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Prediction of Driving Departure of Mining Autonomous Transport Vehicles Based on GRU Network

Prediction of Driving Departure of Mining Autonomous Transpo...

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IEEE international conference on Industrial Informatics (IndIN)

作者： Lecong Li Guizhen Yu Han Li Qi Xia Xiangyu Zhang Han Cai School of Transportation Science and Engineering State Key Lab of Intelligent Transportation System The Key Laboratory of Autonomous Transportation Technology for Special Vehicles Ministry of Industry and Information Technology Beihang University Beijing China

ISBN: (数字)9798331527471

ISBN: (纸本)9798331527488

Open-pit mining areas have special geological structures, complex road networks, multi-rotation sections and poor road conditions, which bring many challenges to the operation of mining autonomous transport vehicles. Due to the large size and high control difficulty of mining autonomous transport vehicles, poor control effect and inaccurate steering mechanism implementation are prone to occur during the driving process, which leads to the vehicle departure from the reference path. To ensure the safety of autonomous operation in intelligent mine, this paper proposes a method for predicting driving departure of mining autonomous vehicles based on Gated Recurrent Unit (GRU). Firstly, the vehicle's historical trajectory data is obtained via on-board sensors, followed by data cleaning and normalization processes. Key features are extracted, and a vehicle driving scene recognition module based on a GRU-based network is designed using deep learning. Subsequently, a GA-seq2seqGRU trajectory prediction module is constructed utilizing the scene recognition results, and the Genetic Algorithms (GA) is employed to tune the network hyper-parameters. Based on the predicted trajectories, the overall departure risk is calculated using two departure judgment methods based on cross-lane time and predicted lateral deviation, which are mapped to the departure level. Simulation experiments demonstrate that the accuracy of the driving scene recognition model in the proposed method in this paper reaches 0.9721, which is better than the 0.9585 of the Long Short Memory Neural Network (LSTM) model, and the trajectory prediction model based on the results of the driving scene recognition has a smaller RMSE value than the LSTM, GRU, and GA-seq2seqLSTM models when the prediction time domains are 1s, 2s, 3s, 4s, and 5s, and the departure detection module The average time consumed is 0.142ms.

关键词： Accuracy Roads Predictive models Feature extraction Trajectory Safety data mining Long short term memory Autonomous vehicles Genetic algorithms

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A Multi-Source Domain Generalization Network for Rotating Machinery Fault Diagnosis under Unseen Operating Conditions

A Multi-Source Domain Generalization Network for Rotating Ma...

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IEEE international conference on Industrial Informatics (IndIN)

作者： Tianyu Gao Jingli Yang Weiwei Hao Xiaopeng Fan School of Electronics and Information Engineering Harbin Institute of Technology Harbin China China Institute of Marine Technology and Economy Beijing China Faculty of Computing Harbin Institute of Technology Harbin China

ISBN: (数字)9798331527471

ISBN: (纸本)9798331527488

With the rapid development or industrial intelligence, domain adaptation technology depending on the availability of the target domain has gradually become a reliable solution to address the performance degradation of fault diagnosis due to the variation of operating conditions or rotating machinery. However, the fault diagnosis scenarios under unseen operating condition are commonly confronted in practical engineering. In response to the above limitations, novel multi-source domain generalization network (MDGN) is proposed in this paper to enhance the fault diagnosis performance of rotating machinery under unseen operating conditions by decoupling the domain-relevant feature information for obtaining domain-invariant diagnosis knowledge. First, a temporal convolutional autoencoder (TCAE) is developed as a feature extraction module to fully capture the effective spatio-temporal information of the sample data. Then, a domain classification module is designed for adversarial transfer learning to facilitate the acquisition of domain-invariant features. Finally, domain divergence loss and boundary margin loss are constructed for MDGN to supervise the feature distribution, alignment among domains and feature distinction among classes. The mechanical comprehensive diagnosis simulation platform (MCDSP) bearing dataset collected in our laboratory is employed to evaluate the domain generalization performance or the proposed method. The experimental results demonstrate that the method achieves an average fault diagnosis is accuracy of 94.50%, which is better than the comparison algorithms.

关键词： Fault diagnosis Knowledge engineering Degradation Accuracy Transfer learning Feature extraction Machinery Informatics

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Evaluation of Grasps in an automatic Intermodal Container Unloading System 25

Evaluation of Grasps in an automatic Intermodal Container Un...

