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learning-based method for lane detection using regionlet representation

IET INTELLIGENT TRANSPORT SYSTEMS 2019年第12期13卷 1745-1753页

作者： Chen, Yuxuan Chen, Wei-Gang Wang, Xun Yu, Runyi Tian, Yan Hangzhou Dianzi Univ Sch Elect & Informat Hangzhou Peoples R China Zhejiang Gongshang Univ Sch Comp & Informat Engn Hangzhou Peoples R China Eastern Mediterranean Univ Dept Elect & Elect Engn Via Mersin 10 Gazimagusa Turkey

Lane detection is an important enabling or enhancing technology for many intelligent applications. A marker line can be segmented into several image blocks, each of which contains lane marking in the centre. This study proposes a learning-based method for lane localisation via detecting and grouping such image blocks. The authors model the marking class using regionlet representation, in which each image block is regarded as a region and is represented by a group of regionlets. A region feature composed of the features extracted from the regionlets contributes a weak classifier. A cascade structure detector is then trained for lane detection. At early stages, it rejects as many negatives as possible. Each layer of the cascade detector is a strong classifier, which consists of several weak classifiers. A real AdaBoost algorithm is adopted to select the most discriminative features and to train the classifiers. Since the use of regionlet features allows desired performance with only a few weak classifiers and the dimensionality of the features is significantly reduced by principal component analysis, the computational burden of their algorithm is much lower than other learning-based methods. Experiment results demonstrate the computational efficiency and robustness of the method.

关键词： feature extraction learning (artificial intelligence) image classification principal component analysis image representation image block lane marking learning-based method lane localisation weak classifier lane detection regionlet feature representation intelligent applications marker line segmentation feature extraction cascade structure detector AdaBoost algorithm principal component analysis

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A Distributed Optimization method with Unknown Cost Function in Multi-Agent Systems via a learning-based method 15

A Distributed Optimization Method with Unknown Cost Function...

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IEEE 15th International Conference on Control and Automation (ICCA)

作者： Pang, Yipeng Hu, Guoqiang Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore

ISBN: (纸本)9781728111643

This paper presents a distributed optimization algorithm to solve the unconstrained (not necessarily convex) optimization problems in a multi-agent system. The algorithm does not require any explicit expressions on the local cost functions, which means gradient information is not available. The only required information is the local measurements of the local cost function from each agent. The idea of the algorithm is to search for a better optimal point until no better points can be found. Each step-update is based on an estimation of the distance between the possible better points from the current point. The estimation is generated from a learning model, which is trained by the historical data of the step-updates. This algorithm does not require any assumptions on the convexity and differentiability of the local and/or global functions except that the solution exists and is finite. Convergence property of the proposed algorithm is carefully studied. based on the idea of exploitation and exploration, it is shown that the algorithm is able to escape from the local optimum to find the best known optimum. A numerical example is provided to verify the performance of the proposed algorithm.

关键词： Distributed non-convex optimization multiagent system learning-based method

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Dominant Emotion Recognition in Short Story Using Keyword Spotting Technique and learning-based method 4

Dominant Emotion Recognition in Short Story Using Keyword Sp...

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3rd International Conference On Advanced Informatics - Concepts, Theory And Application (ICAICTA)

作者： Amelia, Windy Maulidevi, Nur Ulfa Inst Teknol Bandung Sch Elect Engn & Informat Bandung Indonesia

ISBN: (纸本)9781509016365

Short story is one of the stories which has storyline, idea, thought, and feeling. A story is precisely contained of emotion. That is why emotion can be recognized in a short story. The aim of this research is to recognize the one most dominant emotion in short story. This research used Paul Ekman's basic emotions, which are angry, disgust, fear, happiness, sadness, and surprise. The method that was used in this research was hybrid method, a combination of keyword spotting technique and learning-based method. Keyword spotting technique utilized words in NRC Emotion Lexicon which initially were translated from English to Indonesian. On the other hand, learning-based method used three algorithms, those were Multinomial Logistic Regression, Support Vector Machine (SVM), and Naive Bayes Multinomial. Those three alghorithms used instance sentences in short story. Hybrid method implemented voting rule to determine the one most dominant emotion. The accuracy of hybrid method resulted in this research was 63.33%.

关键词： short story emotion word keyword spotting technique learning-based method

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Experimental Evaluations on learning-based Inter-Radar Wideband Interference Mitigation method

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IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES 2024年第8期E107A卷 1255-1264页

作者： Koizumi, Ryoto Wang, Xiaoyan Umehira, Masahiro Sun, Ran Takeda, Shigeki Ibaraki Univ Grad Sch Sci & Engn Hitachi 3168511 Japan Nanzan Univ Dept Elect & Commun Technol Nagoya 4668637 Japan

In recent years, high-resolution 77 GHz band automotive radar, which is indispensable for autonomous driving, has been extensively investigated. In the future, as vehicle-mounted CS (chirp sequence) radars become more and more popular, intensive inter-radar wideband interference will become a serious problem, which results in undesired miss detection of targets. To address this problem, learning-based wideband interference mitigation method has been proposed, and its feasibility has been validated by simulations. In this paper, firstly we evaluated the trade-off between interference mitigation performance and model training time of the learning-based interference mitigation method in a simulation environment. Secondly, we conducted extensive inter-radar interference experiments by using multiple 77 GHz MIMO (Multiple-Input and Multiple-output) CS radars and collected real-world interference data. Finally, we compared the performance of learning-based interference mitigation method with existing algorithm-based methods by real experimental data in terms of SINR (signal to interference plus noise ratio) and MAPE (mean absolute percentage error).

