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A method of Named Entity recognition in Classical Chinese based on Bert-Ancient-Chinese

A method of Named Entity Recognition in Classical Chinese ba...

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international conference on machine learning, Big data and Business Intelligence (MLBDBI)

作者： Ping Feng Jingyu Kang Biao Huang Shulai Wang Jialun Li Jilin University Changchun Jilin China Changchun University Changchun Jilin China Jilin Provincial Key Laboratory of Human Health State Identification and Function Enhancement Changchun Jilin

At present, in the field of Natural Language Processing (NLP), the accuracy of pre-trained models for downstream tasks such as Named Entity recognition (NER) is significantly improved. Named Entity recognition is a key technology in Knowledge Graphs (KG), but there are relatively few studies on this technology in the field of Classical Chinese. this paper proposes a Classical Chinese Named Entity recognition model based on bert-ancient-chinese, which is based on bert-ancient-chinese+Recurrent Long Short-Term Memory+Conditional Random Field (BAC+RLSTM+CRF) for Named Entity recognition and tested on the C-CLUE dataset. Compared with the baseline of the dataset, the F1 value is improved by about 22.11%.

关键词： Training Semantics machine learning Knowledge graphs Big data Benchmark testing Natural language processing

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Prediction of Unsteady Indoor-Temperature with Few pattern data learning and Prediction Model Selection Based on Feature Contribution 4

Prediction of Unsteady Indoor-Temperature with Few Pattern D...

引用

4th international conference on Artificial Intelligence for Industries, AI4I 2021

作者： Maejima, Sotaro Tsunoda, Keisuke Kodama, Midori Arai, Naoki Obana, Kazuaki NTT Smart Data Science Center Nippon Telegraph and Telephone Corporation Tokyo Japan

ISBN: (纸本)9781665434102

the aim of this paper is to predict indoor-temperature by machine learning under three main constraints: 1) indoor-temperature is unsteady due to people flow, 2) only data with few control patterns of air-conditioning can be used for training, and 3) indoor-temperature is to be accurately and plausibly predicted under unknown air-conditioning control patterns not included in training data. Previous studies tried to predict indoor-temperature in buildings without people but with air-conditioning data for various control patterns. However, these constraints make predictions of indoor-temperature irregular because of unsteady indoor-temperature and inadequate data patterns. To solve the problems, we propose a model selection method based on prediction accuracy and feature contribution. the method can select the prediction model appropriate for the observed instability and can augment data patterns We demonstrate the effectiveness of our proposal using measured sensor data. © 2021 IEEE

关键词： Air conditioning

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Shearer cutting pattern recognition based on multi-scale fuzzy entropy and support vector machine 4

Shearer cutting pattern recognition based on multi-scale fuz...

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2020 4th international conference on Energy Material, Chemical Engineering and Mining Engineering, EMCEME 2020

作者： Liang, Bin Liu, Ze Niu, Yanbo China University of Mining and Technology Xuhai College Xuzhou221008 China China University of Mining and Technology School of Mechatronic Engineering Xuzhou221116 China

Aiming at the problem of low intelligent level of shearer, a shearer cutting pattern recognition method is proposed based on the combination of multi-scale fuzzy entropy, Laplace score and support vector machine. By extracting the multi-scale fuzzy entropy of the vibration signal under different cutting modes, the feature vector representing the cutting pattern is mastered. At the same time, the Laplace score is used to select the feature vectors with possessing rich cutting pattern information. the selected features are produced as the learning samples of support vector machine. the experimental system of shearer cutting coal-rock is built, and the vibration signals of rocker arm under different cutting patterns are extracted. the experimental analysis is carried out and the results indicate that the cutting pattern recognition method proposed in this paper has high recognition accuracy, and the correct rate can reach to 98.86%. the research results provide technical support for the intelligent and rapid development of fully mechanized mining face. © Published under licence by IOP Publishing Ltd.

关键词： Support vector machines

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Speech Emotion recognition Using 2D-CNN with data Augmentation 7

Speech Emotion Recognition Using 2D-CNN with Data Augmentati...

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7th international conference on Software Engineering and Computer Systems and 4th international conference on Computational Science and Information Management, ICSECS-ICOCSIM 2021

作者： Mujaddidurrahman, Auliya Ernawan, Ferda Wibowo, Adi Sarwoko, Eko Adi Sugiharto, Aris Wahyudi, Muhammad Didik Rohmad Diponegoro University Department of Informatics Semarang Indonesia Universiti Malaysia Pahang Faculty of Computing College of Computing and Applied Sciences Pahang Malaysia Universitas Islam Negeri Sunan Kalijaga Yogyakarta Indonesia

ISBN: (纸本)9781665414074

Speech emotion recognition becomes a challenging issue, especially interaction between human-machine. Each person expressed their emotions in various ways and the features of speech are still unclear to distinguish between existing emotions. the results of speech were caused by mental and psychological states where it is directly influenced by emotions. this research proposed speech emotion recognition based on the 2D-CNN model with Log-Mel spectrogram as the input. the proposed method of the 2D-CNN model with Log-Mel spectrogram was able to capture the significant speech signals. the experiments used the EMODB database to evaluate the proposed model with augmented data. the proposed model was able to produce higher accuracy of speech emotion than the existing models, which also used deep learning methods for recognizing emotions. the experimental results showed that the proposed model achieved an accuracy of 0.88. © 2021 IEEE.

