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Med-Chat: Tuning ChatGLM3-6B with Chinese Medical Dialogue 6

Med-Chat: Tuning ChatGLM3-6B with Chinese Medical Dialogue

引用

6th international conference on robotics, intelligent Control and Artificial Intelligence, RICAI 2024

作者： Chu, Jiqing Sun, Youqiang Huang, He Liu, Yuan School of Electronic and Information Engineering Anhui Jianzhu University Anhui Hefei China Hefei Institutes of Physical Science Chinese Academy of Sciences Hefei China Co. Ltd. Hefei China

ISBN: (纸本)9798331541699

Large language models (LLMs) have demonstrated significant success across a range of natural language processing tasks in general-purpose domains. However, due to limited specialized knowledge, LLMs sometimes generate responses that inaccurately represent medical facts, commonly referred to as hallucinations. Hallucinations provide possible hazards while using LLM in a medical context, including the possibility of misdiagnosis. To address this issue, we utilize the publicly available medical dialogue dataset DISC-Med-SFT, fine-tune the general model ChatGLM3-6B employing the LoRA method, and construct a dedicated medical knowledge base to provide a reliable source of information. Additionally, cascade deduplication is applied to the model’s generated responses to further enhance accuracy and consistency. Experimental results indicate that, compared to the baseline model ChatGLM3-6B, Med-Chat shows notable improvements in both BLEU and F1 score metrics. The resulting Chinese medical LLM, Med-Chat, demonstrates the capability to produce accurate, coherent responses and possesses fundamental medical question-answering skills. © 2024 ieee.

关键词： Natural language processing systems

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Adaptive Traffic signal Control System Using Deep Reinforcement Learning

Adaptive Traffic Signal Control System Using Deep Reinforcem...

引用

2024 ieee international conference on intelligent signal processing and Effective Communication Technologies, INSPECT 2024

作者： Agrawal, Satyam Sharma, Ritvij Srivastava, Pankaj Patel, Vinal Atal Bihari Vajpayee Indian Institute of Information Technology and Management Department of Information Technology Gwalior India Atal Bihari Vajpayee Indian Institute of Information Technology and Management Department of Engineering Sciences Gwalior India Atal Bihari Vajpayee Indian Institute of Information Technology and Management Department of Electrical and Electronics Engineering Gwalior India

ISBN: (纸本)9798350379525

Traffic congestion remains a major issue in urban areas, leading to environmental deterioration through increased noise and air pollution. Existing traffic management solutions often fall short in adapting to the dynamic nature of urban traffic patterns. Adaptive traffic control systems utilizing Reinforcement Learning (RL) offer a promising alternative by adjusting traffic signals based on real-time traffic conditions in each lane to alleviate congestion. In this paper, we propose an RL-based approach leveraging Deep Q-Networks (DQN) to minimize the cumulative waiting time of vehicles at intersections. Our method involves training a deep neural network to predict Q-values for various traffic signal actions, which are then used to optimize traffic light changes. The simulation environment is set up using SUMO (Simulation of Urban Mobility) for network and traffic generation, with TraCI (Traffic Control Interface) providing real-time traffic data. Our approach demonstrates significant improvements over the traditional methods. Additionally, we include visualizations of the simulation results to illustrate the effectiveness of our RL-based traffic control system. © 2024 ieee.

关键词： Traffic congestion

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Fault detection of induction machine using artificial neural networks 8

Fault detection of induction machine using artificial neural...

引用

8th ieee international conference on Image and signal processing and their Applications, ISPA 2024

作者： Bentrad, Moutaz Bellah Ghoggal, Adel Bahi, Tahar Bouziane, Seif Eddine LGEB Mohamed Khider University Electrical Department Annaba Algeria LGEB Mohamed Khider University Electrical Department Biskra Algeria LASA Badji Mokhtar University Electrical Department Annaba Algeria National School of Artificial Intelligence Intelligent Systems Engineering Department Algiers Algeria

ISBN: (纸本)9798350309249

The diagnosis and monitoring of industrial machinery faults is crucial to the Industrial Revolution but is frequently difficult and labor-intensive. Due to its quick computation, accurate prediction and robustness in performance, the use of artificial intelligence techniques has become a crucial component of condition monitoring of rotating machinery. This paper aims to use the artificial intelligence (neural network technique) to be able to detect and identify air gap eccentricity (static and mixed) and its influence on the stator current signature, the aim is to demonstrate the reliability and accuracy of these techniques to help us make the decision, The achieved simulation results allow distinguishing the types of eccentricity based on the Motor Current Signature Analysis technique data. © 2024 ieee.

关键词： Induction machine

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Deep Reinforcement Learning Based Strategy For Optimizing Phase Splits in Traffic signal Control 25

Deep Reinforcement Learning Based Strategy For Optimizing Ph...

