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6th IEEE Advanced Information Technology, Electronic and Automation Control Conference (IEEE IAEAC)

作者： Shao, Guowei Li, Maohai Lin, Rui Zhou, Yipeng Soochow Univ Sch Elect & Mech Engn Suzhou Peoples R China Key Lab Database & Parallel Comp Heilongjiang Pro Harbin Peoples R China

ISBN: (数字)9781665458641

ISBN: (纸本)9781665458641

To solve the problem that the low accuracy and the excessive drift in large distance of the laser odometry of the three-dimensional laser SLAM (simulta-neous localization and mapping) algorithm, the point cloud matching adopts the multi-metric linear least square ICP (mulls-ICP) algorithm. Firstly, the ground segmentation is realized based on the dual-threshold ground filter, then the fusion of IMU and RTK based on error state Kalman filter (ESKF) provides accurate initial value for point cloud matching, Finally, the laser odometry is optimized based on the multi-metric linear least squares ICP algorithm. The experimental results based on the standard datasets KITTI and MVSEC datasets show that compared with the classic ALOAM and LIO-SAM algorithms, the proposed algorithm has higher positioning accuracy and better mapping effect. compared with LIO-SAM algorithm, the error is reduced by fifty to sixty percent.

关键词： laser odometry error state kalman filter multi-metric linear least square ICP algorithm ground segmentation

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A Multi-task Learning Framework with Features Based on Behavioral Pattern Conversation 20th

A Multi-task Learning Framework with Features Based on Behav...

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20th EAI International Conference on Mobile and Ubiquitous Systems - computing Networking and Services (EAI MobiQuitous)

作者： Tang, Bo Wang, Nan Li, Jinbao Shen, Zhonghui Heilongjiang Univ Coll Comp Sci & Technol Harbin Peoples R China Heilongjiang Univ Key Lab Database & Parallel Comp Harbin Peoples R China Qilu Univ Technol Shandong Artificial Intelligence Inst Jinan Peoples R China Qilu Univ Technol Sch Math & Stat Jinan Peoples R China

ISBN: (纸本)9783031639913;9783031639920

Deep neural network based multi-task learning has been widely successful in many real-world large scale applications, such as recommendation systems. A large number of commodity transaction data from e-commerce platforms show that users often go through a series of behavioral transitions such as impressed. click. add shopping cart before finally forming a purchase behavior, and only very few users click and then make a purchase directly. This phenomenon precisely follows the objective reality of power-law distribution. Based on this, this paper proposes a Multi-task learning framework with features based on Behavioral Pattern Conversation (BPCM). A feature tower model based on attribute information is constructed in the framework, which is able to control the fusion and screening process of features adaptively through the designed novel gate complementary mechanism. In addition, we designed several submodules with behavioral pattern conversation (BPC) applied to multi-task learning. The class of modules is not only able to adaptively model the sequentiality and dependencies between behavioral task transitions through information transfer, but also to effectively control the amount of information transferred between different tasks. Adequate experiments show that our BPCM obtains higher performance compared to more current advanced multitask learning frameworks.

关键词： Multi-task Learning Behavioral Pattern Conversation Recommendation System

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Effective Community Detection Algorithm Based on Edge Influence Weight 2022

Effective Community Detection Algorithm Based on Edge Influe...

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6th International Conference on Innovation in Artificial Intelligence (ICIAI)

作者： Wang, Chang Yang, Yan Heilongjiang Univ Sch Comp Sci & Technol Harbin Peoples R China Heilongjiang Univ Sch Comp Sci & Technol Heilongjiang Prov Key Lab Database & Parallel Com Harbin Peoples R China

ISBN: (纸本)9781450395502

Connections strength between nodes are fundamental and important components in social networks, and connection strength determines the community structure of the network to a large extent. Edge weight is a meaningful representative of connection strength or data credibility, which can be applied to social network analysis. Aiming at the problems of insufficient research on the relationship between nodes and unreasonable initial selection of community centers, a community detection algorithm based on edge influence weight (CDP-EW) was proposed in this research. Specifically, to solve the initial community center selection problem, the degree centrality of nodes was used to calculate node influence. Then, the edge influence weight was redefined to calculate similarity based on the link relationships between nodes. Moreover, CDP-EW was compared with some community detection algorithms on real complex network datasets in experiments, where the proposed algorithm performed well on complex networks.

关键词： Data mining Social network analysis Community detection Graph clustering

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NKMH: A Neural Efficient Recommendation Based on Neighborhood key Information Aggregation of Modified Hawkes 46

NKMH: A Neural Efficient Recommendation Based on Neighborhoo...