关键词： Neural network models

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Partial Discharge pattern recognition in High Voltage Transmission Cable Project

Partial Discharge Pattern Recognition in High Voltage Transm...

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Electronic Technology, Communication and Information (ICETCI), IEEE international conference on

作者： Guangxiuyuan Zhu Shengjie Wang Yongsheng Zhang Ling Jiang Qiuyang Li Daping Zhang Electric Power Research Institute of State Grid Qinghai Electric Power Company Xining China Qinghai Key Laboratory of High Altitude Electric Power Research Xining China Qinghai High Altitude Power Engineering Technology Research Center Xining China State Grid Qinghai Electric Power Company Xining China Xining Power Supply Company of State Grid Qinghai Electric Power Company Xining China

ISBN: (数字)9798350361643

ISBN: (纸本)9798350361650

this paper aims to improve the reliability and safety of cables, prolong the service life of cables and reduce the operating cost of power systems. the paper examines in detail how partial discharge (PD) patterns are identified in high-voltage transmission cable projects. this paper tests the performance of several layers of a convolutional neural network (CNN) to increase recognition efficiency and accuracy. During the experiment, unified network parameters are set, including the use of maximum pooling mode, ReLU activation function, and 32 33-size convolution kernels in each layer. By contrasting the recognition accuracy of 1-layer, 2-layer, 3-layer, 4-layer, 6-layer, and 9-layer CNN, the paper’s findings indicate that: (1) CNN’s recognition accuracy peaks at 90.77% when there are two network layers. However, as the number of layers increases, the accuracy of recognition gradually declines. this result reveals that moderately increasing the network depth is helpful to improve the performance in PD pattern recognition of high voltage transmission cables, but too deep network may lead to over-fitting. (2) CNN is stable in the detection of most defect types, while Support Vector machine (SVM) is the best in some specific defects. Back propagation neural network (BPNN) performs well in some cases, but its overall performance fluctuates greatly. these results are influenced by many factors, such as defect characteristics, model parameters and data set size. therefore, in practical application, it is necessary to select a suitable model for defect detection according to specific scenarios and requirements. this paper provides a useful reference for the follow-up research in high voltage transmission cable project.

关键词： Support vector machines Partial discharges Accuracy Transfer learning High-voltage techniques pattern recognition Safety

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Color Harmony in Visual Communication: Brand Color Design Based on Genetic Algorithm

Color Harmony in Visual Communication: Brand Color Design Ba...

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Electronic Communications, Internet of things and Big data (ICEIB), international conference on

作者： Xiaolu Fang Wuhan Huaxia Institute of Technology Wuhan China

ISBN: (数字)9798350360721

ISBN: (纸本)9798350360738

Color is important for visual communication, especially in branding to transmit feelings, form a brand identity, and leave meaningful traces in people's minds. thus, it is critical to optimize the brand color design with brand objectives and target audience preferences. the proposed solution for such optimization is to use genetic algorithms, a computational optimization technique inspired by natural selection, to generate harmonious color palettes. Case studies demonstrated the effectiveness of this method with optimized color schemes that exhibit superior harmony and desired brand attributes for customer preferences. the results show an increase in brand recognition and customer engagement. With genetic algorithms, companies can develop color schemes according to psychological rules and obtain psychological responses to create a powerful and unique brand identity.

关键词： Visualization Image color analysis Visual communication Brand management Psychology Color machine learning

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Target recognition Method Based on Incremental learning

Target Recognition Method Based on Incremental Learning

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international conference on Applied machine learning (ICAML)

作者： Weize Qin Institute of computing technology Chinese academy of science Beijing China

the traditional batch learning method is only suitable for the solidification recognition model with a fixed number of samples. When the number of new training samples is increasing, the existing and new training samples must be reintegrated, and the recognition model must be retrained to complete the model update. the time cost, computational cost and storage resources of each update are very high, which greatly reduces the efficiency of recognition. In this paper, a new target recognition algorithm based on incremental learning is proposed. By expanding the feature extraction network and classification network, the problem of catastrophic forgetting is effectively suppressed. At the same time, the decoupling small sample incremental learning model based on prototype registration uses meta-learning method in the training process to effectively improve the efficiency of model learning on small sample data. the results are verified on CIFA100 and Tianzhi Cup challenge data sets. After two incremental learning, the recognition accuracy of the algorithm is reduced by only 21 % and 13 %, which is higher than the existing best algorithm by 4.7 % / 19.6 %. the results show that the method based on sample playback and model expansion is compatible, which can improve the accuracy of existing methods.

