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检索条件"任意字段=3rd International Conference on Signal Processing and Machine Learning, CONF-SPML 2023"

共 147 条记录，以下是131-140 订阅

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Classification and Prediction of Incipient Faults in Transformer Oil by Supervised machine learning using Decision Tree

Classification and Prediction of Incipient Faults in Transfo...

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international Symposium on Artificial Intelligence and signal processing (AISP)

作者： Raymon Antony Raj Sarathkumar D Sampath Kumar Venkatachary Leo John Baptist Andrews Department of Electrical and Electronics Engineering Kongu Engineering College Erode district Tamilnadu India Grant Thornton Botswana Plot Acument Park Fair Grounds Gaborone Botswana Department of Information Technology Faculty of Engineering and Technology Botho University Gaborone Botswana

Transformer condition evaluation is a crucial technique for displaying a transformer’s health index. Key gases (KG), which dissolve in oil and become inseparable, are developed because of the different nascent flaws formed in the transformer enclosure. The exact defect that caused the gases to evolve may be found using the composition of KG present in the transformer. Five KG, including hydrogen H 2 , methane (CH 4 ), ethane (C 2 H 2 ), ethylene C 2 H 4 ), and acetylene (C 2 H 2 ), are employed in this study as an input to anticipate the real incipient defect. In the inquiry that follows, classification is carried out using a Decision Tree (DT) and the data is trained using a Supervised machine learning (SML). The dataset is made up of 180 different input KG compositions and defects that are trained, identified, and predicted using a predictive model. 16 more datasets with KG composition are utilized for prediction. TPR versus FNR, PPV vs FDR, AUC, parallel coordinates plot, confusion matrix, and lowest classification error are used to assess the statistical correctness. The accuracy of the projected data using DT and optimized DT is 62.9%, however the accuracy of the optimized DT with principal component analysis (PCA) is only 55%. Therefore, the suggested DT fault classification approach may be utilized to accurately forecast the onset of faults.

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Enhancing Interactive Storytelling: A Computational Approach to Autonomously Generating Role-Playing Game Scripts with Natural Language processing

Enhancing Interactive Storytelling: A Computational Approach...

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signal processing, Communications and Engineering Management conference (ISPCEM), international

作者： Qijun Zhong Beijing World Youth Academy Beijing China

In the realm of role-playing games (RPGs), the crafting of rich and detailed narratives is crucial, with legendary games like Dungeons & Dragons (D&D) setting the bar for creativity and player-driven storytelling. This research explores the innovative intersection of artificial intelligence and RPGs by developing a method for the automated generation of RPG scripts, harnessing the capabilities of advanced natural language processing (NLP). Our study presents an elaborate NLP framework, utilizing a comprehensive collection of D&D source materials, to methodically extract key character features (such as attributes, races, and classes) using machine learning techniques. This framework is further enhanced by integrating sentiment analysis and a sophisticated bi-directional recurrent neural network equipped with a multi-head attention mechanism. This combination is instrumental in accurately modeling narrative elements like event sequences, causal relationships, and thematic links. The culmination of this process is the automated generation of diverse and engaging RPG scripts, each characterized by distinct character dynamics, immersive settings, and compelling story arcs. The effectiveness and impact of these generated scripts were evaluated through a user study that gathered both qualitative and quantitative data. The findings demonstrate our system's proficiency in creating narratives that are not only contextually diverse but also resonate deeply with RPG conventions, indicating a significant advancement in interactive storytelling. This research not only reinforces the role of AI in augmenting game design but also underscores the broader potential of AI in creative content generation, marking a new era in the synergy between computational technology and artistic narrative creation.

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Time-Weighted Frequency Domain Audio Representation with GMM Estimator for Anomalous Sound Detection

Time-Weighted Frequency Domain Audio Representation with GMM...

