2022 4th International Conference on Intelligent Information Processing (IIP)最新文献_第4页

Box Straight Line Detection Method Based on Two PCA and DBSCAN 基于双PCA和DBSCAN的箱形直线检测方法

2022 4th International Conference on Intelligent Information Processing (IIP) Pub Date : 2022-10-01 DOI: 10.1109/IIP57348.2022.00037

Xiao Li, Zhong Xu, Heping Peng, Hong-Bing Wang, Qingdan Huang

{"title":"Box Straight Line Detection Method Based on Two PCA and DBSCAN","authors":"Xiao Li, Zhong Xu, Heping Peng, Hong-Bing Wang, Qingdan Huang","doi":"10.1109/IIP57348.2022.00037","DOIUrl":"https://doi.org/10.1109/IIP57348.2022.00037","url":null,"abstract":"With the rapid development of modern logistics technology, intelligent forklifts have become an important part of intelligent logistics. Intelligent forklifts based on computer vision are developing continuously. In order to adapt to the working environment of boxed and stored goods, this paper designs a linear detection algorithm combining computer vision and machine learning, so as to apply it to intelligent forklifts to identify boxed goods. position and contour and perform access work. This paper proposes twice principal component analysis(TPCA), and uses TPCA to remove obvious noise and detect straight lines, and combines DBSCAN clustering algorithm to classify its data and do twice principal component analysis. The work of this paper is mainly to perform straight line detection on the edge contour of the boxed goods in the warehousing environment. First, a model for identifying the contour box of the boxed goods is trained in Faster-RCNN, and then the rough outline of the boxed goods identified by the model is extracted. RoI, and perform straight line detection on the data in the RoI. Experiments show that the recognition accuracy in the manually collected pictures is 89.10%, of which the correct box recognition rate is 97.91%, and the picture accuracy rate in the normal environment is 91.61%, of which the correct box recognition rate is 98.40%.","PeriodicalId":412907,"journal":{"name":"2022 4th International Conference on Intelligent Information Processing (IIP)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132970923","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Indoor and Outdoor Joint Location Algorithm for Power Smart Supply Chain Park 电力智能供应链园区室内外联合定位算法

2022 4th International Conference on Intelligent Information Processing (IIP) Pub Date : 2022-10-01 DOI: 10.1109/IIP57348.2022.00051

Youtao Jiang, Lei Hao, Xiaoyuan Han, Hongxin Tian

引用次数: 0

An Ant Colony Optimization Algorithm with Variable Neighborhood Search for Vehicle Routing Problems with Time Windows on Road Networks 带时间窗道路网络车辆路径问题的变邻域蚁群优化算法

2022 4th International Conference on Intelligent Information Processing (IIP) Pub Date : 2022-10-01 DOI: 10.1109/iip57348.2022.00071

Mingde Liu, Yingbin Zhang, Ling Li, Zhiming Yang

引用次数: 0

Research on multi-hop reasoning question and answer model for foodborne disease incidents 食源性疾病事件多跳推理问答模型研究

2022 4th International Conference on Intelligent Information Processing (IIP) Pub Date : 2022-10-01 DOI: 10.1109/IIP57348.2022.00048

Yuntao Shi, Yi-Xian Bai, Zhang Tao, Wei-Chuan Liu

{"title":"Research on multi-hop reasoning question and answer model for foodborne disease incidents","authors":"Yuntao Shi, Yi-Xian Bai, Zhang Tao, Wei-Chuan Liu","doi":"10.1109/IIP57348.2022.00048","DOIUrl":"https://doi.org/10.1109/IIP57348.2022.00048","url":null,"abstract":"This paper addresses the problem that there is little research on question-and-answer technical reasoning in the field of food safety and the difficulty of mining deep information for related reasoning. This paper proposes a multi-hop reasoning model for food safety incident knowledge graphs. Firstly, a knowledge graph triad network is established by extracting and embedding the knowledge related to food accidents. Secondly, a two-module system is established by combining the two-channel theory. Module 1 adopts the attention mechanism to calculate the weight coefficients on the relational edges and expand them, and module 2 adopts the GAT neural network to infer and calculate the hidden representations of entities, and the inference prediction of the tail entities is achieved through the interactive iteration of module 1 and module 2. Experiments show that after iterative training of the model on food safety data, the correct answer can be inferred with good accuracy. The multi-hop based food safety incident inference model is highly accurate and interpretable, and can be applied to the question and answer system to assist relevant personnel to have a quick query and determination on the causes, influencing factors, causative factors and their characteristics, pathogenic mechanisms and clinical manifestations of food safety incidents to reduce the occurrence of incidents.","PeriodicalId":412907,"journal":{"name":"2022 4th International Conference on Intelligent Information Processing (IIP)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133160690","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Fraud Web URL Detection Based on Bi-LSTM 基于Bi-LSTM的欺诈Web URL检测

2022 4th International Conference on Intelligent Information Processing (IIP) Pub Date : 2022-10-01 DOI: 10.1109/iip57348.2022.00068

