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2nd World Conference on Intelligent and 3D Technologies, WCI3DT 2023

作者： Lu, Wanli Zhang, Wen Sun, Mingrui Zhang, Jindong College of Computer Science and Technology Jilin University Changchun130012 China College of Software Jilin University Changchun130012 China Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education Jilin University Changchun130012 China

ISBN: (纸本)9789819721436

Visual perception plays an important role in autonomous driving technology. The two key factors in visual perception tasks are monocular object detection and structured data analysis. In this paper, a structured data parsing algorithm based on monocular vision is proposed. Firstly, using the single-stage YOLOv5 object detection model, the target was located and its kind was identified. Secondly, the 3D position of the object in the camera coordinate system is calculated according to the mapping relationship between the imaging point and the target point. Finally, the distance between the object and the camera is calculated by using the pinhole camera model, and the size of the object and the horizontal declination Angle relative to the optical center of the camera are estimated by geometric derivation. This method not only has a simple principle, but also guarantees the precision of structured data output. Experimental findings support the method's viability and efficiency. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Deep neural networks

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Diagnosis of breast cancer tumor based on ICA and LS-SVM

Diagnosis of breast cancer tumor based on ICA and LS-SVM

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2006 International Conference on Machine Learning and Cybernetics

作者： Wang, Chao-Yong Wu, Chun-Guo Liang, Yan-Chun Guo, Xin-Chen Key Laboratory of Symbol Computation and Knowledge Engineering Jilin University Ministry of Education Changchun 130012 China Department of Fundamental Sciences Jilin Teacher's Institute of Engineering and Technology Changchun 130021 China Key Laboratory of Information Science and Engineering Beijing Jiaotong University Railway Ministry Beijing 100044 China

ISBN: (纸本)1424400619

An efficient method for the diagnosis of breast cancer tumor is proposed based on Independent Component Analysis (ICA) and Least Square Support Vector Machine (LS-SVM). In order to save the expense of detection, firstly, variables are selected based on the theory of statistics. Then the ICA is introduced in a concise way and followed by extracting the ICA component from these selected variables. Finally the processed data are classified by the LS-SVM. Experimental and analytical results show that in the diagnosis of breast cancer tumor the proposed method is superior to the classical B-P algorithm. © 2006 IEEE.

关键词： Biomedical engineering

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Semi-automatic hardware design using ontologies

Semi-automatic hardware design using ontologies

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International Conference on Control, Automation, Robotics and Vision (ICARCV)

作者： He Hu Da-you Liu Xiao-yong Du School of Information Renmin University of China Beijing China Open Symbol Computation and Knowledge Engineering Laboratory of State Education Committee Jilin University Changchun China

ISBN: (纸本)0780386531

Hardware description language (HDL) is widely used to model the structure and behavior of digital systems ranging from simple hardware building blocks to complete systems in today's hardware design. To tackle with ever-increasing complexity of process models, higher level of abstraction needs to be exploited. We propose an ontology-based architecture in the process of hardware design using HDLs. The architecture combines the traditional HDL design processes with ontology models and offers a semi-automatic process utilizing the ontology reasoning abilities of OWL (Web ontology language). Based on such ontology assistant architecture, more flexible and automatic processes can be devised, which are of crucial importance to meet current market demands.

关键词： Ontologies Hardware design languages Process design Object oriented modeling OWL Very high speed integrated circuits knowledge engineering Digital systems Service oriented architecture Logic design

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An adaptive multi-heuristic ant colony system for finding optimal elimination orderings in Bayesian networks

An adaptive multi-heuristic ant colony system for finding op...

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International Conference on Electrical engineering Computing Science and Automatic Control

作者： Dong, Xuchu Zhang, Yonggang Cai, Dianbo Yu, Haihong Ye, Yuxin Department of Computer Science and Technology Jilin University China Key Laboratory of Symbol Computation and Knowledge Engineering Ministry of Education China Network Monitor Maintenance Centre of Jilin Branch China Telecom Corporation Limited Changchun Jilin China

ISBN: (纸本)9781424473120

To find an optimal elimination ordering for Bayesian networks, a multi-heuristic-based ant colony system named MHC-HS-ACS is proposed. MHC-HS-ACS uses a set of heuristics to guide the ants to search solutions. The heuristic set can evolve with the searching procedure in an adaptive way. MHC-HS-ACS also utilizes a heuristic-based local search to accelerate its convergence. computational experiments show that MHC-HS-ACS can find very high quality solutions. © 2010 IEEE.

关键词： Bayesian networks

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An efficient and effective similarity measure to enable data mining of fabric image of Xinjiang folk art

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Journal of Information and computational Science 2015年第5期12卷 1695-1702页

作者： Te, Rigen Li, Xiongfei Zhang, Yueying Key Laboratory of Symbol Computation and Knowledge Engineering Jilin University Changchun China College of Materials Science and Engineering Jilin University Changchun China

Fabric pattern is a folk art of artificial fabrics pattern design which includes apparels (known as clothes and hats), and crafts (known as carpets and tapestries). Although image processing, information retrieval and data mining have made a large impact on many human endeavors, they never had an impact on the study of the fabric image. In this paper we will present the reasons for this, and introduce a novel similarity measure and algorithm (Pattern Similarity Measuring Algorithm (PSMA)) which allows efficient data mining for large collections of fabric image. PSMA, the effectiveness of which is proved be experiment test, is an image similarity measurement algorithm that achieves the machine-learning based theory via the gradient descent algorithm with multidimensional linear regression. Copyright © 2015 Binary Information Press.

