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Machine Learning-Based Calibration Approaches for Single-Beam and Multiple-Beam Distance Sensors

IEEE Sensors Journal 2024年第1期24卷 975-983页

作者： Syu, Jia-Hao Lin, Jerry Chun-Wei Biernacki, Piotr Ziebinski, Adam National Taiwan University Department of Computer Science and Information Engineering Taipei10617 Taiwan Silesian University of Technology Faculty of Automatic Control Electronics and Computer Science Department of Distributed Systems and Informatics Devices Gliwice44-100 Poland Silesian University of Technology Department of Distributed Systems and Informatic Devices Gliwice44-100 Poland

Sensors are the foundation to facilitate smart cities, smart grids, and smart transportation, and distance sensors are especially important for sensing the environment and gathering information. Researchers have developed various methods to calibrate distance sensors;however, they mainly focus on using external information for calibration, which is inefficient and computationally expensive. To alleviate the above problems, we propose machine learning (ML)-based calibration approaches for distance sensors. We develop two calibration mechanisms, where the naive mechanism directly predicts the true distance, while the residual mechanism predicts the residual for calibration. The conventional ML regression algorithms are then adopted to predict the true distance or calibration residual. Experimental results show that the designed residual calibration mechanism outperforms the naive mechanism by reducing the root mean square error (RMSE) by 92.2% and mean absolute error (MAE) by 93.3% in the fitting task. In prediction, the proposed 2-D-residual mechanism achieves an improvement of 78.6% and 65.7% compared to the state-of-the-art method. The experimental results show the effectiveness and generalization of the proposed ML-based calibration method, especially for the 2-D mechanism, which achieves the highest robustness in all fitting and prediction tasks. © 2001-2012 IEEE.

关键词： Calibration

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A Quantum Q-Learning Model for the Capacitated Vehicle Routing Problem 17

A Quantum Q-Learning Model for the Capacitated Vehicle Routi...

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17th IEEE International Symposium on Embedded Multicore/Many-core systems-on-Chip, MCSoC 2024

作者： Xu, Jiahua Xie, Ningyi Lee, Xinwei Saito, Yoshiyuki Asai, Nobuyoshi Cai, Dongsheng University of Tsukuba Graduate School of Science and Technology Japan Singapore Management University School of Computing and Information Systems Singapore University of Aizu Graduate School of Computer Science and Engineering Japan University of Aizu School of Computer Science and Engineering Japan University of Tsukuba Faculty of Engineering Information and Systems Japan

ISBN: (纸本)9798331530471

The research on Variational Quantum Algorithms (VQAs) has gained significant momentum because of their promising practicality in the noisy intermediate-scale quantum (NISQ) era. Recent studies highlight the potential of VQAs in reinforcement learning (RL) by replacing classical components with parameterized quantum circuits (PQCs). Building on this, we propose a quantum Q-learning model for the Capacitated Vehicle Routing Problem (CVRP), combining a PQC with an RL training process. We present a PQC for embedding both static and dynamic states in RL environments. The proposed PQC approximates the Q-value function by using the expectation values of different observables. Experimental results demonstrate that the proposed PQC outperforms the existing PQC for the CVRP with fewer quantum resources. Furthermore, our method shows competitive performance compared to the classical counterpart. © 2024 IEEE.

关键词： Vehicle routing

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Exploring Explainable AI for Enhanced Depression Prediction in Mental Health 1

Exploring Explainable AI for Enhanced Depression Prediction ...