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46th Annual IEEE-computer-Society International computers, Software, and Applications Conference (compSAC) - computers, Software, and Applications in an Uncertain World

作者： Xu, Xin Wang, Nan Jin, Hu Liu, Yong Li, Kun Heilongjiang Univ Sch Comp Sci & Technol Key Lab Database & Parallel Comp Heilongjiang Pro Harbin Peoples R China Heilongjiang Univ Sch Comp Sci & Technol Postdoctoral Program Heilongjiang Hengxun Technol Co Ltd Harbin Peoples R China

ISBN: (纸本)9781665488105

The rapid development of neural networks has contributed to the increasing maturity of recommendation systems. However, deep neural networks have poor interpretability for models and do not show strong advantages for sparse data and noisy data. Recently, Hawkes process has become more and more focused for its good interpretability with probabilistic models. Based on this, we proposes A Neural Efficient Recommendation Model Based on Neighborhood key Information Aggregation of Modified Hawkes(NKMH). The model utilizes a neural network and designs three modules to jointly fit the modified Hawkes process. It not only inherits the high interpretability of Hawkes, but also effectively solves the problem of poor prediction ability of the Hawkes process. Besides, we present a novel key information search strategy(KISS), which can effectively remove the noise in a session and alleviate the sparsity of the data to some extent. Extensive experiments on two datasets show that the NKMH model outperforms many current popular models.

关键词： hawkes interpretability key information

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A Sleep Stage Classification Method via Combination of Time and Frequency Domain Features based on Single-Channel EEG 19

A Sleep Stage Classification Method via Combination of Time ...

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19th IEEE International Symposium on parallel and Distributed Processing with Applications (IEEE ISPA)

作者： Zhao, Caihong Neng, Wenpeng Heilongjiang Univ Coll Comp Sci & Technol Harbin Peoples R China Heilongjiang Univ Coll Elect Engn Heilongjiang Key Lab Database & Parallel Comp Harbin Peoples R China

ISBN: (纸本)9781665435741

Sleep staging is an important method to diagnose and treat insomnia, sleep apnea, and other sleep disorders. compared with the multi-channel automatic sleep staging system, the single-channel EEG signal contains less information, and the traditional single analysis domain feature parameter extraction algorithm cannot meet the requirement of sleep stage classification accuracy. To solve this problem, we propose an automatic sleep staging method based on the combination of time-domain and frequency-domain features based on single-channel EEG signals. Empirical mode decomposition is used to decompose EEG signals in the time domain to obtain the decomposed signals at different time scales. Multiple local features are extracted from each decomposed signal. The frequency-domain features of EEG signals are obtained by using the frequency domain decomposition of EEG signals in various rhythms. The time-domain and frequency-domain decomposition features are combined into eigenvectors and selected for sleep staging. The experimental results show that the sleep staging method proposed in this paper with time-frequency domain features of single-cyhannel EEG signals can approach the accuracy of sleep staging of multi-channel signals on the same data set, and superior to the sleep staging method with the same single-channel EEG signals.

关键词： sleep stage classification Empirical Mode Decomposition frequency-domain decomposition time-frequency domain features

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CASR: A Collaborative Attention Model for Session-based Recommendation 45

CASR: A Collaborative Attention Model for Session-based Reco...

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45th Annual International IEEE-computer-Society computers, Software, and Applications Conference (compSAC)

作者： Han, Peiyao Wang, Nan Li, Kun Li, Xiaokun Heilongjiang Univ Sch Comp Sci & Technol Key Lab Database & Parallel Comp Heilongjiang Pro Harbin Peoples R China Heilongjiang Univ Heilongjiang Hengxun Technol Co Ltd Sch Comp Sci & Technol Postdoctoral Program Harbin Peoples R China Heilongjiang Hengxun Technol Co Ltd Postdoctoral Program Harbin Peoples R China

ISBN: (纸本)9781665424639

Today the technological society is developing quickly, recommendation system is becoming an increasingly important technology. Session-based recommendation systems have become a hot research topic due to their ability to provide recommendations for anonymous users. Traditional session-based recommendation systems have some limitations. They either lack the ability to learn complex dependencies or only focus on the current session without explicitly considering collaborative information. We propose a collaborative attention for session-based recommendation model called CASR, which can mine users' long-term and short-term preferences to obtain users' real intention by using the close interaction relationship in time structure. We first model a Gate Recurrent Unit (GRU) framework with attention mechanism to obtain short-term preferences of users in a period of the time. Then, we propose a collaborative session search strategy and design a neighbor session search algorithm. It can not only obtain users'long-term preferences, but also alleviate the sparseness of the original session data to a certain extent. Finally, we use the capsule network with update strategy to get better prediction results. Extensive experiments on two real datasets show that our CASR model outperforms many mainstream methods.

