2021 2nd International Conference on Artificial Intelligence and Computer Engineering (ICAICE)最新文献

Application of Varint_LZW encoding algorithm in image compression Varint_LZW编码算法在图像压缩中的应用

2021 2nd International Conference on Artificial Intelligence and Computer Engineering (ICAICE) Pub Date : 2021-11-01 DOI: 10.1109/ICAICE54393.2021.00049

Yi Wei, Jianfei Zhang, Ying Tang

引用次数: 0

An Gradient-based Continuous-Time Trajectory Optimization method for Real-Time Collision Avoidance on collaborative robot 基于梯度的协同机器人实时避碰连续轨迹优化方法

2021 2nd International Conference on Artificial Intelligence and Computer Engineering (ICAICE) Pub Date : 2021-11-01 DOI: 10.1109/icaice54393.2021.00015

Bo Liu, S. Wei

引用次数: 1

A Deep Learning Method for Pneumonia Detection Based on Fuzzy Non-Maximum Suppression 一种基于模糊非最大值抑制的肺炎检测深度学习方法

2021 2nd International Conference on Artificial Intelligence and Computer Engineering (ICAICE) Pub Date : 2021-11-01 DOI: 10.1109/icaice54393.2021.00026

Hongli Wu, Mingzhu Ping, Huijuan Lu, Wenjie Zhu

引用次数: 0

Discriminative Feature Extraction and Enhancement Network for Low-Light Image 弱光图像判别特征提取与增强网络

2021 2nd International Conference on Artificial Intelligence and Computer Engineering (ICAICE) Pub Date : 2021-11-01 DOI: 10.1109/ICAICE54393.2021.00158

Jiazhen Zu, Yongxia Zhou, Le Chen, Chao Dai

{"title":"Discriminative Feature Extraction and Enhancement Network for Low-Light Image","authors":"Jiazhen Zu, Yongxia Zhou, Le Chen, Chao Dai","doi":"10.1109/ICAICE54393.2021.00158","DOIUrl":"https://doi.org/10.1109/ICAICE54393.2021.00158","url":null,"abstract":"Photos taken in low light conditions will cause a series of visual degradation phenomena due to underexposure, such as low brightness, loss of information, noise and color distortion. In order to solve the above problems, a discriminative feature extraction and enhancement network is proposed for low-light image enhancement. First, the shallow features are extracted by Inception V2,and the deep features are further extracted by the residual module. Then, the shallow and deep features are fused, and the fusion results are input into the discriminative feature enhancement module for enhancing. Specifically, the residual channel attention module is introduced after each stage to capture important feature information, which helps to restore the color of low-light images and reduce artifacts. Finally, the brightness adjustment module is used to adjust the brightness of the image. In addition, a hybrid loss function is designed to measure the loss of model training from multiple levels. The experimental results on the LOL-v2 dataset show that the proposed algorithm can reduce noise while improving image brightness, reduce color distortion and artifacts, and is superior to other related algorithms in objective indicators. The result maps are more real and natural in subjective vision.","PeriodicalId":388444,"journal":{"name":"2021 2nd International Conference on Artificial Intelligence and Computer Engineering (ICAICE)","volume":"17 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":"129052587","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 Wearable Fall Detection System Based on 1D CNN 基于1D CNN的可穿戴跌倒检测系统

2021 2nd International Conference on Artificial Intelligence and Computer Engineering (ICAICE) Pub Date : 2021-11-01 DOI: 10.1109/ICAICE54393.2021.00046

Peng Liu, Julong Pan, Hailiang Zhu, Yanli Li

{"title":"A Wearable Fall Detection System Based on 1D CNN","authors":"Peng Liu, Julong Pan, Hailiang Zhu, Yanli Li","doi":"10.1109/ICAICE54393.2021.00046","DOIUrl":"https://doi.org/10.1109/ICAICE54393.2021.00046","url":null,"abstract":"The current wearable fall detection systems mostly use threshold method with long-distance communication such as 3G/4G or machine learning algorithm with short-distance communication such as Bluetooth and Wi-Fi. But the former method has the problem of low algorithm accuracy, and the latter has the problem of short transmission distance. In order to solve these problems, an Arduino Nano 33 BLE development board with built-in accelerometer sensor is introduced. A deep learning model trained by 1D CNN (one-dimensional convolutional neural network) is trained offline firstly and transformed into a suitable model for the above development board using TensorFlow Lite. After deployment of a fall detection algorithm in an embedded terminal, the model has improved the fall detection accuracy. The inertial data is collected and normalized firstly and used as input data set for 1D CNN. The fall detection result and GPS data will be uploaded to the cloud using the NB-IoT (Narrow Band Internet of Things), and a warning message will be sent to the relative person. The fall accuracy of the above training model reached 98.85%, and the sensitivity and specificity were 98.86% and 99.84%, respectively.","PeriodicalId":388444,"journal":{"name":"2021 2nd International Conference on Artificial Intelligence and Computer Engineering (ICAICE)","volume":"32 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":"125163817","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