关键词： Data mining

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A dynamic time delay neural network for ultrasonic motor identification and control

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3rd International Symposium on Neural Networks, ISNN 2006 - Advances in Neural Networks

作者： Liang, Yanchun Zhang, Jie Xu, Xu Yang, Xiaowei Hao, Zhifeng Key Laboratory of Symbol Computation and Knowledge Engineering Jilin University Ministry of Education Changchun 130012 China College of Mathematics Jilin University Changchun 130012 China School of Mathematical Science South China University of Technology Guangzhou 510640 China

ISBN: (纸本)3540344373

A novel dynamic time delay neural network is proposed for ultrasonic motors identification and control in this paper. By introducing time delay neurons, the neural network identifier and controller of ultrasonic motors are constructed. Both of them are trained online by using an improved back-propagation algorithm. The usefulness and validity of the presented algorithm is examined by the experiments. © Springer-Verlag Berlin Heidelberg 2006.

关键词： Neural networks

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Novel measurement for mining effective association rules

Novel measurement for mining effective association rules

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Jin-Mao Wei Wei-Guo Yi Ming-Yang Wang Institute of Computational Intelligence Northeast Normal University Changchun Jilin China Open Symbol Computation and Knowledge Engineering Laboratory of State Education Department Jilin University China

In this paper, we analyze the method of support-confidence framework when mining association rules. In order to avoid the limitation in the criterion, we propose a new method of match as the substitution of confidence. We analyze in detail the property of the proposed measurement. Experimental results show that there is higher correlation between the antecedent and the consequent of the rules produced by the improved method compared with the rules produced by the support-confidence framework. Furthermore, the improved method decreases the generation of redundancy rules.

关键词： Data mining Association rules Frequency Transaction databases computational intelligence knowledge engineering Laboratories Electronic mail Merchandise Neural networks

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Effective and stable feature selection method based on filter for gene signature identification in paired microarray data

Effective and stable feature selection method based on filte...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Zhongbo Cao Yan Wang Ying Sun Wei Du Yanchun Liang College of Computer Science and Technology Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education Changchun China

A huge amount of microarray datasets are produced with big number of genes and small samples. Feature selection methods have become a very sharp tool to select the gene signatures from the whole gene set. In recent years, researchers are concerned much about the datasets containing samples of cancer as well as corresponding control tissues. However, few feature selection methods consider the effect of paired samples. In this article, we propose a new feature selection method for paired microarray datasets based on the original paired t-test approach. We apply on the paired datasets across six common cancer types. Through comparison with some widely used methods on the performance of prediction power, stability of gene lists and functional stability, our method shows excellent performance. The proposed method has good effectiveness, stability and consistency, which enables the method to be applicative to feature selection for paired microarray expression data analysis.

关键词： Stability analysis Cancer Accuracy Signal to noise ratio Gene expression Redundancy Liver

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An AutoEncoder-Based Matrix Factorization Approach to Estimating Cell Proportion from Bulk Tumor RNA-seq Data

An AutoEncoder-Based Matrix Factorization Approach to Estima...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yingze Xu Yan Wang Xuping Xie Feilong Wang Qiong Chen Huiyan Sun College of Computer Science and Technology Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun China College of Computer Science and Technology School of Artificial Intelligence Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun China School of Artificial Intelligence Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun China

ISBN: (纸本)9781665429825

The deconvolution of infiltrating immune cells and stromal cells from complex tumor tissues is significant for studying the impact of these cells on tumor development, as well as assisting cancer therapies. Integrating cell-specific marker genes from several references, we put forward an AutoEncoder-based matrix factorization method to estimate the cell proportion in heterogeneous samples. The proportion predicted by our method achieved over 0.95 Pearson correlation coefficient (PCC) with the ground truth. Moreover, through analyzing association between cell proportion of tumor tissues and clinical information of the tumor patients, we found that the proportion of cancer-associated fibroblast (CAF) gradually increased with the progress of tumor, and the proportion of B cell in tissues was significantly related to the five-year survival rate of tumor patients.

关键词： Head Deconvolution Medical treatment Melanoma Fibroblasts Neck Reliability

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Study on optimization of agent initial positions in land combat simulation

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自然科学进展(英文版) 2004年第3期14卷 257-261页

作者： WU Chunguo LIANG Yanchun LEE Heow Pueh LU Chun Yang Xiaowei College of Computer Science and Technology Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education Jilin University Changchun 130012 China Institute of High Performance Computing Singapore 117528 Singapore Institute of High Performance Computing Singapore 117528 Singapore Department of Applied Mathematics South China University of Technology Guangzhou 510640 China Centre for ACES Department of Mechanical Engineering National University of Singapore 119260 Singapore

The use of computational-intelligence-based techniques in the optimization of agent initial positions in land combat simulations is studied. A novel method for the reduction of support vectors in the support vector machine (SVM) is presented. The optimization on the width of the Gaussian kernel function and the combination of the SVM with the radial basis function neural network are performed in the proposed method. Simulation results show that the proposed method can improve the running efficiency drastically compared with that using the traditional SVM with the same precision. We also summarize and present some experiences and trends in the study on the optimization problem in land combat simulation.

关键词： multi-agent support vector machine radial basis function genetic algorithm regression

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