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1st International Conference on Innovations in Communications, Electrical and Computer engineering, ICICEC 2024

作者： Jo, Ashly Ann Raj, Ebin Deni Vino, Angel Susan Menon, Parvathy V. Indian Institute of Information Technology FACTS-H Lab Department of Computer Science & Engineering Kottayam India

ISBN: (纸本)9798350376517

Depression is a prevalent mental health condition affecting millions of individuals worldwide. This paper presents a comprehensive analysis using machine learning to predict depression, a prevalent mental health issue affecting millions worldwide. The dataset employed for this study includes a range of mental health conditions such as schizophrenia, bipolar disorder, eating disorders, anxiety disorders, drug use disorders, and alcohol use disorders, investigating their correlations with depression. We explore these relationships and develop predictive models to forecast the likelihood of depression. The study evaluates the performance of various machine learning algorithms, with the Random Forest algorithm demonstrating the highest accuracy rate of 99.30%. Furthermore, we delve into model interpretability by applying techniques like LIME and SHAP to explain the predictions. Our findings contribute significantly to the understanding and prediction of depression, offering valuable insights that could assist healthcare professionals in the early detection and proactive management of this condition. © 2024 IEEE.

关键词： Adversarial machine learning

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TCIP: Transformed Colour Intensity Profiles analysis for fault detection in power line insulators

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Multimedia Tools and Applications 2025年第8期84卷 4503-4534页

作者： Tomaszewski, Michal Gasz, Rafal Kasana, Singara Singh Osuchowski, Jakub Singh, Simranjit Zator, Slawomir Department of Computer Science Faculty of Electrical Engineering Automatic Control and Informatics Opole University of Technology Prószkowska 76 Opole45-758 Poland Department of Computer Science and Information Technology Central University of Haryana Haryana Mahendergarh123031 India Department of Information Technology Dr B. R. Ambedkar National Institute of Technology Punjab Jalandhar India Department of Safety Engineering and Technical Systems Faculty of Production Engineering and Logistics Opole University of Technology Prószkowska 76 Opole45-758 Poland

In response to the escalating demand for electricity, the aging process and inherent failures in power lines have become unavoidable challenges in their operational integrity. This research addresses the imperative need for a robust method to detect damage in power insulator disc images, introducing a novel feature extraction technique grounded in color intensity profile transformation and ensemble learning. For the purposes of the research, a specific dataset was developed based on publicly available images of power insulators. The collection consists of 9232 images of insulator discs which included 2184 images of damaged discs. The discernible damages include breakages and burnouts. Two distinct analysis variants were employed. In the first variant (V1), two classification groups were selected: disc with damages and disc without damages. In the second variant (V2), three classification groups were selected: discs without damage, broken discs, and burnout discs. The described research was divided into two successive stages. In the first of them (S1), the transformed color intensity profiles for the prepared data set were built and various machine learning models for their classification were trained and evaluated. In the next stage (S2), previously developed, highest efficient machine learning models, were used to make the detection of various damaged types of overhead line insulator discs. For the first stage of research high-performance results and a high value of the F1 score parameters were obtained (F1 score = 0.999 for the first variant (S1V1) and F1 score = 0.992 for the second variant (S1V2). In the second stage of research and the first variant (S2V1), the highest result of the F1 score was 0.932 and for the second variant (S2V2) F1 score was 0.930. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Feature extraction

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Semantic Segmentation and YOLO Detector over Aerial Vehicle Images

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Computers, Materials & Continua 2024年第8期80卷 3315-3332页

作者： Asifa Mehmood Qureshi Abdul Haleem Butt Abdulwahab Alazeb Naif Al Mudawi Mohammad Alonazi Nouf Abdullah Almujally Ahmad Jalal Hui Liu Faculty of Computing and AI Air UniversityIslamabad44000Pakistan Department of Computer Science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia Department of Information Systems College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj16273Saudi Arabia Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Cognitive Systems Lab University of BremenBremen28359Germany

Intelligent vehicle tracking and detection are crucial tasks in the realm of highway ***,vehicles come in a range of sizes,which is challenging to detect,affecting the traffic monitoring system’s overall *** learning is considered to be an efficient method for object detection in vision-based *** this paper,we proposed a vision-based vehicle detection and tracking system based on a You Look Only Once version 5(YOLOv5)detector combined with a segmentation *** model consists of six *** the first step,all the extracted traffic sequence images are subjected to pre-processing to remove noise and enhance the contrast level of the *** pre-processed images are segmented by labelling each pixel to extract the uniform regions to aid the detection phase.A single-stage detector YOLOv5 is used to detect and locate vehicles in *** detection was exposed to Speeded Up Robust Feature(SURF)feature extraction to track multiple *** on this,a unique number is assigned to each vehicle to easily locate them in the succeeding image frames by extracting them using the feature-matching ***,we implemented a Kalman filter to track multiple *** the end,the vehicle path is estimated by using the centroid points of the rectangular bounding box predicted by the tracking *** experimental results and comparison reveal that our proposed vehicle detection and tracking system outperformed other state-of-the-art *** proposed implemented system provided 94.1%detection precision for Roundabout and 96.1%detection precision for Vehicle Aerial Imaging from Drone(VAID)datasets,respectively.

