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Machine Learning, Cloud Computing and intelligent Mining (MLCCIM), International Conference on

作者： Wei Gao Jin Liu Jiamao Zhi Junxiang Wang College of Information Engineering Shanghai Maritime University Shanghai China China Shipping Telecommunication Co. Ltd. Shanghai Ship and Shipping Research Institute Shanghai China National Engineering Research Center of Ship Transportation Control Systems COSCO SHIPPING TECHNOLOGY Co. Ltd Shanghai China

In comparison to other regions, the traffic dynamics observed in port waters are more unclear. Given the complexity of maneuvering in multi-vessel encounter scenarios, accurate prediction of vessel trajectories in port waters is crucial for preventing accidents and improving port operations. To address the highly dynamic navigational environment and the uncertainty of navigational patterns such as heading and path selection, we propose an improved SocialV Ae(Variational Autoencoder) method. The approach use local ship observations to comprehend traffic patterns between ships in order to make ship navigation decisions. Furthermore, we present a socially aware regression module that evaluates the compatibility between past trajectory interaction codes and forecast trajectories, and then uses the resulting offsets to adjust the predicted trajectories. Additionally, for the problem of non-random missing data in port waters AIS (Automatic Identification System) data, we use a shift-corrected data interpolation method to fill in the missing values. We utilize two real AIS datasets of waterways surrounding ports to test the efficacy of our method: Copenhagen Port and Shanghai Yangshan Port. When compared to existing methods, our Improved SocialV Aedelivers excellent prediction performance on these two datasets.

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Non-stationarity Removal Techniques in MEG Data: A Review

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Procedia Computer Science 2022年 215卷 824-833页

作者： Beril Susan Philip Girijesh Prasad D Jude Hemanth Department of Electronics and Communication Engineering Karunya institute of Technology and Sciences Coimbatore 641114 India Intelligent Systems Research Centre Ulster University Magee Campus Derry Londonderry Northern Ireland U.K

Brain Computer Interface (BCI) enables communication solely through mental activity. For patients who have lost complete voluntary muscle control, it serves as a potential communication and rehabilitation channel. In a number of recent investigations, BCl based on electroencephalography (EEG) has demonstrated increased reaction in nonresponsive people to interact with others. Despite the method's effectiveness, patient interaction takes way too long. However, magnetoencephalography (MEG) may shorten the training period, improving BCI reliability in the process. One of the major technological difficulties confronting MEG data collection and interpretation is that the strength of neuromagnetic fields recorded externally is considerably lower than interfering signals. There hasn't been much substantial progress on an effective MEG-BCI system because there is no large enough MEG dataset. Despite their huge potential, MEG-based BCI systems still need a lot of work in terms of signal processing algorithms that are both reliable and efficient. Unexpected head movements and changing orientation between sessions may be the cause of non-stationarity in the MEG data that was captured, changing the most effective channel selection between sessions and participants. Not only the head movement, but also non-stationarity in data can be caused by a variety of factors like user weariness, mood changes, or external noise interfering with the MEG system. This paper discusses the many types of data acquisition artifacts and review the techniques to minimize the artifacts to increase the signal-to-noise ratio.

关键词： Brain-computer interface Magnetoencephalography Data acquisition Artifacts

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Enhancing Mild Cognitive Impairment Detection through Efficient Magnetic Resonance Image Analysis

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Computers, Materials & Continua 2024年第8期80卷 2081-2098页