关键词： CS radar inter-radar interference wide band interference learning-based method experimental evaluations

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learning-based Key Points Estimation method for Burden Surface Profile Detection in Blast Furnace

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IEEE SENSORS JOURNAL 2022年第10期22卷 9589-9597页

作者： Wang, Hong Li, Wenbo Zhang, Tianxiang Li, Jiangyun Chen, Xianzhong Univ Sci & Technol Beijing Sch Automat & Engn Beijing 100083 Peoples R China

Accurate Burden Surface Profile (BSP) detection is important for the operation of Blast Furnace (BF). The signal-to-noise ratio of radar signals changes greatly during both the charging period of BF and the long maintenance period of the radar device, which increases the difficulty of radar BSP detection. The traditional radar BSP detection method based on signal energy relies on manually selected detection thresholds according to the noise intensity. Hence, the accuracy of the traditional radar BSP detection method is not reliable in the long term. To address this problem, we propose a novel learning-based Key Points estimation (KP-BSP) method to detect the key points of radar reconstructed BSP image, and a new Key Points-based Connected Region Noise Reduction (KP-CRNR) algorithm to remove the noise-affected regions. The prediction deviation at detected key points (at the positions of the mechanical probes) is then used to correct the radar detection results, leading to the improvement of radar detection accuracy. The experimental data were collected from Wuhan Iron Steel Company No.7 BF. The results show that the proposed methods can achieve an average RMSE of 0.0156m, which is improved by more than 50% compared with previous methods. The long-term reliability of the proposed method is also demonstrated in this dataset.

关键词： Radar Radar detection Radar imaging Estimation Probes Image reconstruction Signal to noise ratio Blast furnace burden surface profile radar learning-based method key points estimation

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A cognition-inspired trajectory prediction method for vehicles in interactive scenarios

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IET INTELLIGENT TRANSPORT SYSTEMS 2023年第8期17卷 1544-1559页

作者： Xie, Shanshan Li, Jiachen Wang, Jianqiang Tsinghua Univ State Key Lab Automot Safety & Energy Beijing Peoples R China Univ Calif Berkeley Berkeley CA 94720 USA

Trajectory prediction of the ego vehicle is necessary for the cooperation driving of intelligent vehicles and drivers. methods based on deep learning can fit complex functions, but they usually focus on vehicles' behavioral characteristics. However, vehicles' trajectories are closely related to the cognition results of drivers. Therefore, based on drivers' cognitive characteristics, a network model is designed to predict vehicle trajectories. Specifically, in the perception stage, featured grids are used that are in the driver's view to encode perceptual information;in the decision stage, convolution and graph attention operations are combined to model the driver's interaction with the surrounding traffic elements;in the motion stage, the elements are constrained in one hidden layer by vehicles' actual control inputs and design the corresponding method to obtain probabilistic results. With experiments in two typical scenarios, including intersection and roundabout, the proposed method can obtain reasonable prediction accuracy and generalizability. Meanwhile, abundant experiments are conducted and the results are compared, which reveal some common problems when predicting vehicle trajectories, particularly based on drivers' cognitive characteristics. These lessons learned from this study are summarized which may be useful for newcomers.

关键词： cognition-inspired method grid learning-based method vehicle trajectory prediction

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LEC-Codec: learning-based Genome Data Compression

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2024年第6期21卷 2447-2458页

作者： Sun, Zhenhao Wang, Meng Wang, Shiqi Kwong, Sam City Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China Lingnan Univ Sch Data Sci Hong Kong Peoples R China

In this paper, we propose a learning-based gEnome Codec (LEC), which is designed for high efficiency and enhanced flexibility. The LEC integrates several advanced technologies, including Group of Bases (GoB) compression, multi-stride coding and bidirectional prediction, all of which are aimed at optimizing the balance between coding complexity and performance in lossless compression. The model applied in our proposed codec is data-driven, based on deep neural networks to infer probabilities for each symbol, enabling fully parallel encoding and decoding with configured complexity for diverse applications. based upon a set of configurations on compression ratios and inference speed, experimental results show that the proposed method is very efficient in terms of compression performance and provides improved flexibility in real-world applications.