关键词： Semantic segmentation YOLOv5 vehicle detection and tracking Kalman filter SURF

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New Perspectives on Data Exfiltration Detection for Advanced Persistent Threats Based on Ensemble Deep Learning Tree 19

New Perspectives on Data Exfiltration Detection for Advanced...

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19th International Conference on Web information systems and Technologies, WEBIST 2023

作者： Cai, Xiaojuan Koide, Hiroshi Department of Information Science and Technology Information Science and Electrical Engineering Kyushu University Fukuoka Japan Section of Cyber Security for Information Systems Research Institute for Information Technology Kyushu University Fukuoka Japan

ISBN: (纸本)9789897586729

Data exfiltration of Advanced Persistent Threats (APTs) is a critical concern for high-value entities such as governments, large enterprises, and critical infrastructures, as attackers deploy increasingly sophisticated and stealthy tactics. Although extensive research has focused on methods to detect and halt APTs at the onset of an attack (e.g., examining data exfiltration over Domain Name System tunnels), there has been a lack of attention towards detecting sensitive data exfiltration once an APT has gained a foothold in the victim system. To address this gap, this paper analyzes data exfiltration detection from two new perspectives: exfiltration over a command-and-control channel and limitations on exfiltration transfer size, assuming that APT attackers have established a presence in the victim system. We introduce two detection mechanisms (Transfer Lifetime Volatility & Transfer Speed Volatility) and propose an ensemble deep learning tree model, EDeepXGB, based on eXtreme Gradient Boosting, to analyze data exfiltration from these perspectives. By comparing our approach with eight deep learning models (including four deep neural networks and four convolutional neural networks) and four traditional machine learning models (Naive Bayes, Quadratic Discriminant Analysis, Random Forest, and AdaBoost), our approach demonstrates competitive performance on the latest public real-world dataset (Unraveled-2023), with Precision of 91.89%, Recall of 93.19%, and F1-Score of 92.49%. Copyright © 2023 by SCITEPRESS - science and technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

关键词： Sensitive data

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Self-supervised graph learning with target-adaptive masking for session-based recommendation

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Frontiers of information technology & Electronic engineering 2023年第1期24卷 73-87页

作者： Yitong WANG Fei CAI Zhiqiang PAN Chengyu SONG Science and Technology on Information Systems Engineering Laboratory National University of Defense TechnologyChangsha 410073China

Session-based recommendation aims to predict the next item based on a user’s limited interactions within a short *** approaches use mainly recurrent neural networks(RNNs)or graph neural networks(GNNs)to model the sequential patterns or the transition relationships between ***,such models either ignore the over-smoothing issue of GNNs,or directly use cross-entropy loss with a softmax layer for model optimization,which easily results in the over-fitting *** tackle the above issues,we propose a self-supervised graph learning with target-adaptive masking(SGL-TM)***,we first construct a global graph based on all involved sessions and subsequently capture the self-supervised signals from the global connections between items,which helps supervise the model in generating accurate representations of items in the ongoing *** that,we calculate the main supervised loss by comparing the ground truth with the predicted scores of items adjusted by our designed target-adaptive masking ***,we combine the main supervised component with the auxiliary self-supervision module to obtain the final loss for optimizing the model *** experimental results from two benchmark datasets,Gowalla and Diginetica,indicate that SGL-TM can outperform state-of-the-art baselines in terms of Recall@20 and MRR@20,especially in short sessions.