引用

ieee 25th international conference on intelligent Transportation systems (ITSC)

作者： Yang, Huan Zhao, Han Wang, Yu Liu, Guoqiang Wang, Danwei Nanyang Technol Univ Sch Elect & Elect Engn 50 Nanyang Ave Singapore 639798 Singapore

ISBN: (数字)9781665468800

ISBN: (纸本)9781665468800

Urban traffic networks are often choked due to recurrent congestion. Heavy economic costs, environmental pollution and severe noise pollution can arise from the lack of valid Traffic signal Control (TSC) strategy. In this paper, assuming the cycle time of traffic lights and the phase order of a signal cycle are fixed, a Deep Reinforcement Learning (DRL) algorithm called Twin Delayed Deep Deterministic Policy Gradient (TD3) is first investigated to control the traffic signal by optimizing phase splits. Unlike widely used Deep Q-learning based TSC strategies, the action space of TD3 is continuous so that a determined phase split can be obtained at each time step. The Markov Decision Process is properly formulated by putting forward innovative representations of state, action, and reward. Several implemented experiments have well showcased the high control performance of the proposed TD3-based TSC strategy via a microscopic traffic simulation platform.

关键词： Pollution Q-learning Costs Roads Microscopy Green products Markov processes

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Robust Secure Beamforming for Multiuser Satellite-Terrestrial Communications via Active RIS 17

Robust Secure Beamforming for Multiuser Satellite-Terrestria...

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17th ieee international conference on signal processing, ICSP 2024

作者： Ge, Yimeng Liu, Haoyu Wan, Pengwu Liu, Chaowen Liu, Boyang Ren, Yuan The School of Communications and Information Engineering Xi'an University of Posts and Telecommunications Xi'an710121 China

ISBN: (纸本)9798350387384

This paper proposes a joint beamforming scheme that leverages the security and robustness of satellite-terrestrial communications, facilitated by an active reconfigurable intelligent surface (RIS). The aim is to improve the communication performance for terrestrial users in the secondary network without compromising the experience of the satellite users. Additionally, the study takes into account the effects of imperfect channel conditions, which is exacerbated by the amplifying capabilities of the active RIS. The primary objective is to minimize the transmission power from the BS, ensuring the secure transmission for the primary users and the communications quality for the cognitive user are both met. To address the non-convex nature of the cooperative beamforming problem, an effective alternating optimization strategy is devised. Ultimately, the simulation results highlight the active RIS's potential for conserving power in satellite-terrestrial communications. © 2024 ieee.

关键词： Satellite communication systems

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Road vehicle object detection based on improved YOLOv8 4

Road vehicle object detection based on improved YOLOv8

引用

4th international signal processing, Communications and Engineering Management conference, ISPCEM 2024

作者： Zhao, Zexi Chang’an University Xi’an China

ISBN: (数字)9798331528676

ISBN: (纸本)9798331528676

This study is dedicated to road vehicle object detection in intelligent transportation systems. By improving the YOLOv8 algorithm, dynamic snake convolution, EMA attention mechanism, and AlphaGIoU loss function are introduced to address the challenges faced by vehicle detection in complex road environments. In the study, data collected from multiple channels and the COCO public dataset were utilized to cover road target images under different lighting and climate conditions, and a diverse training sample set was constructed. By implementing strict preprocessing measures, the quality of the training data is ensured. The model training and validation adopt a comprehensive loss function to enhance the model's generalization ability and detection accuracy. For the YOLOv8 algorithm, we optimized the network structure and training strategy, particularly by enhancing feature extraction ability through dynamic snake convolution, improving key feature attention through EMA attention mechanism, and optimizing the balance between regression and classification loss through AlphaGIoU loss function. In the experimental verification phase, tests were conducted on image datasets containing multiple targets such as cars, buses, and pedestrians. After model training and testing, the improved YOLOv8 algorithm achieved a mAP value of 0.628 on the test set, verifying the system's vehicle detection ability in complex environments and providing effective technical support for the optimization of intelligent transportation systems. ©2024 ieee.

关键词： intelligent systems

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intelligent Software Bug Prediction: An Empirical Approach 3

Intelligent Software Bug Prediction: An Empirical Approach

引用

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

作者： Jahangir, Md. Ariyan Tajwar, Md. Abtahi Marma, Waythin Islam, Md. Siyamul American International Univeristy-Bangladesh Department of Computer Science Dhaka Bangladesh

ISBN: (纸本)9798350346435

Despite the fact that, much research has been conducted to improve accuracy in software bug prediction through different Machine Learning (ML) classifiers, not concentrated on the performance evaluation on the applicability of ML algorithms to detect software bugs. This inadequacy is focused on this paper. In this research, we conducted software bug prediction comparison on six different ML algorithms. Moreover, we adopted Code Based Metrics (CBM) to predict software defect through sequential neural network (DL) model and compared it with generic models. The performance of different models has been evaluated and compared based on PROMISE dataset provided by NASA. Results have shown that ML Techniques and DL Approaches have similar bug prediction capabilities where Decision Tree technique performing the worst and Support Vector Machine gave the best results. Also, thoughtful feature selection provides noticeable difference compared to no feature selection during model construction. © 2023 ieee.