关键词： session-based recommendation collaborative information capsule network

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A Reliable Localization Algorithm Based on Grid Coding and Multi-Layer Perceptron

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IEEE ACCESS 2020年 8卷 60979-60989页

作者： Sun, Zhengtang Zhang, Yang Ren, Qianqian Key Lab Database & Parallel Comp Harbin 150080 Heilongjiang Peoples R China Heilongjiang Univ Sch Elect & Engineer Harbin 150080 Peoples R China Heilongjiang Univ Chinese Med Affliated Hosp 1 Harbin 150040 Peoples R China

The traditional RSS-based fingerprint localization algorithm needs RSS values from all access points (AP) at each reference point (RP). In the large-scale indoor environment, the increasing of the number of APs will lead to establish a large-scale fingerprint database, which occupies a lot of storage space. In this paper, we propose a new reliable localization algorithm, which firstly utilizes quantized RSS to encode the monitoring region which has been divided into grids, so as to specify the grids that the interested target appears roughly. Then, we utilize Multi-Layer Perceptron (MLP) to train the grid regions in which the beacons deployment is non-isomorphic and obtain the accurate localization result. Due to the same deployment of isomorphic regions, it is imperative to train only one model to replace the others, which greatly reduces the computation of neural network. It can be concluded from the experimental results that compared with the traditional MLP-based fingerprint localization algorithm, the proposed algorithm reduces the size of fingerprint database over 80% with guarantee of localization accuracy. Moreover, our algorithm can obtain better localization accuracy compared with the other latest quantization based localization algorithm.

关键词： Grid coding multi-layer perceptron indoor localization

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Design of Radio-over-Fiber Distributed Antenna System and Application of Wireless Positioning 4

Design of Radio-over-Fiber Distributed Antenna System and Ap...

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4th IEEE Information Technology, Networking, Electronic and Automation Control Conference (ITNEC)

作者： Yun, Hao Ji, Wei Yin, Rui ShanDong Univ Sch Informat Sci & Engn Qingdao Peoples R China ShanDong Univ Key Lab Database & Parallel Comp Heilongjiang Prov Lab Intelligent Commun & Multim Qingdao Peoples R China

ISBN: (纸本)9781728143903

In this paper, a radio-over-fiber distributed antenna system (ROF-DAS) and application of wireless positioning are proposed. The modified Medium Access Control (MAC) frame, optical switch array, ROF network based on star topology and Reflective Semiconductor Optical Amplifier (RSOA) are employed to design the system. Based on the structure, wireless positioning could be achieved by Time Division Multiplexing-Time Difference of Arrival (TDM-TDOA) technology combined with Chan assisted Taylor algorithm. The quality of signal transmission is evaluated by Q Factor, and the positioning accuracy is performed in terms of the antennas amount using the positioning algorithm.

关键词： Distributed antenna system (DAS) radio over fiber (ROI) Time Difference of Arrival (TDOA) wireless positioning Chan Taylor Q Factor

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A Binary Code Sequence Based Tracking Algorithm in Wireless Sensor Networks 19th

A Binary Code Sequence Based Tracking Algorithm in Wireless ...

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19th International Conference on Algorithms and Architectures for parallel Processing (ICA3PP)

作者： Zhang, Yang Ren, Qianqian Pan, Yu Li, Jinbao Heilongjiang Univ Key Lab Database & Parallel Comp Heilongjiang Pro Harbin Peoples R China Heilongjiang Univ Key Lab Elect Engn Coll Heilongjiang Prov Harbin Peoples R China Heilongjiang Univ Sch Comp Sci & Technol Harbin Peoples R China

ISBN: (纸本)9783030389611;9783030389604

This paper proposes a binary code sequence based tracking algorithm in wireless sensor network. The proposed algorithm can release the influence of sensed data on localization results via building the map between target's occurrence region and a binary code sequence. To solve the ambiguity problem existing in occurrence region determination, the paper further gives a Voronoi diagram based location refinement algorithm. The simulation results show the tracking results under difference trajectories.

关键词： Target localization RSSI Voronoi

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Using Gaussian mixture model to fix errors in SFS approach based on propagation

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INTERNATIONAL JOURNAL OF compUTATIONAL SCIENCE AND ENGINEERING 2019年第4期19卷 570-580页

作者： Huang, Wenmin Ma, Jiquan Heilongjiang Univ Coll Comp Sci & Technol Harbin Heilongjiang Peoples R China Heilongjiang Univ Coll Comp Sci & Technol Key Lab Database & Parallel Comp Heilongjiang Pro 74 R Xuefu Nangang Dist Harbin 150080 Heilongjiang Peoples R China

A new Gaussian mixture model is used to improve the quality of propagation method for SFS in this paper. The improved algorithm can overcome most difficulties of propagation SFS method including slow convergence, interdependence of propagation nodes and error accumulation. For slow convergence and interdependence of propagation nodes, stable propagation source and integration path are used to make sure that the reconstruction work of each pixel in the image is independent. A Gaussian mixture model based on prior conditions has been proposed to fix the error of integration. Good result has achieved in the experiment for Lambert composite image of front illumination.

关键词： shape from shading propagation method silhouette Gaussian mixture model surface reconstruction

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