关键词： Session-based recommendation Self-supervised learning Graph neural networks Target-adaptive

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Collecting and analyzing key-value data under shuffled differential privacy

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Frontiers of Computer science 2023年第2期17卷 241-243页

作者： Ning WANG Wei ZHENG Zhigang WANG Zhiqiang WEI Yu GU Peng TANG Ge YU Faculty of Information Science and Engineering Ocean University of ChinaQingdao 266100China School of Computer Science and Engineering Northeastern UniversityShenyang 110819China Key Lab of Cryptologic Technology and Information Security Ministry of EducationShandong UniversityQingdao 266200China School of Cyber Science and Technology Shandong UniversityQingdao 266200China

1 Introduction Key-value data,as NoSQL data widely used in recent years,has been frequently collected and used for analysis in major websites and mobile *** frequency distribution estimation on the key domain,as well as the mean estimation of values associated with the same key,are crucial and worth billion dollars in the ***,in real applications,the key-value records are submitted by terminal devices of different *** collecting the information from users may pose serious risks on personal *** the other hand,the emerging shuffled differential privacy(SDP)model[1]is proposed to collect sensitive data from users.

关键词： NoSQL app value

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A Numerical Calculation Algorithm of the Inclination Function Based on the Representation of SO(3) Group

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Acta Mathematicae Applicatae Sinica 2024年第1期40卷 137-148页

作者： Yu-xiao Wei Wen-lin Tang Department of Geophysics School of Geology Engineering and GeomaticsChang’An UniversityXi’an710064China Key Laboratory of Electronics and Information Technology for Space Systems National Space Science CenterChinese Academy of SciencesBeijing100190China

In the determination of the Earth gravity field in satellite geodesy, the inclination functions represent the projection of data observed along the orbital plane of a satellite orbit into the sphere in the terrestial reference frame. The inclination functions in this work is studied from a group theoretical perspective. The inclination functions are proved to generate a representation of the SO(3) group. An orthogonal relation of the inclination functions is derived and some recurrence relations for the inclination functions are given, based on which an algorithm to calculate the inclination functions is proposed.

关键词： SO(3)group inclination function satellite geodesy representation of SO(3)

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IRMIRS:Inception-ResNet-Based Network for MRI Image Super-Resolution

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Computer Modeling in engineering & sciences 2023年第8期136卷 1121-1142页

作者： Wazir Muhammad Zuhaibuddin Bhutto Salman Masroor Murtaza Hussain Shaikh Jalal Shah Ayaz Hussain Department of Electrical Engineering Balochistan University of Engineering and TechnologyKhuzdar89100Pakistan Department of Computer Systems Engineering Balochistan University of Engineering and TechnologyKhuzdar89100Pakistan Department of Mechanical Engineering Balochistan University of Engineering and TechnologyKhuzdar89100Pakistan Department of Mechanical Engineering National Taiwan University of Science and Technology10607Taiwan Department of Information Systems Kyungsung UniversityBusan613010South Korea

Medical image super-resolution is a fundamental challenge due to absorption and scattering in *** challenges are increasing the interest in the quality of medical *** research has proven that the rapid progress in convolutional neural networks(CNNs)has achieved superior performance in the area of medical image ***,the traditional CNN approaches use interpolation techniques as a preprocessing stage to enlarge low-resolution magnetic resonance(MR)images,adding extra noise in the models and more memory ***,conventional deep CNN approaches used layers in series-wise connection to create the deeper mode,because this later end layer cannot receive complete information and work as a dead *** this paper,we propose Inception-ResNet-based Network for MRI Image Super-Resolution known as *** our proposed approach,a bicubic interpolation is replaced with a deconvolution layer to learn the upsampling ***,a residual skip connection with the Inception block is used to reconstruct a high-resolution output image from a low-quality input *** and qualitative evaluations of the proposed method are supported through extensive experiments in reconstructing sharper and clean texture details as compared to the state-of-the-art methods.

关键词： Super-resolution magnetic resonance imaging ResNet block inception block convolutional neural network deconvolution layer

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