2019 Eleventh International Conference on Advanced Computational Intelligence (ICACI)最新文献_第2页

Parameter Identification, Verification and Simulation of the CSD Transport Process CSD运输过程的参数辨识、验证及模拟

2019 Eleventh International Conference on Advanced Computational Intelligence (ICACI) Pub Date : 2019-06-07 DOI: 10.1109/ICACI.2019.8778465

Wei Li, Feixin Wang, Jianqing Jiang

引用次数: 2

Structural Modeling of Road Network and Probability Calculation of Vehicle Trajectory 路网结构建模与车辆轨迹概率计算

2019 Eleventh International Conference on Advanced Computational Intelligence (ICACI) Pub Date : 2019-06-07 DOI: 10.1109/ICACI.2019.8778457

Xianbin Zeng, Meiling Wu, Yunguo Lin, Yongxian Wen

引用次数: 0

A New Learning Scheme of Emotion Recognition From Speech by Using Mean Fourier Parameters 基于平均傅立叶参数的语音情感识别新学习方案

2019 Eleventh International Conference on Advanced Computational Intelligence (ICACI) Pub Date : 2019-06-07 DOI: 10.1109/ICACI.2019.8778548

Xingyu Chen, Li-Jiao Wu, Aihua Mao, Zhi-hui Zhan

{"title":"A New Learning Scheme of Emotion Recognition From Speech by Using Mean Fourier Parameters","authors":"Xingyu Chen, Li-Jiao Wu, Aihua Mao, Zhi-hui Zhan","doi":"10.1109/ICACI.2019.8778548","DOIUrl":"https://doi.org/10.1109/ICACI.2019.8778548","url":null,"abstract":"Recently, the research attention of emotional speech signals has been boosted in human machine interfaces due to the availability of high computation capability. Based on different feature extraction on audio data, it is possible to achieve good accuracy on speech emotion recognition, thus feature extraction plays an important role in speech emotion recognition. However, there are still dilemmas in speech emotion recognition, such as the heavy computation burden due to the high data dimension. In this paper, we propose a new learning scheme with mean Fourier parameters using the perceptual content of voice quality for speaker-independent speech emotion recognition. The dimension of the acoustic feature is greatly reduced and the computational performance is improved with big extent. Two speech databases (German emotional corpus, Interactive Emotional Dyadic Motion Capture) are used in the experiment, and the combination of different features with different classifiers are implemented in the recognition for performance comparison. The recognition results show that the proposed scheme with mean Fourier Parameters combined with the Random Forest classifier is efficient in classifying various emotional states in speech signals and is excellent than other features and classifiers.","PeriodicalId":213368,"journal":{"name":"2019 Eleventh International Conference on Advanced Computational Intelligence (ICACI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130300924","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: 2

Adaptive Correlation Filtering Algorithm for Video Target Tracking based on Multi Feature Fusion 基于多特征融合的视频目标跟踪自适应相关滤波算法

2019 Eleventh International Conference on Advanced Computational Intelligence (ICACI) Pub Date : 2019-06-07 DOI: 10.1109/ICACI.2019.8778454

Yifei Fan, Zhouding Zhao

{"title":"Adaptive Correlation Filtering Algorithm for Video Target Tracking based on Multi Feature Fusion","authors":"Yifei Fan, Zhouding Zhao","doi":"10.1109/ICACI.2019.8778454","DOIUrl":"https://doi.org/10.1109/ICACI.2019.8778454","url":null,"abstract":"In recent years, the target tracking algorithm based on the correlation filter has become a hot topic in the field of target tracking with its excellent performance in tracking precision and tracking speed. Aiming at problem of the insufficient accuracy of complex scene image and video image data, a multi feature fusion adaptive correlation filtering algorithm for video image tracking is proposed. In this work, 36 groups of color video sequences in the tracking benchmark database (OTB-2013) are utilized as samples. Firstly, histogram of oriented gradient (HOG) and color name (CN) are used to extract the two complementary features from video sequences. Then these features are used to train correlation filters and according to the complementarity of features, the response graph of two correlation filters is weighted together to effectively for tracking the image targets. After that, the confidence level of response graph and the intra-frame variation rate are calculated to dynamically adjust the learning rate and update the parameters of two correlation filters. Finally, the scale adaptive estimation algorithm is introduced to achieve scale adaptive tracking of targets. The experimental results from OTB-2013 tracking datum database show that the multi feature fusion adaptive correlation filter is suitable for complex scene video image data and the accuracy and speed of automatic tracking is improved.","PeriodicalId":213368,"journal":{"name":"2019 Eleventh International Conference on Advanced Computational Intelligence (ICACI)","volume":"164 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132789855","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: 1

Detectability and Identifiability Analysis of Bad Data in Current Reckoning Method 电流推算法中不良数据的可检测性和可识别性分析

2019 Eleventh International Conference on Advanced Computational Intelligence (ICACI) Pub Date : 2019-06-07 DOI: 10.1109/ICACI.2019.8778481