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25th KES international conference on Knowledge-Based and intelligent Information & engineering Systems (KES)

作者： Kharitonov, Aleksei Kirchheim, Alice Hentsch, Maximilian Seibold, Johannes Echelmeyer, Wolfgang Aalen Univ Beethovenstr 1 D-73430 Aalen Germany Steinbeis Innovat gGmbH Adomostr 8 D-70599 Stuttgart Germany Reutlingen Univ Alteburgstr 150 D-72762 Reutlingen Germany

Today, many industrial tasks are not automated and still require human intervention. One of these tasks is the unloading of oversea containers. After the end of transportation to the sorting center, the containers must be unloaded manually for further sending the parcels to the recipients. A robot-based automatic unloading of containers was therefore researched. However, the promising results of the system developed in these projects could not be commercialized due to problems with its reliability. Mechanical, algorithmic or other limitations are possible causes of the observed errors. To analyze errors, it is necessary to evaluate the results of the robot's work without complicating the existing system by adding new sensors to it. This paper presents a reference system based on machine learning to evaluate the robotics grasps of parcels. It analyzes two states of the container: before and after picking up one box. The states are represented as a point cloud received from a laser scanner. The proposed system evaluates the success of transferring a box from an overseas container to the sorting line by supervised learning using convolutional neural networks (CNN) and manual labeling of the data. The process of obtaining a working model using a hyperband model search with a maximum classification error of 3.9 % is also described. (C) 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-nd license (https://***/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of KES international.

关键词： automatic parcel unloading evaluation laser scanner machine learning point cloud automated container unloading

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AI-Enabled E-Skin with High-Accuracy Material and Texture Recognition via Stick-Slip and Contact Electrification

AI-Enabled E-Skin with High-Accuracy Material and Texture Re...

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international conference on Solid State Sensors and Actuators (TRANSDUCERS)

作者： Jiahao Yu Jiyuan Zhang Aocheng Bao Jin Wu Bowen Ji Honglong Chang Weizheng Yuan Kai Tao School of Mechanical Engineering Northwestern Polytechnical University China Research & Development Institute in Shenzhen Northwestern Polytechnical University China School of Electronics and Information Technology Sun Yat-sen University China Unmanned System Research Institute Northwestern Polytechnical University China

Bionic electronic skin (e-skin) could endow intelligent robot hands and prosthetics with the ability to perceive materials and texture. Herein, an artificial intelligence (AI)-enabled self-powered e-skin with high-accuracy material and texture recognition via a comprehensive sensing strategy of stick-slip and contact electrification (SC e-skin) is proposed in this work, providing a brand-new solution for human-machine interaction interface. SC e-skin generates stable output voltage based on the coupling of triboelectric and electrostatic induction, and the self-powered sensing was realized subsequently. To make the best of the stick-slip, the macroscopic structure of the interdigital silver electrode and micro structure of hair-like fluorinated ethylene propylene (FEP) surface were fabricated simultaneously, constructing the compact and flexible bionic SC e-skin with polyimide (PI) substrate. The differences of the output signal generated by SC e-skin may not be noticed by human vision clearly, therefore, artificial intelligence was introduced to recognize the material type and even the surface texture. Benefiting from machine learning, the recognition accuracy of six different materials and four kinds of carbon steel with various roughness could achieve as high as 94.88 % and 95.75 %, respectively. This work could pave the way for soft robotics in digital twin and metaverse applications.

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Modeling Without Sharing Privacy: Federated Neural Machine Translation 22nd

Modeling Without Sharing Privacy: Federated Neural Machine T...

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22nd international conference on Web Information Systems engineering (WISE)

作者： Wang, Jianzong Huang, Zhangcheng Kong, Lingwei Li, Denghao Xiao, Jing Ping Technol Shenzhen Co Ltd Shenzhen Peoples R China

ISBN: (纸本)9783030908881;9783030908874

Training neural machine translation models requires large amount of diverse training corpora. It poses a challenge for collecting sufficient data. In addition, labeling monolingual corpus demands professional knowledge in certain domain. Building collaboration between different institutes produces other problems such as legality of data exchange and commercial data leakage. In this paper, we proposed a federated neural machine translation model FedNMT to train a robust machine translation system without sharing raw data from participants. By applying FedNMT, neural machine translation (NMT) systems can be ameliorated from the corpus held by different contributors without directly exposing them to one another. This approach preserves the user privacy by utilizing the federated learning framework, encryption techniques. In the federated learning paradigms, a global model is distributed to user clients, and a central server is built to aggregate the learning parameters and update the gradients. Experimental results show the effectiveness of our model in comparison with the data-centralized model.