Classification and Identification of Domestic Catsbased on Deep Learning 基于深度学习的家猫分类与识别

2021 2nd International Conference on Artificial Intelligence and Computer Engineering (ICAICE) Pub Date : 2021-11-01 DOI: 10.1109/ICAICE54393.2021.00029

Rui Zhang

引用次数: 1

ECG Signal Detection via Bidirectional Projection Clustering-enhanced Hashing 基于双向投影聚类增强哈希的心电信号检测

2021 2nd International Conference on Artificial Intelligence and Computer Engineering (ICAICE) Pub Date : 2021-11-01 DOI: 10.1109/ICAICE54393.2021.00134

Xiaoyun Yi, Wenrui Lv, Li Qi, Panpan Zhang, Yixian Fang, Yuwei Ren

{"title":"ECG Signal Detection via Bidirectional Projection Clustering-enhanced Hashing","authors":"Xiaoyun Yi, Wenrui Lv, Li Qi, Panpan Zhang, Yixian Fang, Yuwei Ren","doi":"10.1109/ICAICE54393.2021.00134","DOIUrl":"https://doi.org/10.1109/ICAICE54393.2021.00134","url":null,"abstract":"Hashing has been extremely regarded in the field of machine vision due to its fast query ability and lightweight storage. Whereas, the binary discrete constraint invariably disturbs the optimization of various bit allocation algorithms. This article engineers a Bidirectional Projection Clustering-enhanced Hashing (BPCH) framework which dexterously avoids the binary optimization and can generate binary codes without any iteration. Concretely, BPCH leverages clustering to generate pseudo tags and class centers and takes their binary dissimilarity as the calibration, and then conducts Kronecker product with the real semantic affinity to generate a binary matrix, which is then rearranged according to the semantic tags to generate the final hash encodes. The whole process not only avoids the problem of binary optimization, but also does not involve any iteration, thus improving the robustness of hash book and reducing the impact of semantic information noise. Furthermore, a bidirectional projection hashing is constructed to link the raw data space and the latent Hamming space, thus providing a directly practicable hash function for out-of-sample data. Experimental results on two ECG data sets show its superiority over the current ECG signal retrieval algorithm.","PeriodicalId":388444,"journal":{"name":"2021 2nd International Conference on Artificial Intelligence and Computer Engineering (ICAICE)","volume":"1 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":"129227180","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

CNN Implementation on Major Skin Cancer Types Classification and NLP Diagnose Robot System 主要皮肤癌类型分类和NLP诊断机器人系统的CNN实现

2021 2nd International Conference on Artificial Intelligence and Computer Engineering (ICAICE) Pub Date : 2021-11-01 DOI: 10.1109/ICAICE54393.2021.00028

Yujia Guo, Zijian Ye, Xizheng Yu, Yuze Zhao

引用次数: 0

Modeling Neural Networks Training Process with Markov Decision Process 用马尔可夫决策过程建模神经网络训练过程

2021 2nd International Conference on Artificial Intelligence and Computer Engineering (ICAICE) Pub Date : 2021-11-01 DOI: 10.1109/ICAICE54393.2021.00102

Yan Bai, Wanwei Liu, Xinjun Mao, Zhenwei. Liang

引用次数: 0

Analysis of Volatility Characteristics of Chinese and American Financial Markets Based on SV Model 基于SV模型的中美金融市场波动特征分析

2021 2nd International Conference on Artificial Intelligence and Computer Engineering (ICAICE) Pub Date : 2021-11-01 DOI: 10.1109/ICAICE54393.2021.00097

Hao Yang, Yongmei Ding, Xuan Zhang

引用次数: 0