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international conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Jian Guan Youde Liu Qiaoxi Zhu Tieran Zheng Jiqing Han Wenwu Wang Group of Intelligent Signal Processing College of Computer Science and Technology Harbin Engineering University China School of Computer Science and Technology Harbin Institute of Technology China Centre for Audio Acoustics and Vibration University of Technology Sydney Australia Centre for Vision Speech and Signal Processing University of Surrey UK

Although deep learning is the mainstream method in unsupervised anomalous sound detection, Gaussian Mixture Model (GMM) with statistical audio frequency representation as input can achieve comparable results with much lower model complexity and fewer parameters. Existing statistical frequency representations, e.g. the log-Mel spectrogram’s average or maximum over time, do not always work well for different machines. This paper presents Time-Weighted Frequency Domain Representation (TWFR) with the GMM method (TWFR-GMM) for anomalous sound detection. The TWFR is a generalized statistical frequency domain representation that can adapt to different machine types, using the global weighted ranking pooling over time-domain. This allows GMM estimator to recognize anomalies, even under domain-shift conditions, as visualized with a Mahalanobis distance-based metric. Experiments on DCASE 2022 Challenge Task2 dataset show that our method has better detection performance than recent deep learning methods. TWFR-GMM is the core of our submission that achieved the 3rd place in DCASE 2022 Challenge Task2.

关键词： Measurement Deep learning Visualization Time-frequency analysis signal processing Complexity theory Task analysis

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FPGA processing of Decision Tree Ensembles Stored in External DRAM

FPGA Processing of Decision Tree Ensembles Stored in Externa...

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Electrical, Computer, Communications and Mechatronics Engineering (ICECCME), international conference on

作者： Jakob Winkler Michael Lunglmayr Institute of Signal Processing Johannes Kepler University Linz Linz Austria

Ensemble methods are an interesting alternative to neural networks, especially due to their inherent parallelization capabilities. Forming ensembles from decision trees (e.g. in so-called random forests) has the additional advantage that processing a decision tree can be performed with low-complexity operations. When compared to other machine learning approaches such as (deep) neural networks, tree-based ensemble methods can deliver similar accuracy while being considerably simpler in terms of implementation effort and computational complexity. A major challenge for implementation in resource-constrained hardware such as FPGAs are the memory requirements of decision trees. While on-chip memories on current FPGAs are typically very fast, the available space is often not enough for complex problems, leading to a need to integrate external dynamic memories into the system architecture. In this paper, we propose a hardware architecture and an accompanying subtree-based memory layout that considers the inherent problems of decision trees and the memories’ physical latency constraints to speed up the processing of decision tree ensembles by increasing the bandwidth efficiency of memory transfers. We show that this can increase the processing speed by up to 3.38 times when compared to a naive (linear) memory layout for a single tree and 3.72 in a parallel scenario. This way it enables efficiently implementing tree-based ensemble learners for practical problem sizes on FPGAs.

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A machine learning Approach for Anomaly Detection in Power Mixing Equipment Intelligent Bearing Fault Diagnosis

A Machine Learning Approach for Anomaly Detection in Power M...

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Power Electronics, Computer Applications (ICPECA), IEEE international conference on

作者： Ce Fang Qian Wang Binbin Huang Zhejiang Canaan Technology Limited China International College Krirk University Bangkok Thailand Zhejiang Canaan Technology Limited Wenzhou China

Bearing and Gear are the essential and most significant pieces of pivoting machinery utilized in ventures. Normal applications incorporate planes, autos, power turbines, and steel plants. In the event of flaws happening in any bearing or gear, of these machines during working circumstances, serious outcomes are to happen. The issue determination utilizes the vibration signal at the beginning phase of shortcoming event. Vibration-based investigation is taken as a result of its simplicity of estimation. The viability of the astute shortcoming finding strategy is approved utilizing different datasets gathered from turning bearing and gear parts from an only made test rig in the research center through planned plans. For the robotized bearing shortcoming finding framework, three distinct and novel plans were figured out, and it is assessed. Also, for gear issue conclusion, another three unique and novel plans were modeled and assessed independently. In bearing issue determination, contain wavelet parcel change for highlight extraction, half and half hereditary calculation and particle swarm optimization for highlight choice and for coordinated ANFIS for navigation. In plot 2, Feature Extraction process did from the time and recurrence area of the gained signal, Hereditary Calculation and Particle Swarm optimization strategies with various wellness capabilities were applied. The outcomes from gear and bearing shortcoming analysis exhibit that the proposed smart shortcoming finding philosophy is exceptionally exact and successful in separating the issue examples of the two bearings and gears.

