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Optimal investment and retirement strategies for defined-contribution pension with jump diffusion risk process 2

Optimal investment and retirement strategies for defined-con...

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2nd Asia conference on Advanced robotics, Automation, and Control Engineering, ARACE 2023

作者： Luan, Huifang Gao, Jinggui Shandong University of Science and Technology College of Mathematics and Systems Science Shandong Qingdao China

ISBN: (纸本)9798350328363

An optimal investment strategy and optimal delay retirement time of defined-contribution pension which want to achieve the expected utility maximization problem of the terminal wealth were studied in this paper. Firstly, the stochastic differential equation with jump-diffusion is constructed where the terminal time is free, to describe the corresponding asset accumulation process under unknown retirement time. Then, a stochastic optimal control problem with jump-diffusion risk process is proposed, where the delay retirement time and the investment strategy are control parameter and control function, respectively. In order to obtain the optimal solutions of above optimization model with Cobb-Douglas utility function, the stochastic optimal control problem with free terminal time is transformed to that with determined terminal time by time-scaling transformation. After that, based on stochastic control theory, a Hamilton-Jacobi-Bellman equation for stochastic optimal control problem with terminal time was derived. The optimal investment strategy is obtained by hypothesis method. Finally, sensitivity analysis and numerical simulation are performed. © 2023 IEEE.

关键词： Sensitivity analysis

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Continuous Wave Mud Pulse Generator Control Using Improved Sliding Mode Reaching Law 2

Continuous Wave Mud Pulse Generator Control Using Improved S...

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2nd international conference on Laser, Optics and Optoelectronic technology, LOPET 2022

作者： Jin, Bo-Pi Wu, Shen-Hua Gong, Hai-Li Yang, Sheng-Xuan State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China

ISBN: (纸本)9781510657588

Conventional PID control is widely used in the permanent magnet synchronous motor (PMSM) speed control system because of it is simple to design, but it is difficult to meet the high precision control requirement for controlling the continuous wave mud pulse generator used in the circumstance of downhole. Therefore, an improved exponential reaching law based on the conventional sliding mode control (SMC) is proposed to achieve the purpose of tracking the velocity step signal quickly and accurately. In this law, fractional polynomials containing exponential terms in the coefficients of constant velocity terms is introduced. Simultaneously, to attenuate the influence of chattering phenomenon, saturation function is used to instead of switching function and integral-type sliding mode surface is applied to improve the convergence speed. The simulations show that the proposed improved sliding mode reaching law can effectively improve the performance of control and robustness compared with conventional PID control. © 2022 SPIE.

关键词： Three term control systems

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Machine Learning Techniques for Parkinson's Disease Detection 2

Machine Learning Techniques for Parkinson's Disease Detectio...

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2nd international conference on Smart Technologies, Communication and robotics, STCR 2022

作者： Sanjay, V. Swarnalatha, P. Vellore Institute of Technology School of Computer Science and Engineering Vellore India

ISBN: (纸本)9781665460477

A neurological disease is Parkinson's disease. It causes trembling in the hands, trouble walking, losing balance, and coordination. In the high-level stage, there is no access to medical care. Blood test reports, CT scan results, and X-ray reports are not accessible early enough. Early Parkinson's disease detection is crucial to implement effective treatment. The purpose of the proposed effort was to identify Parkinson's disease in early prediction using clinical imaging and machine learning technologies. Despite the fact that there are numerous methods for detecting Parkinson's disease, using MRI scan images still it is a big challenge. In this study, an Adaboost classifier is used with a hybrid PSO algorithm to propose a novel technique for detecting Parkinson's disease. Adaboost acted as the best classifier among other classifiers. Initially, MRI image best features are extracted and identified by the curvelet transform and principal component analysis. This Ad boost classifier receives optimal features as input. Finally, Adaboost classifieds the MRI images and gave excellent classification accuracy. To evaluate the proposed method three methods metrics namely accuracy, specificity, and sensitivity are used. Based on the results the proposed methods yield greater accuracy than the existing systems. © 2022 IEEE.

