2021 International Conference on Signal Processing and Machine Learning (CONF-SPML)最新文献_第3页

Application of Tableau in Visual Analysis Data of a US Supermarket Sales Tableau在美国某超市销售数据可视化分析中的应用

2021 International Conference on Signal Processing and Machine Learning (CONF-SPML) Pub Date : 2021-11-01 DOI: 10.1109/CONF-SPML54095.2021.00044

Yutao Li

引用次数: 3

A Generative Adversarial Network-based Framework for Fruit and Vegetable Occlusion Detection in Smart Refrigerators 基于生成对抗网络的智能冰箱果蔬遮挡检测框架

2021 International Conference on Signal Processing and Machine Learning (CONF-SPML) Pub Date : 2021-11-01 DOI: 10.1109/CONF-SPML54095.2021.00063

Yuting Zhou, Linze Shi, Bo Yuan

引用次数: 2

Bayesian Inference in Census-House Dataset 人口普查数据集的贝叶斯推断

2021 International Conference on Signal Processing and Machine Learning (CONF-SPML) Pub Date : 2021-11-01 DOI: 10.1109/CONF-SPML54095.2021.00061

Hui-Chu Shu

引用次数: 0

Research and Analysis of Obstacle Avoidance Path Planning for Autonomous Vehicles 自动驾驶汽车避障路径规划研究与分析

2021 International Conference on Signal Processing and Machine Learning (CONF-SPML) Pub Date : 2021-11-01 DOI: 10.1109/CONF-SPML54095.2021.00050

Lv Qinyang

引用次数: 1

Design of Photoelectric Signal Parameter Test System for Liquid Crystal Filters 液晶滤波器光电信号参数测试系统的设计

2021 International Conference on Signal Processing and Machine Learning (CONF-SPML) Pub Date : 2021-11-01 DOI: 10.1109/CONF-SPML54095.2021.00014

Bangqi Guo, Chujiao Peng

{"title":"Design of Photoelectric Signal Parameter Test System for Liquid Crystal Filters","authors":"Bangqi Guo, Chujiao Peng","doi":"10.1109/CONF-SPML54095.2021.00014","DOIUrl":"https://doi.org/10.1109/CONF-SPML54095.2021.00014","url":null,"abstract":"The system studied in this study is specifically used for the measurement of the optical transmittance of liquid crystal filters. In practical applications, liquid crystal filters are mainly used in the production of welding masks. Therefore, the optical transmittance of the liquid crystal filter is an important parameter of the entire liquid crystal material, which is of great significance to the performance of the welding mask products. This study is aimed at the optical transmittance measurement system with white light as the light source. The system include s a light source, an integrating sphere, and a luxmeter. The core point of this system is that the photodetector detects the electrical signal at the opening on the side of the integrating sphere and compares it with the standard illuminance meter. The light intensity measured at the front opening is subjected to a fitting calibration, so that the detected optical signal measured at the opening on the side of the integrating sphere represents the light intensity at the front opening of the integrating sphere. The equipment needs to be measured in a dark room. The test results prove that the low collimation of the light source has a certain impact on the experimental results. The test results show that after debugging, the light signal detected at the opening on the side of the integrating sphere can accurately represent the light intensity measured by the standard illuminance meter at the front opening.","PeriodicalId":415094,"journal":{"name":"2021 International Conference on Signal Processing and Machine Learning (CONF-SPML)","volume":" 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132187724","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 Multi-source Based Healthcare Method for Heart Disease Prediction by Machine Learning 一种基于机器学习的多源心脏病预测方法

2021 International Conference on Signal Processing and Machine Learning (CONF-SPML) Pub Date : 2021-11-01 DOI: 10.1109/CONF-SPML54095.2021.00036

Shuying Shen

引用次数: 2

Application of CLIP on Advanced GAN of Zero-Shot Learning CLIP在高级GAN零射击学习中的应用

2021 International Conference on Signal Processing and Machine Learning (CONF-SPML) Pub Date : 2021-11-01 DOI: 10.1109/CONF-SPML54095.2021.00052