关键词： Machine translation Federated learning Privacy preserving

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Multi-person Identification and Localization Algorithm Using RGB-D Image Segmentation

Multi-person Identification and Localization Algorithm Using...

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international conference on Communication Technology (ICCT)

作者： Xu Han Jun Yan College of Communication & Information Engineering Nanjing University of Posts & Telecommunications Nanjing China

In recent years, with the development of smart phones and intelligent monitoring systems, visual object detection and localization methods have been widely used. Since RGB-D based person identification and localization technology have achieved better performances, an algorithm based on deep learning and image segmentation is proposed in this paper. First, the raw images are preprocessed. Then, image segmentations are performed according to the results of person detecting by YOLOv3 network. For multi-person identification, the Haar cascade classifier is used to determine regions of face on the segmented color images and the Eigen Face method is used for classification. For person localization, the convolutional neural network is preformed regression learning based on the depth images of a single person. Experiments results show that the proposed algorithm performs better than other existing approaches.

关键词： Location awareness Image segmentation Visualization Neural networks Object detection Classification algorithms Faces

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Leveraging Reinforcement learning and Blockchain Technology for Enhanced Supply Chain Management Efficiency and Transparency

Leveraging Reinforcement Learning and Blockchain Technology ...

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Innovations in High Speed Communication and Signal Processing (IHCSP), international conference on

作者： Bertila A K.Raghuveer Shuchita Chandhok Prasanta Parida P. Pirakatheeswari Ponni Valavan M Saveetha School of Law Saveetha Institute of Medical and Technical Sciences SIMATS Chennai India School of Management V.R. Siddhartha Engineering College (Deemed to be University) Kanuru A.P India AICC Amity University AUUP Noida India School of Rural Management KIIT University Bhubaneswar Odisha India Sri Ramakrishna College of Arts & Science Coimbatore India Department of AI & DS K. Ramakrishnan College of Engineering Trichy Tamil Nadu India

ISBN: (数字)9798350368949

ISBN: (纸本)9798350368956

In today’s complex global economy, supply chain management (SCM) efficiency, transparency and security face enormous challenges. The benefits of emerging technologies such as reinforcement learning (RL) and blockchain provide a transformational approach to these issues. This study explores the integration of RL and blockchain technologies to enhance the SCM process. Reinforcement learning, a subset of machine learning, enables systems to learn optimal design through trial and error interactions with dynamic environments When applied to SCM, RL algorithms can continuously learn from supply chain data and adjust processes in real-time for routing, optimal inventory management, and demand forecasting. Blockchain technology supports RL by creating a decentralized and immutable ledger that provides transparency and security throughout the supply chain. Every transaction from raw materials to end products is recorded in the blockchain, creating an accountable process that increases trust among stakeholders, decreases it fraud and integrates smart contracts to keep the transaction automated, ensure compliance with predetermined conditions and increase operational efficiency. This paper presents a framework for integrating RL and blockchain in SCM, focusing on the benefits of integrating enterprises with these technologies. The proposed approach demonstrates how RL can enable adaptive intelligent decision making, while ensuring the integrity and transparency of blockchain data.

关键词： Technological innovation Supply chain management Heuristic algorithms Supply chains Smart contracts Signal processing algorithms Reinforcement learning Blockchains Security Stakeholders

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Advanced Deep learning Methods for Comprehensive Quality Inspection and Fault Detection in Casting Processes

Advanced Deep Learning Methods for Comprehensive Quality Ins...