关键词： Vibrations Fault diagnosis Gears signal processing algorithms signal processing Feature extraction Turning

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Empirical Wavelet Transform Grounded Poignant Ground Target Recognition and Classification by Seismic signal processing 1

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3rd international conference on machine Intelligence and Smart Systems, MISS 2023

作者： Mittal, Aman Centre for Interdisciplinary Research in Business and Technology Chitkara University Punjab Rajpura140401 India

ISBN: (数字)9783031317231

ISBN: (纸本)9783031317224

Humans have had the ability to recognise items for hundreds of years, possibly even since they first appeared on Earth. Humans use their senses of sight, smell, hearing, taste, and touch to determine the identities of things in their immediate surroundings. Transmission of sensory organ impulses to the brain allows for the latter's processing and interpretation of the data. That's why repetition is so important for learning. The learned knowledge is put to use in a wide variety of ways, from the mundane to the crucial, such as in the areas of security, surveillance, traffic monitoring, etc. Due to the short range of the human senses and the potential dangers of working in some environments, this approach has spatial and temporal constraints. Seismic signal processing employs the wavelet transform for detection and classification. However, picking the mother wavelet is tricky because it depends on how well it fits the original signal. As a result, the empirical wavelet transform (EWT) is investigated in this research paper as a potential adaptive wavelet transform for detection and classification. In the same way as EWT does, the mother wavelet is chosen mechanically with reference to the input signal. This study shows that EWT-based detection and classification methods perform better than STFT-based methods. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Seismic response

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Multiple Feature Extraction Based on PPG signal to Realize Blood Pressure Measurement of Composite Multi-channel Convolutional Neural Network Model

Multiple Feature Extraction Based on PPG Signal to Realize B...

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Electronic Communications, Internet of Things and Big Data (ICEIB), international conference on

作者： Cheng-Gang Lu Lei Wang Department of Electrical Engineering Feng Chia University Taichung Taiwan

Photoplethysmography (PPG) changes according to arterial blood pressure (ABP). The change in the physiological signal reflects the change in the functional state of the heart and vascular system. To prevent cardiovascular disease due to hypertension, the PPG signal has been widely used for non-invasive continuous estimation of blood pressure. In recent years, artificial neural network-based algorithms have found the relationship between ABP and related physiological signals. Compared with traditional cuff measurement methods, it is more intuitive to understand the overall blood pressure trend and avoid invasive pressure Measured bleeding risk. Most artificial neural networks use an electrocardiogram (ECG) and PPG at the same time, and the ECG acquisition device needs electrodes and wires to the chest cavity, which indirectly affects the convenience of physiological signal acquisition. Using three feature extraction techniques with different detailed attributes for a single PPG measurement and substituting short-time Fourier transform (STFT), continuous wavelet transform (CWT), and Hilbert-Huang transform (HHT) into the "compound multi-channel convolutional neural network(CMCNN)" model, we evaluate the overall blood pressure value. Considering that the distorted data caused by motion artifacts causes the training results to fail to converge, we select Physionet's MIMIC-III database and use data purification-based processing steps. We screen out the artifact labels for PPG and ABP at first and then use the artifact labels to perform seven-level data segmentation. Finally, using STFT, CWT, and HHT, we obtain spectrogram, scalogram, and Hilber spectrum. The original waveform of PPG and the spectrum, the scalogram, and the Hilbert spectrum are used as input data for learning and analysis. Compare with the traditional machine learning method, the blood pressure estimation results is closer to the actual situation.

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A Technique Based on Ensemble machine learning for The Analysis of Electronic Nose signals

A Technique Based on Ensemble Machine Learning for The Analy...