Xiuqing Ji, Huawei Song, F. Wan, Kaizhan Huang

引用次数: 0

Research on P2P Default Risk Prediction Based on Logistic Regression 基于Logistic回归的P2P违约风险预测研究

2022 4th International Conference on Intelligent Information Processing (IIP) Pub Date : 2022-10-01 DOI: 10.1109/iip57348.2022.00036

Taoning Zhang, Wenhao Sun

{"title":"Research on P2P Default Risk Prediction Based on Logistic Regression","authors":"Taoning Zhang, Wenhao Sun","doi":"10.1109/iip57348.2022.00036","DOIUrl":"https://doi.org/10.1109/iip57348.2022.00036","url":null,"abstract":"With the rapid development of the Internet, a variety of traditional industries use information technology and Internet platform closely derived from a variety of emerging industries. In the financial field, traditional banks cannot meet the needs of small and medium-sized enterprises with rapid growth. Thanks to the mature development of Internet platforms, various P2P(Peer to Peer Lending) platforms have emerged as the times require. They provide a simple reception approval process and provide investors and borrowers with convenient lending channels. However, with the rapid development of the P2P industry, P2P has frequent security problems. The main reason for this phenomenon is that the personal credit evaluation of the borrower is inaccurate, which leads to a chain reaction. Therefore, the effective estimation of the credit data of the borrower is the key to solving the credit risk. With the rapid development of machine learning, various risk prevention and control models are more and more accurate in predicting personal credit. However, default data accounts for only a small part of the massive credit data, so it is particularly important to process the data set, especially to balance the sample data. Traditionally, SMOTE(Synthetic Minority Oversampling Technique) algorithm is generally used for oversampling of data. However, when there are noise samples in the auxiliary samples of the samples synthesized by this algorithm, the new samples may become the noise sample interference model. In this paper, the Borderline-SMOTE algorithm is used to oversample the data features. The results show that the model trained by the processed data set is better than the control model, both in accuracy and recall rate.","PeriodicalId":412907,"journal":{"name":"2022 4th International Conference on Intelligent Information Processing (IIP)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125470558","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Design and Implementation of a Radio Frequency Fingerprint Identification System 射频指纹识别系统的设计与实现

2022 4th International Conference on Intelligent Information Processing (IIP) Pub Date : 2022-10-01 DOI: 10.1109/iip57348.2022.00015

Haonan Zeng, Yuelei Xie

引用次数: 1

Monte Carlo Simulation for Inertia Moment Measurement Test of Seaplane Model 水上飞机模型惯性矩测量试验的蒙特卡罗模拟

2022 4th International Conference on Intelligent Information Processing (IIP) Pub Date : 2022-10-01 DOI: 10.1109/IIP57348.2022.00041

Sheng-zhe Shi, Ting Jiang, Fan Wang, Xiao-long Zheng

引用次数: 0

Study on Prediction Mechanism of High-speed Traffic Accidents Based on Space-Time Meteorological Grid 基于时空气象网格的高速交通事故预测机理研究

2022 4th International Conference on Intelligent Information Processing (IIP) Pub Date : 2022-10-01 DOI: 10.1109/iip57348.2022.00038

W. Zhang, Jun Lu, Min Zhao, Lei Liang, Ran Tang, Jiakai Peng

{"title":"Study on Prediction Mechanism of High-speed Traffic Accidents Based on Space-Time Meteorological Grid","authors":"W. Zhang, Jun Lu, Min Zhao, Lei Liang, Ran Tang, Jiakai Peng","doi":"10.1109/iip57348.2022.00038","DOIUrl":"https://doi.org/10.1109/iip57348.2022.00038","url":null,"abstract":"Based on the traffic accident data of the Yaxi section of the G5 Beijing-Kunming high-speed from 2018 to 2021 and the observation data of the national meteorological station corresponding to the accident occurrence point provided by the Sichuan Meteorological Office, this paper studies the prediction mechanism of the high-speed traffic accident by using the Kmeans cluster analysis, space-time grid coding and multiple logistic regression model in SPSS algorithm. In this paper, the model analysis and prediction mechanism of highway traffic meteorological disaster in western Sichuan Plateau region are studied by using data resources from meteorological and transportation departments. In this paper, a fine intelligent grid space-time model is used to improve the granularity of model analysis and prediction. Sections of meteorological elements, this article will research traffic accident data, regression analysis on the data collection using smart grid model of time and space, with meteorological elements as the emphasis, build the traffic accident of time, space, space and time on the grid data model, aiming at key points in traffic accident risk accident and the regression analysis between grid meteorological elements of time and space, Thus the traffic meteorological risk prediction model is established.","PeriodicalId":412907,"journal":{"name":"2022 4th International Conference on Intelligent Information Processing (IIP)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123117801","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A temporal knowledge graphs prediction method for community gas risk 社区燃气风险的时间知识图预测方法

2022 4th International Conference on Intelligent Information Processing (IIP) Pub Date : 2022-10-01 DOI: 10.1109/IIP57348.2022.00012

Lei Zhao, Yuntao Shi, Zhang Tao, Meng Zhou

引用次数: 0