the synaptic strength between neurons within the ANN. Organic nonvolatile redox memory has recently been demonstrated as a promising device for neuromorphic computing, offering a continuous range of linearly programmable resistance states and tunable electronic and electrochemical properties, opening a path toward massively parallel and energy efficient ANN implementation. However, one of the key issues with implementations relying on electrochemical gating of organic materials is the state-retention time and device stability. Here, revealed are the mechanisms leading to state loss and cycling instability in redox-gated neuromorphic devices: parasitic redox reactions and out-diffusion of reducing additives. The results of this study are used to design an encapsulation structure which shows an order of magnitude improvement in state retention and cycling stability for poly(3,4-ethylenedioxythio phene)/polyethyleneimine:poly(styrene sulfonate) devices by tuning the concentration of additives, implementing a solid-state electrolyte, and encapsulating devices in an inert environment. Finally, a comparison is made between programming range and state retention to optimize device operation.
关键词： resistive memory,PEDOT:PSS,polymer semiconductor,artificial synapse,neural network
更新于2025-09-23 15:21:01
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Setting Up Surface-Enhanced Raman Scattering Database for Artificial Intelligence-Based Label-Free Discrimination of Tumor Suppressor Genes
摘要： The quality of input data in deep learning is tightly associated with the ultimate performance of machine learner. Taking advantages of unique merits of surface-enhanced Raman scattering (SERS) methodology in the collection and construction of database (e.g., abundant intrinsic fingerprint information, noninvasive data acquisition process, strong anti-interfering ability, etc.), herein we set up SERS-based database of deoxyribonucleic acid (DNA), suitable for artificial intelligence (AI)-based sensing applications. The database is collected and analyzed by silver nanoparticles (Ag NPs)-decorated silicon wafer (Ag NPs@Si) SERS chip, followed by training with a deep neural network (DNN). As proof-of-concept applications, three kinds of representative tumor suppressor genes, i.e., p16, p21 and p53 fragments, are readily discriminated in label-free manners. Prominent and reproducible SERS spectra of these DNA molecules are collected and employed as input data for DNN learning and training, which enables selective discrimination of DNA target(s). The accuracy rate for the recognition of specific DNA target reaches 90.28%.
关键词： surface-enhanced Raman scattering,label-free discrimination,deep neural network,tumor suppressor genes,artificial intelligence
更新于2025-09-23 15:21:01