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Advance Computing and Innovative Technologies in Engineering (ICACITE), international conference on

作者： Amit Taneja Santhosh Kumar Rajamani Deepika Shekhawat Yerrolla Chanti Kapil Joshi Ashish Garg AIT-CSE Chandigarh University Department of E.N.T MAEER MIT Pune’s MIMER Medical College and Dr BSTR Hospital TalegaonDabhade Pune Maharashtra India Department of Computer Science & Engineering Manipal University Jaipur Rajasthan School of Computer Science and Artificial Intelligent SR University Warangal Telangana India Uttaranchal Institute of Technology Uttaranchal University Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India

Numerous applications have been documented for electronic nose (e-nose) devices, which are valued for their speed, affordability, and lack of invasiveness. The e-nose scheme is a useful instrument for analyzing volatile chemicals as a distinct 'fingerprint,' especially in the meat industry. The pattern identification algorithm's capacity to decode e-nose signals is crucial to the e-nose system's effectiveness in a wide variety of uses. However, positive results using ensemble approaches have been reported in a number of data types. In this study, an ensemble learning strategy is proposed for e-nose signal analysis, particularly for evaluating beef quality. Not only are learning algorithms and sensor array optimization using ensemble approaches. Ensemble FSA is created by combining three filter-based feature selection algorithms (FSAs)—reliefF, chi-square, and Gini index—for sensor array optimization. When using a single FSA on a collection of homogenous e-nose data for beef quality monitoring, it is common for the results to be inconsistent or unexpected. In addition, multi-class regression and categorization problems are handled by using ensemble learning techniques. We use bagging and boosting algorithms, shown here by Random Forest (RF) and Adaboost. Both a support vector machine (SVM) and a decision tree are used as independent learners, and their results are compared to those of the ensemble. Experiments show that our ensemble method is effective and generalizable for e-nose signal processing. Optimized sensor combination using filter-based FSA consistently outperforms previous methods in both classification and regression. Additionally, despite using a reduced sensor complement, Adaboost as a boosting algorithm yields the highest quality prediction.

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Review on machine learning and its application in Atmospheric science and Human Behavior Recognition 2020

Review on Machine learning and its application in Atmospheri...

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3rd international conference on signal processing and machine learning, spml 2020

作者： Chen, Hongyu Jiang, Meiqiu Liu, Yixin He, Junlin Li, Haoru Nanjing University of Information Science and Technology China University of Electronic Science and Technology of China China Winchester High School United States Mcgill University Canada

ISBN: (纸本)9781450375733

machine learning is a very popular research field, almost every industry is using machine learning to improve the data analysis ability of the industry. In this paper, we mainly analyze the application of machine learning in behavior recognition, meteorology and other industries, and summarize the common methods and development status of machine learning in these two industries. © 2020 ACM.

关键词： machine learning

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Welcome Message from the ICCI2023 General Co-Chairs

Welcome Message from the ICCI2023 General Co-Chairs

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Cybernetics and Innovations (ICCI), international conference on

From the first meeting held in 2022 by IEEE SMC Thailand Chapter and Muban Chombueng Rajabhat University in Ratchaburi to the second meeting hosted in 2023 by Phetchaburi Rajabhat University, The international conference on Cybernetics and Innovations (ICCI 2023) was a conference from March, 2023. The conference will cover a board range of research topics in natural language processing, data analytic, machine learning, robotics, Internet of things, embedded systems, signal, image, speech processing and smart industrial *** ICCI2023 is the joint event of the 2th The international conference on Cybernetics and Innovations (ICCI 2023). It is hosted by Phetchaburi Rajabhat University (PBRU) and IEEE System, Man and Cybernetics Chapter (Thailand). together with the 3rd international Exhibition of Inventions of Thailand 2023, the icci 2023 is also held hybrid this year during March 29-31, *** behalf of the icci2023 committee, we would like to thank our keynote speakers, Assoc. Prof. Dr. Siridech Boonsang from Dean of the School of Information Technology King Mongkut's Institute of Technology Ladkrabang and Dr. Thittaporn Ganokratanaa University Lecturer at Applied Computer Science Programme, King Mongkut's University of Technology *** would also like to express our appreciation to Rachada Kongkachandra from Thammasat University, Mahasak Ketcham from King Mongkut's University of Technology North Bangkok. the program chair of ICCI2023 for their excellent arrangement of technical program. We acknowledge the co-hosts: Phetchaburi Rajabhat University(PBRU) and Artificial Intelligence Association of Thailand (AIAT, Thailand) and the supporters: IEEE Thailand Section, IEEE System, Man and Cybernetics Chapter (Thailand). Finally, we appreciate the presenters, the reviewers, the organizing team for their contribution. We look forward to seeing you soon at ICCI 2024 online during March , 2024.

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