关键词： Forecasting

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Artificial Neural Networks for the Forward Kinematics of a SCARA Manipulator: A Comparative Study with Two Datasets

Artificial Neural Networks for the Forward Kinematics of a S...

引用

2024 ASU international conference in Emerging Technologies for Sustainability and intelligent systems, ICETSIS 2024

作者： Bouzid, Rania Narayan, Jyotindra Gritli, Hassène Tunis Borj Cedria1164 Tunisia Indian Institute of Technology Guwahati Mechatronics and Robotics Laboratory Department of Mechanical Engineering Assam Guwahati781039 India Le Belvédère Tunis1002 Tunisia

ISBN: (纸本)9798350372229

In the field of robotics and automation, the precise determination of manipulator positions is a fundamental aspect with widespread applications. Despite the significance of analytical methods, solving the Forward Kinematics (FK) for Selective Compliance Assembly Robot Arm (SCARA) manipulator robots can be complex and computationally intensive. Our research introduces a method to address the FK problem for the four degrees of freedom (4-DoF) SCARA manipulator through Artificial Neural Networks (ANNs). We utilize two different generated datasets, one with a fixed step size and another one based on a sinusoidal signal, for training the ANNs. This choice of datasets allows us to assess the ANN s' generalization capabilities across a range of operating conditions. Thereafter, the study is planned to investigate the ANN's performance while employing three distinct training algorithms: Levenberg-Marquardt (LM), Bayesian Regularization (BR), and Scaled Conjugate Gradient (SCG). Through a comprehensive comparison of the performance of different ANN models, different training algorithms, and the two adopted generated datasets, this study provides valuable insights into selecting the most optimal training configurations for the SCARA robot's FK solutions. We employ the Mean Squared Error (MSE) and the error histogram as two performance metrics to assess the accuracy of various ANNs. The results reveal that optimal MSE outcomes were obtained when utilizing a balanced architecture with three hidden layers in the analysis of the sinusoidal-signal-based datasets. © 2024 ieee.

关键词： Artificial Neural Network Fixed-step- and sinusoidal-signal-based datasets Forward kinematics SCARA manipulator robot Training optimizers

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Adversarial attacks for speaker recognition system with FGSM-CNN and FGSM-DNN

Adversarial attacks for speaker recognition system with FGSM...

引用

2024 international conference on Telecommunications and intelligent systems, ICTIS 2024

作者： Abdelhakim, Mokdad Yacine Ahmed, Krobba Mohamed, Debyeche USTHB University Speech Communication and Signal Processing Laboratory Algiers Algeria

ISBN: (纸本)9798331527396

Adversarial attacks are specifically designed to interfere with models of speaker recognition systems by introducing imperceptible noise into speech signals. This paper explores deep learning-based adversarial attacks targeting speaker recognition systems. We used the x-vector framework and various variants of the FGSM technique applied to convolutional neural networks (CNNs) and deep neural networks (DNNs). We evaluated five attack methods: FGSM, BIM, FGSM-CNN and FGSM-DNN. Performance evaluations of the proposed methods were conducted using the LibriSpeech corpus, focusing on the equalised error rate (EER) measured by two metrics: PLDA and Cosine. The experimental results indicate that the proposed adversarial attack methods offer superior performance in terms of EER compared with the traditional FGSM approach. © 2024 ieee.

关键词： Adversarial machine learning

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Research and Design of eVTOL Garage Scheduling and Management systems for Urban Air Mobility 4

Research and Design of eVTOL Garage Scheduling and Managemen...

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4th international signal processing, Communications and Engineering Management conference, ISPCEM 2024

作者： Ge, Gaoyuan School of Transportation and Logistics Engineering Wuhan University of Technology Wuhan China

ISBN: (数字)9798331528676

ISBN: (纸本)9798331528676

As global urbanization accelerates, traffic congestion has emerged as a crucial issue affecting the sustainable development of cities. Urban Air Mobility (UAM) has been introduced as an effective solution to alleviate ground traffic congestion. Electric Vertical Takeoff and Landing (eVTOL) vehicles, known for their speed, flexibility, and sustainability, have garnered significant attention. However, the widespread implementation of eVTOLs faces technical challenges in efficient and safe scheduling and management. This paper investigates and designs an intelligent eVTOL garage scheduling and management system tailored for urban air traffic. Through system requirement analysis, a modular system architecture was proposed, and an intelligent scheduling algorithm was developed using mathematical modeling and ant colony optimization, enhancing the scheduling process. The results demonstrate that the established mathematical model accurately describes the complex constraints and objectives of eVTOL scheduling, and the proposed ant colony algorithm effectively resolves scheduling issues, improving task allocation and resource utilization efficiency. The system design meets the real-time data processing, high concurrency, and scalability requirements of urban eVTOL operations. This study provides a scientific solution for designing and managing eVTOL garage systems in urban air traffic, enhancing the accessibility and reliability of urban air transportation. ©2024 ieee.

关键词： Traffic congestion

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