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Speech Recognition for intelligent System in Service Robots: A Review 4

Speech Recognition for Intelligent System in Service Robots:...

引用

4th international conference on Electrical Engineering and Computer Science, ICECOS 2024

作者： Atika, Ratna Dwijayanti, Suci Yudho Suprapto, Bhakti Doctoral Program in Engineering Science Universitas Sriwijaya Department of Electrical Engineering Polytechnic of Sriwijaya Palembang Indonesia Faculty of Engineering Universitas Sriwijaya Department of Electrical Engineering Palembang Indonesia Faculty of Engineering Palembang Universitas Department of Electrical Engineering Sriwijaya Indonesia

ISBN: (纸本)9798350368253

Speech recognition and response system technology in service robot research continues to evolve alongside technical advances and the increasing demand for intelligent automation across industries. The development of machine learning, digital signal processing, and artificial neural networks has brought significant advancements to speech recognition systems. While speech recognition has been implemented in various systems, there is limited discussion on categorizing these systems by examining four aspects: speech recognition applications, datasets used, emotional speech recognition, and automatic speech recognition. In this study, a review of these four aspects is conducted, focusing on the algorithms and methods used, along with their advantages and disadvantages, particularly in the context of implementing them in service robots using the Indonesian language. This study involves a literature review of 100 research papers from the last five years. The review concludes that the use of the transformer method combined with other techniques and Indonesian language datasets is still very limited. Therefore, future research will focus on developing and implementing these methods in humanoid service robots. © 2024 ieee.

关键词： intelligent systems

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Application of the YOLOv5 Neural Network for Pattern Recognition on Radiographic Images of the Human Wrist Joint 10

Application of the YOLOv5 Neural Network for Pattern Recogni...

引用

10th international conference on signal processing and intelligent systems, ICSPIS 2024

作者： Krasnoperova, Alena S. Kamenskiy, Andrey V. Tomsk State University of Control Systems and Radioelectronics Laboratory of television automation Dept. of Television and Control Tomsk Russia

ISBN: (纸本)9798331532543

The possibility of using artificial intelligence in the task of recognizing objects of interest in medical radiographic images has been studied. To implement computer vision, the YOLOv5 neural network and the use of its functionality for pattern recognition on radiographic images of the human wrist joint were considered. YOLOv5 is one of the most popular neural network models due to a number of advantages and features: high learning speed, object detection accuracy, efficient use of hardware and support for various architectures. The use of an artificial neural network in medicine is a promising direction that allows one to obtain data on pathologies and anomalies on radiographs, improving the diagnostic process and providing quick results with a high percentage of reliability. To adapt the model for the task of recognizing objects of the wrist joint in radiographic images, a data set was labeled, including images for retraining the neural network model and test images. Learning key numerical properties is key to building and tuning effective neural network models. The correctness of their selection and debugging have a direct impact on achieving the desired results when retraining and using neural networks. According to the results of the neural network, all classes of objects were correctly identified in more than 70% of the images;this indicator is a worthy result of the work, which confirms the relevance of using a computer vision tool in medical diagnostics and practical application in clinical practice. © 2024 ieee.

关键词： Diagnostic radiography

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Design of a single-phase inverter control strategy with improved BP neural network 8

Design of a single-phase inverter control strategy with impr...

引用

8th international conference on intelligent Computing and signal processing, ICSP 2023

作者： Shen, Mingnan Zhang, Fan Zhu, Wenzhang Shen, Yafeng Ma, Luyao Huang, Ke Xiamen University of Technology School of Opto-Electronic and Communication Engineering Xiamen China Xiamen Hualian Electronics Co. Ltd Xiamen China Xiamen University of Technology Xiamen China

ISBN: (纸本)9798350302455

PID control is an excellent control method and one of the first inverter control strategies to emerge. Its robustness, precise control and simple algorithm logic are often used in industrial production technology to control systems with precise mathematical models, but its use in inverter control systems is often due to the influence of the complex external environment and access to power electronics, resulting in a system that cannot be represented internally by a simple linear relationship. The PID control parameters need to be debugged empirically and cannot be precisely modified according to the real time situation, so BP neural networks are introduced to assist the process, using their ability to deal with non-linear problems to compensate for the lack of accuracy of PID control. The results of the simulation show that the improved BP neural network with the introduction of compactness and scheduling processing of the particle swarm algorithm will result in the inverter outputting a voltage and current waveform with less steady-state error than the original control strategy, and the final effect of grid-connected current tracking will be better. © 2023 ieee.

