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Optimization Deployment of Temperature Measurement Points for CZ Silicon Single Crystal Growth Equipment Based on Improved Fuzzy C-Means Clustering

Optimization Deployment of Temperature Measurement Points fo...

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第43届中国控制会议

作者： Weichao Huang Siyuan Cao Ding Lui School of Automation and Information Engineering Xi'an University of Technology Shannxi Key Laboratory of Complex System Control and Intelligent Information Processing

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

The inverse heat transfer method is an effective inversion algorithm for solving the heat transfer at non-measurable places of the study object. However, the current inverse heat transfer inversion method has the problems of low accuracy and difficult selection of temperature measurement point locations. To solve this problem, this paper proposes an optimization algorithm for measurement point locations based on improved fuzzy c-means clustering(IFCM). The method first generates the initial clustering center through a specific density function and introduces the Mahalanobis distance to solve the problem that the traditional fuzzy c-means clustering results depend on the initial clustering center and the results are unstable. Then, an optimized deployment algorithm based on IFCM temperature measurement points is proposed and compared with other mean value clustering algorithms. Finally, the optimal temperature measurement position on the crystal surface is selected by the IFCM temperature measurement point optimization deployment algorithm on the CZ(Czochraski)silicon single crystal growth equipment, and the inversion results of the randomly selected temperature measurement point scheme and the optimized temperature measurement point scheme are compared by the real-time inversion algorithm based on the dynamic matrix control multi-boundary heat flux, and the results of the optimized temperature measurement point scheme are obtained to be better than that of the randomly selected temperature measurement point scheme.

关键词： Fuzzy c-means clustering IFCM Temperature measurement point optimization Inverse heat transfer

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ViT-based Classification of Oracle Bones for Periodization of the Shang Dynasty

ViT-based Classification of Oracle Bones for Periodization o...

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2023 International Conference on Algorithms, High Performance Computing, and Artificial Intelligence, AHPCAI 2023

作者： Li, Bang Ding, Zengmao Zheng, Rongxin Han, Shengwei Zhang, Han Zhang, Zhan Guo, An Wang, Nan Gao, Feng Liu, Yongge School of Computer & Information Engineering Anyang Normal University Henan Anyang455000 China Key Laboratory of Oracle Bone Inscriptions Information Processing Ministry of Education of China Henan Anyang455000 China International Joint Research Laboratory for Perception Data Intelligent Processing of Henan Anyang Normal University Henan Anyang455000 China

ISBN: (纸本)9781510671881

Oracle bone inscriptions are the oldest systematically written characters in China, and their periodization and chronology are essential tasks in historical research. However, current deep-learning models face significant challenges when they are used to recognize oracle bone images directly. These models can only achieve an accuracy of no more than 45%. As the research on the periodization of oracle bones mainly relies on the characters inscribed on the bones, a domain knowledge-based oracle bone classification model is proposed that improves the recognition accuracy of Vision Transformer (ViT) by 5.28%. The proposed approach incorporates results obtained from oracle bone character detection to provide a more effective recognition object. Moreover, a novel model with a style network and category embedding is proposed in this paper that focuses on characters, resulting in a notable improvement in recognition accuracy of 16.01%. Our study reveals the importance of domain knowledge in the classification of historical documents, and it presents new insights into using artificial intelligence for assisting in historical document organization tasks. © 2023 SPIE. All rights reserved.

关键词： Domain Knowledge

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Prominent Edge Characteristics for Open World Object Detection

Prominent Edge Characteristics for Open World Object Detecti...

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International Congress on Image and Signal processing, BioMedical Engineering and Informatics (CISP-BMEI)

作者： Shihao Li Feng Liu Jiangsu Key Laboratory of Intelligent Information Processing and Communication Technology Nanjing University of Posts and Telecommunications Nanjing China

ISBN: (数字)9798331507398

ISBN: (纸本)9798331507404

Open world object detection aims to simulate the human process of recognizing objects in real life scenarios. It can identify categories not introduced during the training phase as unknown during the testing phase and recognize these unknowns in subsequent incremental learning. This capability is beyond the reach of traditional object detection, making it closer to real world human experiences and thus garnering significant attention from researchers. However, existing studies largely focus on generating better pseudo labels to improve the recall rate of unknown objects, which evidently neglects the intrinsic differences between objects and backgrounds. Considering important information such as the visual saliency and edge features of objects in realistic scenes, this paper proposes a novel solution called PEC, which directly extracts objects from their background to detect known and unknown objects more accurately. Comprehensive experiments on the M-OWODB benchmark demonstrate that our method outperforms other current mainstream approaches.

关键词： Training Visualization Computer vision Incremental learning Image edge detection Object detection Signal processing Feature extraction Data mining Informatics

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Design of a Passive Sensor Based on Air Holes for Humidity Detection

Design of a Passive Sensor Based on Air Holes for Humidity D...