Peize Li

{"title":"Application of CLIP on Advanced GAN of Zero-Shot Learning","authors":"Peize Li","doi":"10.1109/CONF-SPML54095.2021.00052","DOIUrl":"https://doi.org/10.1109/CONF-SPML54095.2021.00052","url":null,"abstract":"In recent years, deep learning models have achieved world-renowned achievements in the fields of image, speech and text recognition. However, the insufficient amount of labeled data has brought serious problems, and it is also difficult to identify unseen classes well. Therefore, if we want to achieve perfect recognition of unseen classes, we need to perform zero-shot learning. In order to solve the zero-shot learning problem, a better solution can be obtained by using the semantic space method. Zero-shot learning attempts to classify unseen data after learning the seen data. In this case, it is one of the most difficult learning methods to achieve perfect recognition. CLIP uses a data set of 400 million data pairs, resulting in higher efficiency and better robustness. Using the features obtained by traditional RESNET neural network and CLIP, two advanced methods, F-CLSWGAN and TF-VAEGAN, were tested. Through ZSL and GZSL experiments, excellent results have been achieved and the effectiveness of the combined method has been verified. This paper has tested the good effect of the application of CLIP on ZSL and GZSL. The experimental results show that CLIP has excellent performance on the AWA2 data set, whether it is using F-CLSWGAN or TF-VAEGAN. Among them, the effect of TF-VAEGAN is better.","PeriodicalId":415094,"journal":{"name":"2021 International Conference on Signal Processing and Machine Learning (CONF-SPML)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128538500","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

Stock Return Prediction using Financial News: A Unified Sequence Model based on Hierarchical Attention and Long-Short Term Memory Networks 利用财经新闻预测股票收益:一种基于分层注意和长短期记忆网络的统一序列模型

2021 International Conference on Signal Processing and Machine Learning (CONF-SPML) Pub Date : 2021-11-01 DOI: 10.1109/CONF-SPML54095.2021.00034

Haoling Chen, Peng Liu

{"title":"Stock Return Prediction using Financial News: A Unified Sequence Model based on Hierarchical Attention and Long-Short Term Memory Networks","authors":"Haoling Chen, Peng Liu","doi":"10.1109/CONF-SPML54095.2021.00034","DOIUrl":"https://doi.org/10.1109/CONF-SPML54095.2021.00034","url":null,"abstract":"Stock return prediction has been a hot topic in both research and industry given its potential for large financial gain. The return signal, apart from its inherent volatility and complexity, is often accompanied by a multitude of noises, such as other stocks’ performance, macroeconomic factors and financial news, etc. To better characterize these factors, we propose a new model that consists of two levels of sequence: an NLP-based module to capture the sequential nature of words and sentences in the financial news, and a time-series-based module to exploit the sequential nature of adjacent observations in the stock price. In this proposed framework, we employ Hierarchical Attention Networks (HAN) in the text mining module, which could effectively model the financial news and extract important signals at both word and sentence level. For the time series module, the established Long-Short Term Memory (LSTM) network is used to model the complex serial dependence in the time series data. We compare with benchmark models using either module alone, as well as other alternatives using the traditional Bag of Words (BOW) approach, based on the Dow Jones Industrial Average (DJIA) dataset. Experiment results show that our proposal method performs better in several classification metrics for both positive and negative stock returns.","PeriodicalId":415094,"journal":{"name":"2021 International Conference on Signal Processing and Machine Learning (CONF-SPML)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126662760","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

Challenges in Visual Navigation of AGV and Comparison Study of Potential Solutions AGV视觉导航面临的挑战及解决方案的比较研究

2021 International Conference on Signal Processing and Machine Learning (CONF-SPML) Pub Date : 2021-11-01 DOI: 10.1109/CONF-SPML54095.2021.00058

Y. Pan

引用次数: 1

Building a Chinese Slang Sentiment Lexicon Using Online Crowdsourcing Dictionaries 利用网络众包词典构建汉语俚语情感词典

2021 International Conference on Signal Processing and Machine Learning (CONF-SPML) Pub Date : 2021-11-01 DOI: 10.1109/CONF-SPML54095.2021.00026

Binjun Jiang

引用次数: 0