关键词： Three term control systems

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Code Generation Based on Quality-Aware Reordering 9

Code Generation Based on Quality-Aware Reordering

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9th international conference on intelligent Computing and signal processing, ICSP 2024

作者： Song, Yuan Yu, Luo College of Computer Science and Technology Wuhan University of Science and Technology Wuhan430065 China Hubei Provincial Key Laboratory of Intelligent Information Processing and Real-time Industrial Systems Wuhan430065 China

ISBN: (纸本)9798350376548

Translation efforts from natural language to code generation have achieved great success with large pre-trained language models. However, selecting the correct code output for the problem posed by the generated set of candidate codes is still challenging. In this paper, we address this problem by proposing an outcome-based quality-aware reordering method for code selection. This method scores each code by whether the set of candidate codes passes the test cases or not as a reference to select the top k best code outputs. In addition, this method depends on the quality and number of test cases. However, it is impractical to manually create a large number of test cases for each problem, so this paper adopts an automated approach that utilizes the current model to generate test cases for each problem. The experimental results show that the approach proposed in this paper obtains a significant improvement in the pass@k benchmark for each model, and the correctness and quality of test cases generated automatically using the model are discussed experimentally. Summarizing the experimental results and discussions, the result-based quality-aware reordering method proposed in this paper achieves a significant improvement in solving the problem of selecting the correct output from natural language to code generation, effectively improves the accuracy of evaluation by automating the generation of test cases, and provides an effective solution to improving the correctness of code generation. © 2024 ieee.

关键词： code generation natural language processing (NLP) pre-trained large models quality estimation reranking test case generation

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Unified Graph and Hypergraph Neural Network for Next-item Recommendation

Unified Graph and Hypergraph Neural Network for Next-item Re...

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2025 ieee international conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Zhang, Xinyu Jiang, Qize Zhang, Hanyuan Sun, Weiwei School of Computer Science Shanghai Key Laboratory of Data Science Fudan University Shanghai China Shanghai Institute of Intelligent Electronics & Systems Shanghai China

ISBN: (纸本)9798350368741

The task of next-item recommendation is a crucial component in recommendation systems. The challenge of this task lies in extracting complex interaction information from users' historical interactions with items. While prior research has transformed users' interaction histories into graphs and hypergraphs to mine high-order interactions, the integration of both remains uncharted. Existing methods treat graphs and hypergraphs separately in representation learning, missing out on their shared attributes. Addressing this gap, we introduce a novel unified framework that integrates graphs and hypergraphs into one unified message passing paradigm. Our method employs graph neural networks and hypergraph neural networks to continuously conduct representation learning, achieving end-to-end next item recommendation. Specifically, our framework consists of the following components: 1) a unique method for graph and hypergraph construction from interaction histories, with items as nodes and users as hyperedges;2) a novel message passing framework compatible with both graph and hypergraph neural networks;and 3) an innovative hypergraph neural network aggregation module enhanced with time and position encoders. The test results on multiple public benchmarks verify that our method outperforms current best practices in performance. © 2025 ieee.

关键词： Graph Neural Network Hypergraph Neural Network Next-item Recommendation User Modeling

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Dynamic Vehicle Graph Interaction for Trajectory Prediction Based on Video signals 48

Dynamic Vehicle Graph Interaction for Trajectory Prediction ...

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48th ieee international conference on Acoustics, Speech and signal processing, ICASSP 2023

作者： Chen, Jian Wang, Wei Chen, Junxin Cai, Ming Shenzhen MSU-BIT University Department of Engineering Shenzhen518172 China Sun Yat-Sen University School of Intelligent Systems Engineering Shenzhen518107 China Dalian University of Technology School of Software Dalian116621 China

ISBN: (纸本)9781728163277

The roadside video surveillance signal can help people achieve vehicle tracking and trajectory generation. Using these trajectories can learn the future motion of vehicles. Existing prediction methods can not analyze the interaction between vehicles well. To this end, we design a dynamic vehicle graph to represent the dynamic interaction between vehicles for trajectory prediction. A graph convolution module with an attention mechanism is used to extract the feature of the dynamic vehicle graph. Experiments comparing with baseline methods are conducted on a real-world dataset to demonstrate our model's improvement in forecast accuracy. © 2023 ieee.

关键词： Attention Mechanism Trajectory Prediction Vehicle Interaction Graph Video signal processing

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IOT Devices and Control systems Working Together to Improve Security 4

IOT Devices and Control Systems Working Together to Improve ...

引用

4th ieee international conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Singh, Rupinder Bajaj, Jaspreet Singh Bawa, Simerjeet Singh Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Chitkara University Chitkara Business School Punjab India

ISBN: (纸本)9798331518981

The Internet of Things (IoT) facilitates seamless integration and interoperability among various networks, devices, technologies, and products by placing emphasis on control schemas. End-users require uninterrupted and precise connectivity, irrespective of the integration node (e.g., tablet, laptop, television, smartphone, or other internet-connected device). intelligent IoT devices have the capability to function as flexible connections with ever-changing configurations, enabling timely reactions and potentially worldwide *** response to the urgent requirement for secure real-time control operations, this research paper introduces a methodology that integrates intelligent Internet of Things (IoT) system functionalities with power system interfaces. The proposed methodology integrates computing, cryptography, signal/image processing, and communication networks to facilitate authentication and authorization operations via gateways and terminals. This results in a real-time performance environment that is not only more secure but also more adaptable and robust in comparison to conventional approaches. Moreover, the researchers delineate a framework for the Internet of Things that is applicable to power system inputs in administrative, industrial, transportation, and comprehensive monitoring environments. © 2024 ieee.