关键词： Training Solid modeling Costs Target recognition Computational modeling Prototypes machine learning

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Enhanced Speech Emotion and Gender recognition Using Hybrid CNN-LSTM Models

Enhanced Speech Emotion and Gender Recognition Using Hybrid ...

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Sentiment Analysis and Deep learning (ICSADL), international conference on

作者： S Priyadharshini G. Naveen Sundar D. Narmadha Senthil Kumar thangavel Adeyemi Abel Ajibesin Selvanayaki Kolandapalayam Shanmugam Department of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India Department of Computer Science and Engineering Amrita School of Computing Coimbatore India Department of Information Technology Faculty of Informatics and Design Cape Peninsula University of Technology South Africa Department of Mathematics and Computer Science Ashland University USA

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

the current paper proposes a new approach for peripheral speech emotion analysis and gender estimation incorporating the best machine learning architectures such as CNNs and LSTMs. Its correct depiction of emotions and the variation of the gendered speech to indicate manifold signal-to-noise ratio make the approach accommodate the difficulty inherent in recognizing emotions from the recording of voices as well as predicting the gender of a speaker. It is then able to ally with preprocessing and collection of a vast range of different speech samples, feature extraction of pitch, tone and spectral data. these features serve as input to the CNNs and LSTMs: LSTMs work well at temporal regular features and the CNNs work well in feature extraction by location (spatio feature). the approach is tested on the RAVDESS and IEMOCAP datasets and establishes a near perfect match to prior approaches with reference to recognition accuracy of emotion analysis and gender prediction. thus, applying the identified dual method approach, which could be described by such characteristics as highly accurate data pre-preprocessing, clear indication of the features of interest, a different CNN-LSTM model joint training, it offers a reasonably good platform to gain certain insights into such characteristics of the speech as our experiment demonstrates. this work offers satisfactory configurations on concerns such as human computer relation, customer relation and mental health surveillance to suggest that other applications could be achieved in future.

关键词： Training Emotion recognition Accuracy Computational modeling Speech recognition Speech enhancement Predictive models Feature extraction data models Convolutional neural networks

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Improving Image Compression Performance by Spatial-Channel Context Adaptive Model

Improving Image Compression Performance by Spatial-Channel C...

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Electronic Communication and Artificial Intelligence (IWECAI), international Workshop on

作者： Hao Wang Huifen Wang Junda Xue Enmin Lu Hanming Wang Zijun Wu Yunlong Song Institute of Big Data and Artificial Intelligence China Telecom Corporation Limited Beijing Research Institute Beijing China

the significance of enhancing image compression efficiency for machine vision, analysis, and comprehension tasks has gained increasing recognition. In response to this need, we propose and implement a novel method called ELIC (Efficient Learned Image Compression with Unevenly Grouped Space-Channel Contextual Adaptive Coding) to achieve high compression efficiency. Our method is evaluated on the classic OpenImage V6 Common Test Condition (CTC) eval datasets, and its performance is compared to baseline methods for machine vision tasks. the results of our study demonstrate a substantial enhancement in compression efficiency, suggesting that the ELIC technique holds promise for pushing the boundaries of state-of-the-art visual compression for vision tasks. Furthermore, we believe that our approach can promote the application of learning-based image compression.

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Intelligent Fault Diagnosis of Gearbox in Rotating machinery Using Adversarial Neural Networks under Variable Speeds

Intelligent Fault Diagnosis of Gearbox in Rotating Machinery...

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Electrical machines and Drives (ICEMD), international conference on

作者： Saba Abhari Mostafa Abedi Javad Hasanpour-Sangelaji Electrical Engineering Shahid Beheshti University Tehran Iran Department of Electrical Engineering Shahid Beheshti University Tehran Iran

ISBN: (数字)9798331507510

ISBN: (纸本)9798331507527

this research presents an intelligent fault diagnosis method for gearbox systems in rotating machinery using Generative Adversarial Networks (GANs). Due to variable speeds and the scarcity of reliable data in real-world conditions, accurate fault detection faces significant challenges. While GANs can generate realistic synthetic data, this study leverages adversarial training, aiming to make the diagnostic system resilient to speed variations. By combining adversarial learning with real data, diagnostic models can better recognize various fault patterns and improve detection accuracy. the primary goal of this study is to enhance the precision and reliability of gearbox fault diagnosis under different operational conditions using GANs. With deep learning techniques, the proposed approach can identify various gearbox faults even under limited and fluctuating data conditions. Experimental results demonstrate that this approach improves detection performance and fault classification accuracy compared to traditional methods.

关键词： Fault diagnosis Training Accuracy Fault detection Neural networks Generative adversarial networks data models pattern recognition machinery Synthetic data

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