关键词： Adaptive boosting

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Advanced ML Techniques for Real-Time Air Quality Prediction in Megacities: A Comparative Study 2

Advanced ML Techniques for Real-Time Air Quality Prediction ...

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2nd international conference on Automation, Computing and Renewable systems, ICACRS 2023

作者： Vignesh, U. Elakya, R. Selvi, R. Thanga Shanthana, S. School of Computer Science and Engineering Vellore Institute of Technology VIT- Kelambakkam - Vandalur Road Tamil Nadu Chennai India Information Technology Sri Venkateswara College of Engineering Sriperumbudur Chennai India Computer Science and Engineering Vel Tech Rangarajan Dr Sagunthala R & D Institute of Science and Technology Avadi Chennai India Rajalakshmi Institute of Technology Chennai India

ISBN: (纸本)9798350340235

It is impossible to imagine humans surviving without oxygen. Due to the recent developments in almost all aspects of civilization, air quality has continually decreased. The day-to-day industrial, transportation, and home activities produce harmful pollutants into our surroundings. Monitori ng and forecasting air quality have become crucial in the modern era, particularly in the developing countries like India. In comparison to more traditional methodologies, new prediction technologies based on machine learning have emerged as the most useful tools for analyzing those unique threats. The current study analyses air quality data from 23 Indian cities over a period of six years in order to make air quality projections. Correlation analysis is used to identify the most significant aspects of the dataset and to complete the preprocessing. Pollutants with a direct impact on the air quality index are identified using exploratory data analysis, which is used to identify hidden patterns in the dataset. Due to pandemic situation, almost all pollutants were greatly reduced in the year 2020. After addressing the data imbalance issue with a resampling method, five machine learning models are employed to predict air quality. There are comparisons between these models and the gold standard metrics. The Gaussi an Naive Bayes model has the highest accuracy, while the S upport Vector Machine model has the lowest. When compared to other existing models, XGBoost has the maximum linearity between the predicted and actual data. © 2023 IEEE.

关键词： Air quality prediction comparative analysis Environmental data Machine learning Real- time processing Regression algorithms Sensor data Time-series data

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Wearable Fabric Tactile Sensors for Robotic Elderly Assistance 2

Wearable Fabric Tactile Sensors for Robotic Elderly Assistan...

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2nd international conference on Smart Technologies, Communication and robotics, STCR 2022

作者： Mary Catherine, V.G. Paul, Binu Vinoj, P.G. Cochin University of Science and Technology Division of Electronics Cochin India SCMS School of Engineering and Technology Department of ECE Karukutty India

ISBN: (纸本)9781665460477

The demand for Robots in Elderly assistance is increasing due to the lack of human caregivers. In the context of Robot coexisting with the human beings in a home environment, for the safe and friendly interaction it is essential to endow the sense of touch through Tactile sensor systems. This paper proposes a novel scalable approach for tactile sensors based on low cost wearable conductive fabric. Fabric tactile sensor (FABTAC) is conformable with the robot body and can be used as a tactile sensing skin that perceives touch and force applied at the contact location. FABTAC sensors are developed as an array of touch sensors sewed on the cloth substrate with the stainless-steel conductive thread. The thermistor sensors are also sewed to fabric to perceive the temperature information. The FABTAC sensors are integrated on to the custom-made 3D printed Robotic hand and the tactile data is processed with a novel wearable electronic FLORA microcontroller platform. The acquired data can be used to provide a real time tactile feedback for performing assistive tasks like grasping objects of diverse profiles, avoiding slippage. The FABTAC sensors has the advantage of utilizing flexible, light weight sensors with good spatial and temporal resolution. Thus, the system can potentially aid the automation of daily life activities of the Elderly thereby enhancing the quality of their life. © 2022 IEEE.