关键词： Genomics Bioinformatics Encoding Context modeling Symbols Predictive models Codecs Computational modeling Complexity theory Termination of employment Data compression learning-based method lossless genome compression non-reference method

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learning-based dispatching system with trajectory optimisation for autonomous mining transportation

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INTERNATIONAL JOURNAL OF MINING RECLAMATION AND ENVIRONMENT 2025年

作者： Pei, Shuwei Yang, Jue Gong, Zeyu Univ Sci & Technol Beijing Sch Mech Engn Beijing 100083 Peoples R China

This study addresses this challenge by formulating fleet management as a multi-agent reinforcement learning (MARL) problem and integrating a Deep Q-Network (DQN) dispatching system with Dynamic Programming (DP) velocity optimisation. Utilising an event-driven mining simulator based on real mine parameters, the proposed approach achieves a 10% reduction in average energy consumption per ton and increases total production by 3,000 tons over an 8-hour simulation compared to conventional baseline methods. It ensures more equitable task distribution and improved resource utilisation. Additionally, this policy maintains robust performance across varying fleet sizes (20-25 trucks) without retraining, ensuring consistent efficiency in dynamic operational environments.

关键词： Energy consumption fleet management learning-based method mining and sustainability transportation efficiency word

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A learning and evolution-based intelligence algorithm for multi-objective heterogeneous cloud scheduling optimization

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KNOWLEDGE-based SYSTEMS 2024年 286卷

作者： Hao, Yuanyuan Zhao, Chunliang Li, Zhong Si, Bingfeng Unger, Herwig Beijing Jiaotong Univ Sch Syst Sci 3 Shangyuan Village Beijing 100028 Peoples R China Fern Univ Hagen Fac Math & Comp Sci D-58097 Hagen Germany Qingdao Univ Sci & Technol Sch Informat Sci & Technol 99 Songling Rd Qingdao 266061 Peoples R China

The multi-objective directed acyclic graph scheduling problem (MDAGSP) is prevalent in cloud scheduling systems, involving the selection, assignment, and execution of multiple tasks/jobs with complex coupling interdependencies. High-quality solutions can yield substantial economic benefits. However, prevailing methods face challenges in obtaining a set of superior solutions for MDAGSP, due to the multifaceted nature of its variables, objectives, constraints, and heterogeneity. Firstly, this paper formulates a three-objective MDAGSP that includes makespan, energy costs and revenue, to model cloud scheduling systems. Subsequently, we propose a composite algorithm consisting of a selection phase and an assignment phase to automatically generate an efficient scheduling policy for this model. During the selection phase, a graph convolutional neural network learns high-level features to extract complex dependencies between tasks. During the assignment phase, an adaptive evolutionary algorithm assigns tasks to the appropriate executors. Finally, a series of experiments are conducted to validate the model's accuracy and assess the algorithm's efficiency. Compared to heuristic approaches, the algorithm achieves at least a 20.1% makespan reduction and 3.17% revenue increase. Remarkably, the algorithm obtains at least an 8.86% reduction in energy costs over the eleven baselines. In conclusion, the proposed algorithm provides decision-makers with a global view of scheduling plan.

关键词： Multi-objective scheduling problem Evolutionary algorithm Policy network learning-based method

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learning-based Bundling Strategy for Two Products Under Uncertain Consumer's Valuations

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IEEE TRANSACTIONS ON ENGINEERING MANAGEMENT 2025年 72卷 1970-1982页

作者： Fang, Lang Tang, Jiafu Pan, Zhendong Dongbei Univ Finance & Econ Sch Management Sci & Engn Dalian 116025 Peoples R China Dongbei Univ Finance & Econ Liaoning Key Lab Big Data Management & Optimizat D Dalian 116025 Peoples R China

Should a firm engage in bundling to boost revenue when consumer's valuations of products are heterogeneous and uncertain? In recent years, technological advances have made it possible for firms to use large amounts of available data to make decisions under demand-side information uncertainty. However, it remains unclear exactly how they can dynamically optimize bundling and pricing decisions by learning from uncertain consumer's valuations. To answer this question, in this article, we study the bundling and pricing decisions of a monopoly firm offering a basic product and a premium product over finite T periods. We first analyze the equilibrium outcomes of pure component strategies and pure bundling strategies (PBSs) under deterministic consumer's valuations. We then introduce a learning-based bundling strategy (LBBS) framework to make decisions dynamically over time. It employs Thompson sampling to estimate the fractions of low-valuation consumers (LVCs) of two products, allowing the firm to adjust its decisions based on updated beliefs about consumer's valuation distributions. We demonstrate the robust performance of the LBBS and show the interesting findings. That is, the fraction of LVC of premium product, the ratio between high and low valuation of basic product, the ratio of low valuation and the ratio of high valuation of two products wield significant influence on the adoption of bundling strategy and its possibility. These findings offer practical guidance to firm's practitioners regarding when and which PBS to adopt and how to improve PBS decisions according to the fractions of LVC of products. We also extend the model to uniform distribution of consumer's valuations and correlated consumer's valuations to test our results.

关键词： Cost accounting Pricing Training Data mining Artificial intelligence Uncertainty Profitability Supply chain management Sustainable development Surveys Bundling decision demand uncertainty learning-based method Thompson sampling

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