作者： Atif Mehmood Zhonglong Zheng Rizwan Khan Ahmad Al Smadi Farah Shahid Shahid Iqbal Mutasem K.Alsmadi Yazeed Yasin Ghadi Syed Aziz Shah Mostafa M.Ibrahim Department of Computer Science and Technology Zhejiang Normal UniversityJinhua321002China Zhejiang Institute of Photoelectronics&Zhejiang Institute for Advanced Light Source Zhejiang Normal UniversityJinhua321004China Department of Data Science and Artificial Intelligence Zarqa UniversityZarqa13100Jordan School of Chemical and Environmental Engineering College of ChemistryChemical Engineering andMaterials ScienceSoochow UniversitySuzhou215123China Department of Management Information Systems College of Applied Studies and Community ServiceImam Abdulrahman Bin Faisal UniversityDammam32244Saudi Arabia Department of Computer Science Al Ain UniversityAbu Dhabi999041United Arab Emirates Department of Electrical Engineering Faculty of EngineeringMinia UniversityMinia61519Egypt Healthcare Technology and Innovation Theme Faculty Research Centre for Intelligent HealthcareCoventry UniversityCoventryCV48BWUK

Neuroimaging has emerged over the last few decades as a crucial tool in diagnosing Alzheimer’s disease(AD).Mild cognitive impairment(MCI)is a condition that falls between the spectrum of normal cognitive function and ***,previous studies have mainly used handcrafted features to classify MCI,AD,and normal control(NC)*** paper focuses on using gray matter(GM)scans obtained through magnetic resonance imaging(MRI)for the diagnosis of individuals with MCI,AD,and *** improve classification performance,we developed two transfer learning strategies with data augmentation(i.e.,shear range,rotation,zoom range,channel shift).The first approach is a deep Siamese network(DSN),and the second approach involves using a cross-domain strategy with customized *** performed experiments on the Alzheimer’s Disease Neuroimaging Initiative(ADNI)dataset to evaluate the performance of our proposed *** experimental results demonstrate superior performance in classifying the three binary classification tasks:NC ***,NC ***,and MCI ***,we achieved a classification accuracy of 97.68%,94.25%,and 92.18%for the three cases,*** study proposes two transfer learning strategies with data augmentation to accurately diagnose MCI,AD,and normal control individuals using GM *** findings provide promising results for future research and clinical applications in the early detection and diagnosis of AD.

关键词： Alzheimer’s disease mild cognitive impairment normal control transfer learning classification augmentation

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Constrained MPC Design for Heave Disturbance Attenuation in Offshore Drilling systems

Constrained MPC Design for Heave Disturbance Attenuation in ...

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Oceans 2013 - Norway

作者： Amirhossein Nikoofard Tor Arne Johansen Hessam Mahdianfar Alexey Pavlov Center for Autonomous Marine Operations and Systems (AMOS) Department of Engineering Cybernetics Norwegian University of Science and Technology Statoil Research Centre Department of Intelligent Well Construction

ISBN: (纸本)9781479900022

This paper presents a constrained model predictive control scheme for regulation of the annular pressure in a well during managed pressure drilling from a floating rig subject to heave motion. The results show that closed-loop simulation without disturbance has a fast regulation response and without any overshoot. The robustness of controller to deal with heave disturbances is investigated. The constrained MPC shows good disturbance rejection capabilities. The simulation results show that this controller has better performance than a PID controller and is also capable of handling constraints of the system with the heave disturbance.

关键词： Managed pressure drilling Heave Compensation and model predictive control

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Current status and framework of China's inland passenger ship safety system

Current status and framework of China's inland passenger shi...

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International Conference on Logistics, Informatics and Service Sciences (LISS)

作者： Shiqi Fan Di Zhang Eduardo Blanco-Davis Jin Wang Ming Huang Fan Zhang Xinping Yan School of Logistics Engineering National Engineering Research Centre for Water Transport Safety Wuhan China National Engineering Research Centre for Water Transport Safety Wuhan University of Technology Wuhan China Department of Maritime and Mechanical Engineering Liverpool John Moores University Liverpool United Kingdom School of Navigation Wuhan University of Technology Wuhan China National Engineering Research Centre for Water Transport Safety Intelligent Transport Systems Research Centre Wuhan China