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Advancement in Computation & Computer Technologies (InCACCT), international conference on

作者： Gunjan Sharma Vatsala Anand Rahul Chauhan Hemant Singh Pokhariya Sheifali Gupta G Sunil Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Computer Science and Engineering Graphic Era Hill University Dehradun Uttarakhand India Computer Science and Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India School of Computer Science & Artificial Intelligence SR University Warangal

ISBN: (数字)9798350371314

ISBN: (纸本)9798350371321

Encouraging the production of casting products while cutting down on inspection expenses and time commitments is made possible through automated surface fault inspection. As a key element of contemporary modern manufacturing, intelligent systems with image classification capabilities are frequently used in visual inspection. In comparison to traditional machine learning methods, Convolutional Neural Networks have demonstrated notable improvements in image categorization tasks. The purpose of this research is to investigate the use of transfer learning techniques for the recognition and classification of casting defects on the surface using the Resnet152V2-based Convolutional Neural Network architecture. To demonstrate the model’s adaptability in heterogeneous casting, a range of image-processing techniques were first used to casting datasets for data enhancement. The lightweight ResNet152V2 model that has already been trained is then modified, and its hyperparameters are adjusted to maximize its performance. The effort produces a scalable, lightweight, and adaptive model that is perfect for edge devices with limited resources. The model has been assessed using different performance matrix. The accuracy of the model is 99.65%, precision achieved is 99.95%, recall is 99%, based on statistical parameters. These findings demonstrate that a CNN framework based on the ResNet150V2 architecture can be effectively used for the identification of casting surface defects of impellers used in pumps in manufacturing sectors, despite its uncomplicated architecture.

关键词： Performance evaluation Casting Adaptation models Visualization Transfer learning Impellers Production

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A Machine learning Based MPTCP Subflow Control Algorithm for Multi-access Heterogeneous Networks 22

A Machine Learning Based MPTCP Subflow Control Algorithm for...

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22nd IEEE international conference on Communication Technology, ICCT 2022

作者： Chen, Chengbing Jiang, Lingyun Jun, She Zhu, Hongbo Gu, Jia Chang, Xiangbei School of Communications and Information Engineering Nanjing University of Posts and Telecommunications Nanjing China Jiangsu Electric Power Research Institute Co. Ltd. Nanjing China

ISBN: (纸本)9781665470674

Ever growing number of devices and the demand for applications are generating tremendous network traffic today. In the edge network environment, due to the network instability caused by device movement, the single network cannot meet its throughput requirements. Hence, using multi-access network to transmit data packages has become a way to improve throughput. Multi-path TCP utilizes multiple channels to transmit data packages simultaneously. However, MPTCP is unable to control subflows adaptively, and the disorder of data packages caused by difference between paths leads to its poor performance compared with the single path transmission. Therefore, a new MPTCP subflow control algorithm (MSCA) based on Support Vector Machine (SVM) prediction model is proposed. The SDN controller is utilized to continuously monitor the network status and predicts the impact factor of the network based on the path parameters monitored. Then, according to the impact factor, the subflow configurations is dynamically adjusted by the system for each user to improve the average throughput. The system is implemented on the network emulator platform Mininet-WiFi. Emulation results shows the proposed MSCA improves the average throughput compared with traditional scheme. MSCA also responses quickly to the network changes. © 2022 IEEE.

关键词： Support vector machines

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SRBR: Anti-selfish Routing Based on Social Similarity and Reputation Using Fuzzy Logic

SRBR: Anti-selfish Routing Based on Social Similarity and Re...

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IEEE international conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Haoxiang Wang Yu’Ang Zhang Yujie Song Yue Cao Chee Yen Leow Shihan Bao School of Cyber Science and Engineering Wuhan University China Wireless Communication Centre Universiti Teknologi Malaysia Malaysia Innovation Centre CICT Mobile Beijing China

ISBN: (数字)9798350381993

ISBN: (纸本)9798350382006

With the emergence of various applications in intelligent Transportation Systems (ITS), the cooperative communication among vehicles has become increasingly important to provide transportation data services. However, vehicles with selfish behaviors rarely provide high-performance communication services to others, resulting in a low packet delivery ratio and a high end-to-end delay. It is extremely challenging to substantially alleviate the influence of selfish vehicles in vehicular networks, because of the network topology and unstable communication qualities. To address the above problems, the Social similarity and Reputation Based Routing (SRBR) is proposed, considering social relationships, vehicle behaviors, and transmission capacity. The social similarity can improve network performance by leveraging the social relationships of vehicles, while reputation mechanisms can effectively defend against selfish vehicles’ attacks. Additionally, to integrate the above two-dimensional metrics, fuzzy logic is employed. Furthermore, extensive results show SRBR outperforms other baseline schemes in terms of delivery ratio, average latency, and overhead ratio.

关键词： Fuzzy logic Measurement Privacy Machine learning algorithms Network topology Transportation Machine learning

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