关键词： Authentication

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CSA-SVM method for internal cavitation defects detection and its application of district heating pipes

CSA-SVM method for internal cavitation defects detection and...

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ieee/RSJ international conference on intelligent Robots and systems (IROS)

作者： Chen, Yanran Ma, Shugen Li, Longchuan Li, Zhiqing Yang, Yulin Beijing Univ Chem Technol Coll Informat Sci & Technol Beijing Peoples R China Ritsumeikan Univ Grad Sch Sci & Engn 1-1-1 Nojihigashi Shiga 5258577 Japan Beijing Univ Chem Technol Coll Informat Sci & Technol Beijing Peoples R China Ritsumeikan Univ Grad Sch Sci & Engn 1-1-1 Nojihigashi Shiga 5258577 Japan

ISBN: (数字)9781665479271

ISBN: (纸本)9781665479271

The goal of this paper is to develop an ultrasonic detection device that can be mounted on an underwater snake vehicle (USV) for underwater district heating pipe (DHP) detection in the future. Ultrasonic detection technology (UDT) is the detection means used, and the cavitation defects in polyurethane (PUR) layer of DHPs are the object being detected. Due to the large thickness of PUR layer and the complex interface information of multi-layer structure, detecting defects of DHPs quantitatively is a difficult task. To address this issue, this paper proposes an approach that combines feature extraction and crow search algorithm (CSA) optimized support vector machine (SVM). Firstly, the main parameters and detection method of UDT are designed after investigation. Secondly, defective signals are pre-processed by signal processing to extract the features form three domains. Finally, four different classifiers are used to identify cavitation defects based on the feature-set. When compared among optimized random forest (RF), k-nearest neighbor (KNN), and ordinary SVM, the experimental results show that CSA-SVM had the highest accuracy in defect size prediction, and the validation-experiment verifies the practicability and feasibility of the CSA-SVM classifier. All experiments illustrate that the issue could be well solved by our method.

关键词： Support vector machines Heating systems Resistance Sensitivity signal processing algorithms signal processing Feature extraction

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Design of a compact Microstrip Bandpass Filter for 2.4 GHz Applications

Design of a compact Microstrip Bandpass Filter for 2.4 GHz A...

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2024 international conference on Circuit, systems and Communication, ICCSC 2024

作者： Khardioui, Youssef El Alami, Ali El Ghzaoui, Mohammed Errachidia Moulay Ismail University Intelligent Renewable Energies Information Processing and Transmission Laboratory Faculty of Sciences and Technics Meknes Morocco Sidi Mohamed Ben Abdellah University Faculty of Sciences Fez Morocco

ISBN: (纸本)9798350365306

Microstrip bandpass filters are the core part of wireless communications. They help refine the signal and ensure that only the desired signal reaches the receiver. Microstrip bandpass filters are the core part of wireless communications. In this paper, a compact bandpass filter is designed using two rectangular open-loop resonators. The rectangular shape is used to achieve compact size and wide bandwidth performance. The substrate used is Rogers 6010 substrate with a thickness of 1.27 mm, a relative permittivity of 10.2, and an overall filter size of 11.55 × 6.62 mm. The resulting passband has a center frequency of 2.40 GHz, a bandwidth of 1.55 GHz, a fractional bandwidth (FBW) of 64.5%, a return loss of approximately -26.3 dB, and an insertion loss of -0.1 dB. The proposed filter was simulated and performance evaluated using HFSS (High Frequency Simulation Software). The resulting performance of the proposed filter makes it very desirable for Bluetooth and Wi-Fi applications (ieee 802.11n). © 2024 ieee.

关键词： Bandwidth

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A Simulation of a Robot Operating System Based Autonomous Wheelchair with Web Based HMI Using Rosbridge 3

A Simulation of a Robot Operating System Based Autonomous Wh...

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3rd international conference on robotics, Electrical and signal processing Techniques, ICREST 2023

作者： Islam, Md. Touhidul Hameem, Imtiaz Reza Saha, Shuvra Chowdhury, Mohammad Jamilur Reza Deowan, Md. Ether American International University Bangladesh Dept. of Electrical and Electronics Engineering Dhaka Bangladesh

ISBN: (纸本)9798350346435

Our society is aging, which will result in a dramatic increase in the number of individuals suffering from various types of physical impairment and disability. It is necessary to provide mobility support to these persons so that they can live independently at home and be fully integrated into society. This work aims to show a simulation of a ROS (Robot Operating System)-based autonomous wheelchair, as well as a cloud-based online platform for controlling and tracking the wheelchair's mobility and the patient's location. Which allows the user to remotely control the wheelchair from any Internet-enabled device and location. With an intuitive human-machine interface, this wheelchair may be useful for disabled and elderly individuals. ROS offers a simple and user-friendly architecture that enables the development of complex systems at a cheaper cost. Several simulation results are shown in this paper to demonstrate the practicability and performance of the designed system. © 2023 ieee.

关键词： Robot Operating System

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