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intelligent Design (ICID), International Conference on

作者： Hairong Kou Tingxin Wang Junbing Shi Sheng Cao Shanxi Provincial Key Laboratory of Intelligent Sensing and Multi-dimensional Information Processing Taiyuan University Taiyuan China

ISBN: (数字)9798331541491

ISBN: (纸本)9798331541507

Aiming at the problems of humidity sensors in closed and dangerous situations, limited measurement of humidity parameters by external power supply and limited application range of traditional microwave passive devices composed of metal waveguides or microstrip lines, a new type of waveguide structure for humidity detection is designed and fabricated. The model of the SIW sensor is built, simulated and optimized by using HFSS electromagnetic simulation software, and the design of the passive humidity sensor is completed. A passive humidity sensor is realized by using the air through hole as a humidity sensitive material combined with the SIW structure. The feasibility of the sensor can be used for humidity measurement is verified by circuit analysis and simulation. The PCB board process is used to complete the fabrication of the sensor, and the humidity test platform is used to complete the test of the sensor. The test results show that the sensor can be detected in the range of $70-100 \% \text{rH}$ humidity, and the sensitivity is $1 \text{MHz} / \text{RH} \%$ . The sensor has a wide range of application value in industrial and environmental monitoring and other fields with simple preparation process and high sensitivity.

关键词： Microwave measurement Fabrication Sensitivity Humidity measurement Humidity Electromagnetic waveguides Sensors Environmental monitoring Circuit analysis intelligent sensors

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Enhanced Breakdown Voltage of p-GaN HEMT with a Lateral Gradient in Barrier Layer Component

Enhanced Breakdown Voltage of p-GaN HEMT with a Lateral Grad...

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IEEE International Workshop on Radio-Frequency Integration Technology (RFIT)

作者： Wei Zhi Xingang Ren Hui Wang Shubin Zhao Gang Wang Shuping He Zhixiang Huang Xianliang Wu Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui University Hefei China Key Laboratory of Intelligent Computing & Signal Processing Ministry of Education Anhui University Hefei China Anhui Province Key Laboratory of Simulation and Design for Electronic Information System Hefei Normal University Hefei China Key Laboratory of Intelligent Computing & Signal Processing Anhui University Hefei China

ISBN: (数字)9798331541095

ISBN: (纸本)9798331541101

In this paper, an optimized enhanced-mode p-GaN high electron mobility transistor (p-GaN HEMT) is proposed, in which the barrier layer is set into a composite structure with a lateral gradient of Aluminum component (LGC) from the drain edge under the gate towards both source and drain, respectively. By leveraging the polarization effects from different heterojunctions, a two-dimensional electron gas (2DEG) with gradually changing density is formed laterally in the channel layer, which more precisely modulates the electric field distribution in the gate-drain region, enhances the voltage resistance of it, and reduces leakage current, thereby enhancing the breakdown voltage of the device and improving the output performance. The breakdown voltage of the proposed optimized LGC-HEMT under simulation is 716.73V, which is 13.1 % higher than that of the conventional p-GaN HEMT (633.64V), while the specific on-resistance $(\mathrm{R}_{\mathrm{o}\mathrm{n},\mathrm{s}\mathrm{p}})$ is only 1.34 $m\Omega\cdot cm^{2}$ , and the Baliga's figure of merit (BFOM) changes from 216.42 to 382.68 $MW/cm^2$ , which is 76.82% higher, hinting at the promising potential of the proposed HEMT.

关键词： Resistance Radio frequency Performance evaluation Breakdown voltage HEMTs Logic gates Impact ionization Leakage currents MODFETs Electric fields

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Selective Visual Relationship Detection with Adaptive Context Learning

Selective Visual Relationship Detection with Adaptive Contex...

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International Congress on Image and Signal processing, BioMedical Engineering and Informatics (CISP-BMEI)

作者： Jiangwei Tu Feng Liu Jiangsu Key Laboratory of Intelligent Information Processing and Communication Technology Nanjing University of Posts and Telecommunications Nanjing China

ISBN: (数字)9798331507398

ISBN: (纸本)9798331507404

Visual relation detection aims to describe the relationships between objects in a scene by using the form of a triplet . Existing methods not only suffer from the huge number of combinations of triples, but also make the context of most of the object pairs in a scene non-discriminative due to simply using the union box of the object pair. In this work, we propose a novel two-stage network. In the first stage, the multi-modal feature fusion strategy is used to complete the correlation detection of object pairs, which reduces the number of triples and enables the model to selectively focus on the relationship between strongly correlated objects in the scene. The object-directed context module (OCM) and global message passing (GMP) introduced in the second stage to adaptively learning the higher-order context of each object pair in the scenario. We evaluate our method on two widely used datasets: Visual Relationship Detection (VRD) and Visual Genome(VG) datasets. Experimental results verify the effectiveness of our method, e.g., the top 100 phrases detection recall improves from 40.01% to 41.21% on the VRD dataset.