ISBN: (纸本)9781509011032

Inland passenger shipping is one of the most convenient means of waterborne travel. However, recent serious accidents with passenger ships in China, have exposed a certain weakness in the country's inland passenger ship safety system. It is thus vital to overview the current conditions of China's inland passenger ship safety system, with regard to the different ship types, employment, management systems, legislative documents, specifications and standards, navigational regulations and emergency rescue procedures. In doing so, this paper analyses similarities and discrepancies with overseas inland river passenger ship safety systems. The paper then outlines the principles and proposes suggestions underlining the improved construction and implementation of the inland ship system. In particular, the last is done considering ship design, construction, operation, regulation, emergency rescue, practitioners, and relevant documents. The purpose of this work is to provide recommendations for policy making and scientific research on inland passenger transport with life cycle perspective.

关键词： Marine vehicles Safety Rivers Accidents Navigation Standards Europe

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A new unidirectional coupling for global synchronization of two Hindmarsh-Rose neurons

A new unidirectional coupling for global synchronization of ...

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第27届中国控制与决策会议

作者： Ke Ding Qing-Long Han School of Information Technology Jiangxi University of Finance and Economics Jiangxi E-commerce High Level Engineering Technology Research Centre Griffith School of Engineering Griffith UniversityGold Coast Campus Centre for Intelligent and Networked Systems Central Queensland University

ISBN: (纸本)9781479970186

In this paper,a new unidirectional coupling for global synchronization of two Hindmarsh-Rose neurons is *** with existing results,external control for global synchronization is no longer required.A synchronization criterion of two Hindmarsh-Rose neurons with rigorous mathematical proof is *** example is given to illustrate the effectiveness of the obtained result.

关键词： Synchronization,Unidirectional coupling,Hindmarsh-Rose neuron

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A Memristor Based Chaotic Oscillator for Weak Signal Detection and Its Circuitry Realization

Research Square

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research Square 2021年

作者： Dong, Kaifeng Xu, Kun Zhou, Youyou Zuo, Chao Wang, Leimin Zhang, Chuanke Jin, Fang Song, Junlei Mo, Wenqin Hui, Yajuan School of Automation China University of Geosciences Wuhan430074 China Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China Wuhan Second Ship Design and Research Institute Wuhan430064 China

A new type of weak signal detection system that combines the memristor and Van der pol-Duffing chaotic system has been proposed in this paper, and the dynamic characteristics of the system are studied. It is observed that the system can change from a chaotic state to a periodic state under different driving force amplitudes. Moreover, as compared with several classical chaotic oscillators, the numerical simulation results show that the system has stronger anti-noise performance with the detectable signal-to-noise ratio reaches -163dB, and has a wider detection range. Its detection accuracy is up to 1 × 10−9. More importantly, this paper provides the circuit implementation scheme of the system, and the weak signal can be detected with our designed circuit. This may offer a possible way for weak signal detection system with good performance in anti-noise performance, detection range and accuracy. © 2021, CC BY.

关键词： Memristors

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Design of intelligent Meridian Magnetotherapy instrument and Monitoring system based on TCM

Design of Intelligent Meridian Magnetotherapy instrument and...

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6th International Conference on intelligent Computing and Signal Processing (ICSP)

作者： Shuheng Ren Dongsong Yan Xu Yang Yuqian Liao Wenlong Yang Zhihao Liu School of International Energy Jinan University Zhuhai City Guangdong Province Guangdong elcctrical and intelligent control engineering technology research centre Zhuhai City Guangdong Province