关键词： Wearable sensors

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A Practical Multi-camera SLAM System for Large Mobile Robots 2

A Practical Multi-camera SLAM System for Large Mobile Robots

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2nd international conference on Big Data, Artificial Intelligence and Risk Management, ICBAR 2022

作者： Dong, Yunlong Ding, Hong Zha, Fusheng Li, Mantian State Key Laboratory of Robotics and Systems Harbin Institute of Technology Harbin150080 China

ISBN: (纸本)9798350334265

It is very important to provide sufficient visual perception for large mobile robots to improve their security and intelligence. Considering the narrow field of vision and insufficient perception of a single camera, this paper proposes a practical multi-camera SLAM system. According to the hardware structure of the large mobile robot, the layout scheme of the multi-camera is designed and its field of view is analyzed. To make full use of the advantages of multi-camera data fusion, a multi-camera localization algorithm based on the main localization module and sub-localization module is proposed. In order to build a dense 3D point cloud map, a novel depth estimation method is present, which first uses the depth estimation method based on camera geometry to obtain a preliminary depth map, and then uses a convolutional neural network to refine it. Comprehensive experiments prove that our multi-camera SLAM system achieves appealing results and has strong practicability. © 2022 IEEE.

关键词： Mobile robots

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Stimuli based Paralyzed Patient Monitor 2

Stimuli based Paralyzed Patient Monitor

引用

2nd international conference on Smart Technologies, Communication and robotics, STCR 2022

作者： Sakthivel, B. Sneha, R. Vijayalakshmi, M. Vidhya, B. Harshene, H. Dr. N. G. P. Institute of Technology Department of Biomedical Engineering Coimbatore India

ISBN: (纸本)9781665460477

The issues in the peripheral nervous system and central nervous systems leads to paralysis. Motor neurons send electrical signals to muscles, causing them to contract. Nerves that are not harmed provide information to muscles. Muscles move as a result of these impulses. The inability to perform these voluntary motions is known as paralysis. Strokes, spinal cord injuries, and multiple sclerosis like pain, fatigue are all common causes of paralysis. Physical therapy (using muscle stimulators), occupational therapy and nerve transfer surgery are all viable therapies for paralysis. Neuromuscular electrical stimulation is a technique for causing paretic muscles to contract using an electrical current that can efficiently produce muscle contractions which are used on paralyzed patients. Electromyograms are used to evaluate the electrical activity in response to nerve stimulation of the muscle. EMG data can be used to examine the disorders of nerve, muscles and communication between them. The proposed system uses an EMG in conjunction with a muscle stimulator to detect the muscle movement of the patient through which the physiotherapist will be able to treat the muscle with proper intensity and stimulate it better. This will help to increase the recovery rate of the patient. © 2022 IEEE.

关键词： Muscle

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Test-Retest Reliability of a Football Performance Tracking System in Evaluating Players’ Match-Related Load Variables 2nd

Test-Retest Reliability of a Football Performance Tracking S...

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2nd international conference on Intelligent Manufacturing and robotics, ICIMR 2024

作者： Rasid, Aina Munirah Ab Musa, Rabiu Muazu Majeed, Anwar P. P. Abdul Abdullah, Mohamad Razali Raj, Naresh Bhaskar Mohamad, Zulkifli Centre for Fundamental and Continuing Education Universiti Malaysia Terengganu Terengganu Kuala Nerus21030 Malaysia Sunway University No 5 Jalan Universiti Selangor Darul Ehsan Bandar Sunway47500 Malaysia Faculty of Health Science Universiti Sultan Zainal Abidin Terengganu Kuala Nerus21300 Malaysia Innovative Sports Engineering and Artificial Intelligence Centre College of Engineering MARA University of Technology Selangor Shah Alam40450 Malaysia

ISBN: (纸本)9789819639489

Advancements in the sports industry have led to the extensive use of the Global Positioning System (GPS) for monitoring athletes in football. Ensuring the reliability of these systems is crucial for providing coaches with accurate data to improve team performance and individual capabilities. However, the reliability of such systems often remains unexamined and unreported. This study aims to evaluate the reliability of the Sports Performance Tracking (SPT) device in assessing football players’ performance during matches. A professional football player (30 years old, 174 cm tall, 60 kg) from an elite club was selected, having participated in all matches for equal periods. The player wore the SPT device during the games, and data from the first game were compared with those from nine subsequent games to assess test-retest reliability. Statistical analyses were conducted, including the Kolmogorov-Smirnov test, intraclass correlation coefficients (ICC), Cronbach’s alpha, and correlation coefficient. The device demonstrated high reliability across all games. These findings offer valuable insights for evaluating other commercial tracking systems before data collection. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Global positioning system

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Multimodal Biometric Recognition Neural Networks with Branch Attention Mechanisms 2

Multimodal Biometric Recognition Neural Networks with Branch...