Zhang Na, Yang Gang Wang Dawe

引用次数: 0

Drawing Order Recovery based on deep learning 基于深度学习的图单恢复

2019 Eleventh International Conference on Advanced Computational Intelligence (ICACI) Pub Date : 2019-06-07 DOI: 10.1109/ICACI.2019.8778533

Rui Zhang, Jinlong Chen, Minghao Yang

引用次数: 2

Object Localization and Segmentation Using Hybrid Features and Fuzzy Classifiers With a Small Training Set from an RGB-D Camera 基于RGB-D相机小训练集的混合特征和模糊分类器的目标定位和分割

2019 Eleventh International Conference on Advanced Computational Intelligence (ICACI) Pub Date : 2019-06-01 DOI: 10.1109/ICACI.2019.8778523

Huai-An Lin, Tzu-Ting Tseng, Chia-Feng Juang, G. Chen

引用次数: 1

Landslide Prediction Based on Multiple Inducing Factors 基于多诱发因素的滑坡预测

2019 Eleventh International Conference on Advanced Computational Intelligence (ICACI) Pub Date : 2019-06-01 DOI: 10.1109/ICACI.2019.8778620

Li Zhou, Ying Zhu, Shanwen Guan, Xiyan Sun, Xiaonan Luo

{"title":"Landslide Prediction Based on Multiple Inducing Factors","authors":"Li Zhou, Ying Zhu, Shanwen Guan, Xiyan Sun, Xiaonan Luo","doi":"10.1109/ICACI.2019.8778620","DOIUrl":"https://doi.org/10.1109/ICACI.2019.8778620","url":null,"abstract":"China is a country with frequent landslide disasters, and the Three Gorges Reservoir area is a landslide disaster-prone area and a serious disaster area. GPS surface displacement monitoring is an important means of landslide stability monitoring. In this paper, we present a novel landslide prediction method based on multiple inducing factors. Firstly, stepwise regression analysis is applied to obtain dominant inducing factors of the landslide. The inducing factors will be processed one by one: the one with significant impact will be retained while the others will be eliminated. Then, each inducing factor will be decomposed by CEEMDAN method, and the components with less influence are eliminated by the gray correlation analysis method. This paper takes the Shuping landslide in the Three Gorges reservoir area as an example. the ELM model is optimized by genetic algorithm, and then the induced factors of optimization are used as input of the model. The experimental results show that the prediction error of the model is relatively small, and the fitting coefficient reaches 0.98. The proposed model has a good effect on landslide prediction.","PeriodicalId":213368,"journal":{"name":"2019 Eleventh International Conference on Advanced Computational Intelligence (ICACI)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121071391","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

Location Algorithm for WSNs with Mobile Anchor Node Based on Optimzed K-Nearest Neighbers 基于最优k近邻的移动锚节点wsn定位算法

2019 Eleventh International Conference on Advanced Computational Intelligence (ICACI) Pub Date : 2019-06-01 DOI: 10.1109/ICACI.2019.8778540

Huijiao Wang, Kuilin Lyu, Hua Jiang, Yao Wu, Q. Yue, Qing Zhao

{"title":"Location Algorithm for WSNs with Mobile Anchor Node Based on Optimzed K-Nearest Neighbers","authors":"Huijiao Wang, Kuilin Lyu, Hua Jiang, Yao Wu, Q. Yue, Qing Zhao","doi":"10.1109/ICACI.2019.8778540","DOIUrl":"https://doi.org/10.1109/ICACI.2019.8778540","url":null,"abstract":"Aiming at the problem of low accuracy of node location in the local scope, a location algorithm for wireless sensor network with a mobile anchor node was proposed based on the improved k-nearest neighbor classification algorithm. The algorithm used K most similar reference nodes to calculate the coordinates of the unknown nodes. The location of the reference node is important. The Received Signal Strength Indication relative value between the unknown node and the reference node was acquired by using chi-square distance optimization. The Fisher criterion is used to select the reference nodes with the strong ability within the communication scope of unknown nodes and evaluate the error. The different weights are assigned to the reference nodes distribution, and the reference nodes with low signal intensity are deleted, and the reference nodes with shortest distance is the best. The proposed algorithm optimizes the selection of reference nodes. Experimental results show that the positioning accuracy is optimized by 14.54% with a smaller error distribution range compared with the K-Nearest Neighbor positioning algorithm.","PeriodicalId":213368,"journal":{"name":"2019 Eleventh International Conference on Advanced Computational Intelligence (ICACI)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127841370","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: 3

Parallelization of a Self-adaptive Harmony Search Algorithm on Graphics Processing Units 图形处理单元上自适应和谐搜索算法的并行化

2019 Eleventh International Conference on Advanced Computational Intelligence (ICACI) Pub Date : 2019-06-01 DOI: 10.1109/ICACI.2019.8778491

Yin-Fu Huang, SunHo Cho

引用次数: 0