An intelligent acupoint magnetotherapy instrument, used in physiotherapy as well as simultaneous magnetotherapy acupoint with multiple acupoints, and its monitoring system are developed. Based on TCM meridian theory, it can be designed patterns of health preservation, self-defined magnetotherapy, auxiliary medical treatment for common diseases and stuff. Through the recommendation of accurate acupoints, the instrument meets the needs of different groups and reduces the threshold of magnetotherapy equipment. At the same time, through the five elements music accompanied with the course of treatment, this instrument realizes the comprehensive treatment of magnetic and Wuyin Therapy and finally improve the effect of physiotherapy. At the same time, according to the data uploaded by the instrument, the monitoring system, with the functions of displaying real-time parameter, analyzing magnetotherapy instrument and course of treatment, which increases the user experience. A questionnaire is also used to design the course recommendation based on the user's own situation, improves the intelligence of the instrument. Therefore, the design has the characteristics of safety, convenience, simplification and intelligence. As it can well meet the needs of health preservation, it also has a good application prospect in the field of intelligent medical devices.

关键词： Medical devices Instruments Medical treatment Magnetic devices Signal processing Real-time systems User experience

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PillarDAN: Pillar-based Dual Attention Attention Network for 3D Object Detection with 4D RaDAR

PillarDAN: Pillar-based Dual Attention Attention Network for...

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International Conference on intelligent Transportation

作者： Jingzhong Li Lin Yang Yuxuan Chen Yixin Yang Yue Jin Kuanta Akiyama National Engineering Research Centre for Automotive Power and Intelligent Control School of Mechanical Engineering Shanghai Jiao Tong University Shanghai China Department of Computer Science and Engineering Hong Kong University of Science and Technology Kowloon Hong Kong

3D object detection plays an indispensable role in autonomous driving. Most existing methods for 3D object detection from point clouds are LiDAR-based. However, LiDAR may suffer significant performance degradation from poor environmental conditions, such as rainy and foggy weather. Compared to LiDAR, 4D RaDAR is more robust to various environments and can provide velocity information. Nevertheless, its point cloud is sparser and contains more noise. Thus, existing LiDAR-dependent 3D object detection methods cannot be effectively applied to 4D Radar. To cope with this issue, we propose a new framework to improve the 3D object detection performance of 4D Radar, dubbed Pillar-based Dual Attention Network (PillarDAN). Specifically, PillarDAN builds the Global Pillar Attention (GPA) to enhance the feature extraction capability from sparser 4D Radar point clouds. Meanwhile, the Pillar Feature Attention (PFA) is proposed to focus on the truly effective information, thus suppressing the noise of point clouds. We also present an effective 3D coordinate embedding to improve the position awareness of the bird's-eye-view (BEV) feature map. Experimental results on the Astyx HiRes2019 dataset show our PillarDAN achieves effective performance improvement, which is 3.28% higher in 3D mAP and 3.12% higher in BEV mAP than the previous best method.

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Double Deep Q-learning for Anti-saturation Attack Problem of Warship Group

Double Deep Q-learning for Anti-saturation Attack Problem of...

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Digital Twins and Parallel Intelligence (DTPI), IEEE International Conference on

作者： Wei Li Gang Xiong Hao Tao Yunjun Han Fang Wang Xisong Dong Zhen Shen State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences School of Artificial Intelligence University of Chinese Academy of Sciences Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Chinese Academy of Sciences China China Ship Development and Design Center Wuhan China School of Control Science and Engineering (CSE) Shandong University Jinan China Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Chinese Academy of Sciences Dongguan China

Anti-saturation attack (ASA) strategy is vital for the survival of a warship group, and attracts the focus of many researchers. In this paper, the dynamics of ASA is formulated as a Markov Decision Process (MDP) with an enhanced states space since those characters are involved, such as the formation and detection and interception areas of warship group. A reinforcement learning method (Double Deep Q-leaning, DDQN) is developed to solve the problem and deal with the curse of dimensionality whereby the cost-to-go value is calculated by a marine engagement simulation system. A heuristic defense algorithm guided by field expert knowledge is designed for comparison. The experimental results show that the DDQN method performs better in anti-saturation attack scenarios.

关键词： Industries Heuristic algorithms Digital twin Conferences Knowledge based systems Reinforcement learning Markov processes

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