The paper demonstrates the implementation of a pulse method for estimating the parameters of small-signal circuits of semiconductor devices at moderately high frequencies. The theoretical foundations of the method wer...
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ISBN:
(数字)9798331531836
ISBN:
(纸本)9798331531843
The paper demonstrates the implementation of a pulse method for estimating the parameters of small-signal circuits of semiconductor devices at moderately high frequencies. The theoretical foundations of the method were described, computermodeling was carried out. A mock-up has been assembled for a low-frequency bipolar transistor, the experimental study of which confirmed the theoretical analysis. The results of the work have been successfully implemented in the educational process in the form of laboratory work, which makes it possible to study the properties of a bipolar transistor.
Gas pipeline infrastructure is critical to energy distribution, but its operational existence is often threatened by anomalies such as leaks, malfunctions, and system disruptions. Early detection of these anomalies is...
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ISBN:
(数字)9798331513320
ISBN:
(纸本)9798331513337
Gas pipeline infrastructure is critical to energy distribution, but its operational existence is often threatened by anomalies such as leaks, malfunctions, and system disruptions. Early detection of these anomalies is critical to prevent safety hazards, operational inefficiencies, and economic losses. This paper proposes an anomaly detection framework using Classic Seasonal Decomposition and Level Shift Anomaly Detection methods for frequency selection and labeling processes, as well as LSTM and VAE-GAN algorithms for anomaly detection. The dataset consists of time-series sensor measurements, including pressure, temperature, energy flow rate, and gas composition. Classic Seasonal Decomposition isolates trend, seasonal, and residual components, enhancing periodic anomaly detection. Meanwhile, Level Shift Anomaly Detection identifies abrupt changes in data levels that signal critical events. LSTM leverages temporal dependencies to detect anomalies with high accuracy in data with strong sequential patterns, while VAE-GAN is effective in modeling complex data distributions and capturing anomalies in datasets with less obvious patterns. Experimental results demonstrate the effectiveness of the proposed approach, with model performance evaluated using F1-Score for each LSTM and VAE-GAN model. The proposed framework shows great potential for peering into real-time monitoring systems, improving operational efficiency, supporting data-driven decision making, and significantly reducing the risk of system failure.
In recent twenty years fiber optic sensors using Mandelstam Brillouin scattering effect have obtained a wide application in distributed measurements of temperature and deformation. Their application in engineering str...
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ISBN:
(数字)9798331511241
ISBN:
(纸本)9798331511258
In recent twenty years fiber optic sensors using Mandelstam Brillouin scattering effect have obtained a wide application in distributed measurements of temperature and deformation. Their application in engineering structures requires the analysis of functional parameters under conditions of noise interference onto backscatter signal spectrum. The research presents a mathematical model for measurement process based on stimulated scattering emphasizing on the measurement errors analysis. The formulas are presented for evaluation of mean quadratic errors in temperature and deformation measurements depending on noise-to-signal ratio and number of measurement cycles. In addition, methods to assess spatial resolution and dynamic range are proposed. Variations in methods for detection and processing backscattering spectra make it possible to produce efficient sensor systems which can monitor parameter change in real time.
Learning performance data, such as correct or incorrect responses to questions in Intelligent Tutoring systems (ITSs) is crucial for tracking and assessing the learners’ progress and mastery of knowledge. However, th...
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Mobile Ad-hoc Networks (MANETs) encounter challenges related to dynamic topologies, energy constraints, and link instability. To enhance performance, a hybrid model integrates Ad-hoc On-demand Multipath Distance Vecto...
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The coded Aperture Snapshot Spectral Imaging (CASSI) system has great advantages in dynamically acquiring Hyper-Spectral Image (HSI) compared to traditional measurement methods, but there are the following problems. 1...
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With the development of artificial intelligence technology, the cultivation of computer professionals faces new opportunities and challenges. Traditional training models can no longer meet the industry's demand fo...
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ISBN:
(数字)9798350389579
ISBN:
(纸本)9798350389586
With the development of artificial intelligence technology, the cultivation of computer professionals faces new opportunities and challenges. Traditional training models can no longer meet the industry's demand for talent, making innovation in training models urgent. This paper proposes an innovative training model driven by generative artificial intelligence (GAI), which encompasses facilitating student-centered teaching models, strengthening engineering practice ability, and enhancing innovation awareness. A case study on the rebar counting problem is analyzed to illustrate the application of this new approach. The research findings are summarized to provide beneficial references for computer major talent training in universities.
With the emergence of AI for good, there has been an increasing interest in building computer vision data-driven deep learning inclusive AI solutions. Sign language Recognition (SLR) has gained attention recently. It ...
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The proceedings contain 194 papers. The special focus in this conference is on Natural Language Processing and Chinese Computing. The topics include: Hierarchical Knowledge Aggregation for Personalized Response Genera...
ISBN:
(纸本)9789819794423
The proceedings contain 194 papers. The special focus in this conference is on Natural Language Processing and Chinese Computing. The topics include: Hierarchical Knowledge Aggregation for Personalized Response Generation in Dialogue systems;multi-hop Reading Comprehension Model Based on Abstract Meaning Representation and Multi-task Joint Learning;Leveraging Large Language Models for QA Dialogue Dataset Construction and Analysis in Public Services;MCFC: A Momentum-Driven Clicked Feature Compressed Pre-trained Language Model for Information Retrieval;integrating Syntax Tree and Graph Neural Network for Conversational Question Answering over Heterogeneous Sources;pqE: Zero-Shot Document Expansion for Dense Retrieval with Large Language Models;CKF: Conditional Knowledge Fusion Method for CommonSense Question Answering;MPPQA: Structure-Aware Extractive Multi-span Question Answering for Procedural Documents;GraphLLM: A General Framework for Multi-hop Question Answering over Knowledge Graphs Using Large Language Models;local or Global Optimization for Dialogue Discourse Parsing;structure and Behavior Dual-Graph Reasoning with Integrated Key-Clue Parsing for Multi-party Dialogue Reading Comprehension;enhancing Emotional Support Conversation with Cognitive Chain-of-Thought Reasoning;a Simple and Effective Span Interaction modeling Method for Enhancing Multiple Span Question Answering;FacGPT: An Effective and Efficient Method for Evaluating Knowledge-Based Visual Question Answering;PAPER: A Persona-Aware Chain-of-Thought Learning Framework for Personalized Dialogue Response Generation;towards Building a Robust Knowledge Intensive Question Answering Model with Large Language Models;model-Agnostic Knowledge Distillation Between Heterogeneous Models;exploring Multimodal Information Fusion in Spoken Off-Topic Degree Assessment;integrating Hierarchical Key Information and Semantic Difference Features for Long Text Matching;CausalAPM: Generalizable Literal Disentanglement for NLU
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