关键词： Visualization Adaptation models Correlation Filtering Message passing Genomics Signal processing Feature extraction Context modeling Biomedical engineering

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An Adaptive Parallel Gaussian Homotopy Approach for Zeroth-Order Optimization

An Adaptive Parallel Gaussian Homotopy Approach for Zeroth-O...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Bingyi Zhou Guoyou Wang National Key Laboratory of Multispectral Information Intelligent Processing Technology Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

The Gaussian homotopy method is a classical optimization approach for solving nonconvex problems. It applies Gaussian smoothing to a given problem, using varying smoothing factors to generate a series of proxy problems that are easier to solve, subsequently addressing them gradually from simpler to more complex ones. Traditional Gaussian homotopy methods typically utilize a predefined sequence or parameter to update a single smoothing factor serially. However, this sequential updating process is inefficient and unsuitable for parallel homotopy pipelines. Moreover, the utilization of predefined sequences or parameters leads to a lack of adaptability. To address the abovementioned challenges, we propose an adaptive parallel Gaussian homotopy optimization method. Initially, we introduce an adaptive model called Scaling. This model is formulated as a scaler that concurrently adjusts multiple smoothing factors, with each factor aligning to a homotopy level. It can be integrated easily into serial and parallel homotopy pipelines. Furthermore, we establish a collaborative training regimen to jointly train the Scaling model and a parallel homotopy model, named continuation path learning (CPL) [1] model. Throughout the training process, the Scaling model furnishes CPL with multiple scaled smoothing factors and updates CPL implicitly. Extensive experiments demonstrate that the proposed Gaussian homotopy approach performs competitively.

关键词： Training Adaptation models Smoothing methods Pipelines Optimization methods Collaboration Cybernetics

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Powerful Lossy Compression for Noisy Images

Powerful Lossy Compression for Noisy Images

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Shilv Cai Xiaoguo Liang Shuning Cao Luxin Yan Sheng Zhong Liqun Chen Xu Zou Huazhong University of Science and Technology China National Key Laboratory of Multispectral Information Intelligent Processing Technology China

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

Image compression and denoising represent fundamental challenges in image processing with many real-world applications. To address practical demands, current solutions can be categorized into two main strategies: 1) sequential method; and 2) joint method. However, sequential methods have the disadvantage of error accumulation as there is information loss between multiple individual models. Recently, the academic community began to make some attempts to tackle this problem through end-to-end joint methods. Most of them ignore that different regions of noisy images have different characteristics. To solve these problems, in this paper, our proposed signal-to-noise ratio (SNR) aware joint solution exploits local and non-local features for image compression and denoising simultaneously. We design an end-to-end trainable network, which includes the main encoder branch, the guidance branch, and the signal-to-noise ratio (SNR) aware branch. We conducted extensive experiments on both synthetic and real-world datasets, demonstrating that our joint solution outperforms existing state-of-the-art methods.

关键词： Image coding Noise reduction Decoding Noise measurement Signal to noise ratio

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SPIKING CONVOLUTIONAL NEURAL NETWORKS FOR TEXT CLASSIFICATION

arXiv

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arXiv 2024年

作者： Lv, Changze Xu, Jianhan Zheng, Xiaoqing School of Computer Science Fudan University Shanghai 200433 China Shanghai Key Laboratory of Intelligent Information Processing

Spiking neural networks (SNNs) offer a promising pathway to implement deep neural networks (DNNs) in a more energy-efficient manner since their neurons are sparsely activated and inferences are event-driven. However, there have been very few works that have demonstrated the efficacy of SNNs in language tasks partially because it is non-trivial to represent words in the forms of spikes and to deal with variable-length texts by SNNs. This work presents a "conversion + fine-tuning" two-step method for training SNNs for text classification and proposes a simple but effective way to encode pre-trained word embeddings as spike trains. We show empirically that after fine-tuning with surrogate gradients, the converted SNNs achieve comparable results to their DNN counterparts with much less energy consumption across multiple datasets for both English and Chinese. We also show that such SNNs are more robust to adversarial attacks than DNNs. © 2024, CC BY.

关键词： Deep neural networks

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Community Energy Management System Based on Deep Learning and Lagrangian Programming Neural Network

Community Energy Management System Based on Deep Learning an...

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Neuromorphic Computing (ICNC), International Conference on

作者： Shuying Xu Xing He Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing School of Electronic and Information Engineering Southwest University Chongqing China

ISBN: (数字)9798331508661

ISBN: (纸本)9798331508678

In this paper, a innovative approach is proposed for solving the energy scheduling problem (ESP) in community energy management system (CEMS), combining deep learning with lagrangian programming neural network (LPNN). First, The LPNN is employed to address the ESP, and its effectiveness is thereafter validated. Subsequently, a deep learning-based LPNN (DLPNN) model accompanied by a specially designed loss function is introduced. This enables the trained DLPNN to solve the ESP in CEMS without the need for repeated steps, which is a limitation of using LPNN alone. Additionally, the dynamic variables in the ESP are incorporated as input variables for training the DLPNN, allowing the model to determine optimal solutions for varying values of these variables. Ultimately, the effectiveness of the introduced approach is confirmed through simulation experiments.

关键词： Deep learning Training Processor scheduling Computational modeling Neural networks Urban areas Programming Dynamic scheduling Dynamic programming Energy management systems

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