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2nd Asia conference on Advanced robotics, Automation, and Control Engineering, ARACE 2023

作者： Liujun Mingjin, Chen Guoyong Zengxi, Huang Xihua University College of Computer and Software Engineering Chengdu China Sichuan Police College Criminal Science and Technology Department Chengdu China

ISBN: (纸本)9798350328363

It is generally known that compared to unimodal systems, the multi-modal biometric systems can improve recognition accuracy by utilizing the complementary features of multiple biometric. Nevertheless, the modalities used may not always play equally important roles in multi-modal recognition. Especially, in practical application scenarios, it often occurs that some of the biometric samples are contaminated by noise and corruption. In this context, the multi-modal recognition systems should pay more attention to the high-quality modality than the ones with low-quality samples, so as to maintain high accuracy. In this paper, we proposes a multi-branch deep learning network for multi-modal recognition, each branch using one biometric sample as input. To highlight the more important biometric modalities, we propose to embed a branch attention module between branches before multi-modal feature fusion. The branch attention module includes fuse, select, and calibrate steps, which can dynamically calculate the weight of each branch based on its importance. The proposed multi-modal recognition model is tested on a chimeric bimodal dataset with the face and the ear modalities. Our proposed method still achieves 92.76% of rank-1 on the most contaminated dataset;the rest of the experimental results are shown in the Experiment and Performance Analysis section. Our experimental results clearly demonstrate that the proposed branch attention method can improve the multi-modal recognition performance. © 2023 IEEE.

关键词： Biometrics

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A Smart Model to Manage Traffic Using Infrared Sensors and Bell Detection System: A Computer Controlled Traffic System 2

A Smart Model to Manage Traffic Using Infrared Sensors and B...

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2nd IEEE international conference on Communication, Security and Artificial Intelligence, ICCSAI 2023

作者： Saxena, Ashendra Kumar Adlin, Jebakumari S. College of Computing Science and Information Technology Teerthanker Mahaveer University Uttar Pradesh Moradabad India University Karnataka Bangalore India

ISBN: (纸本)9798350369960

The escalating challenge of traffic congestion in urban areas necessitates innovative solutions at the intersection of computer science and artificial intelligence. The main problem is in the outdated nature of conventional traffic signal systems, which function based on predetermined time intervals and fail to appropriately adjust to the ever-changing patterns of urban traffic. This study presents a sophisticated traffic management system that utilizes machine learning and data mining concepts to optimize real-time traffic signal regulation. The suggested system utilizes infrared sensors to constantly monitor the level of traffic congestion at crossings. The data is transmitted to an advanced microcontroller, which utilizes machine learning techniques to forecast traffic patterns and subsequently modify the timing of traffic signals. This approach enables a more seamless and adaptable control of traffic flows, resulting in a substantial decrease in congestion and enhancement of overall traffic efficiency compared to traditional systems. By incorporating computer vision techniques, the system's ability to detect and evaluate several traffic parameters, such as vehicle count, speed, and categorization, is improved. The data is essential for the machine learning models to make well-informed recommendations regarding the durations of traffic lights. Furthermore, the system integrates robotics components to physically manipulate and operate traffic light hardware, guaranteeing smooth functioning and immediate response to the computational decisions made by the microcontroller. This research not only addresses the immediate practical concerns of urban traffic management but also contributes to the broader field of AI and robotics in realworld applications. The synergy of data mining, machine learning, and computer vision within a robust traffic management system exemplifies the transformative potential of AI technologies in urban infrastructure management. © 2023 IEEE.

关键词： Audio Signal Detection Machine Learning Algorithms Real-Time Data Processing Traffic Optimization Techniques

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