2019 International Conference on Machine Learning and Cybernetics (ICMLC)最新文献_第9页

Using Feature Spatial Order in Progressive Image Feature Matching 基于特征空间顺序的渐进图像特征匹配

2019 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2019-07-01 DOI: 10.1109/ICMLC48188.2019.8949192

C. Teng, Ben-Jian Dong

引用次数: 0

Posture Estimation Method Using Cushion Type Seat Pressure Sensor 基于坐垫式座椅压力传感器的姿态估计方法

2019 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2019-07-01 DOI: 10.1109/ICMLC48188.2019.8949190

T. Takeda

引用次数: 0

Multimodal Representation Learning: Advances, Trends and Challenges 多模态表示学习:进展、趋势和挑战

2019 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2019-07-01 DOI: 10.1109/ICMLC48188.2019.8949228

Sufang Zhang, Jun-Hai Zhai, Bo-Jun Xie, Yan Zhan, Xin Wang

{"title":"Multimodal Representation Learning: Advances, Trends and Challenges","authors":"Sufang Zhang, Jun-Hai Zhai, Bo-Jun Xie, Yan Zhan, Xin Wang","doi":"10.1109/ICMLC48188.2019.8949228","DOIUrl":"https://doi.org/10.1109/ICMLC48188.2019.8949228","url":null,"abstract":"Representation learning is the base and crucial for consequential tasks, such as classification, regression, and recognition. The goal of representation learning is to automatically learning good features with deep models. Multimodal representation learning is a special representation learning, which automatically learns good features from multiple modalities, and these modalities are not independent, there are correlations and associations among modalities. Furthermore, multimodal data are usually heterogeneous. Due to the characteristics, multimodal representation learning poses many difficulties: how to combine multimodal data from heterogeneous sources; how to jointly learning features from multimodal data; how to effectively describe the correlations and associations, etc. These difficulties triggered great interest of researchers along with the upsurge of deep learning, many deep multimodal learning methods have been proposed by different researchers. In this paper, we present an overview of deep multimodal learning, especially the approaches proposed within the last decades. We provide potential readers with advances, trends and challenges, which can be very helpful to researchers in the field of machine, especially for the ones engaging in the study of multimodal deep machine learning.","PeriodicalId":221349,"journal":{"name":"2019 International Conference on Machine Learning and Cybernetics (ICMLC)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134044045","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: 8

An Automated Identification Tool for LC-MS Based Metabolomics Studies 基于LC-MS的代谢组学研究的自动鉴定工具

2019 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2019-07-01 DOI: 10.1109/ICMLC48188.2019.8949193

Ke-Shiuan Lynn, Chun-Ju Chen, C. Tseng, M. Cheng, Wen-Harn Pan

{"title":"An Automated Identification Tool for LC-MS Based Metabolomics Studies","authors":"Ke-Shiuan Lynn, Chun-Ju Chen, C. Tseng, M. Cheng, Wen-Harn Pan","doi":"10.1109/ICMLC48188.2019.8949193","DOIUrl":"https://doi.org/10.1109/ICMLC48188.2019.8949193","url":null,"abstract":"Liquid chromatography/mass spectrometer (LC/MS) has become one of the most popular analytical platform for metabolomics studies owing to its wide range of detectable polarity and molecular mass. However, metabolite identification remains quite costly and time-consuming in LC/MS-based metabolomics, mostly due to lower database integrity and a separated MS/MS spectra generation process. In this work, we constructed an automated, user-friendly, and freely available tool. From a peak list, the tool first groups peaks, which are usually associated with a metabolite, based on their retention time and abundance correlation across samples. In each group, different ions are annotated and the mass of the underlying metabolite is derived. Finally, the fragments are used to match with low-energy MS/MS spectra in public databases for metabolite identification. To identify metabolites without accessible MS/MS spectra, we have developed characteristic fragment and common substructure matches. Through the above approach, we anticipate facilitating the metabolite identification in LC-MS-based metabolomics studies.","PeriodicalId":221349,"journal":{"name":"2019 International Conference on Machine Learning and Cybernetics (ICMLC)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126396501","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

Multi-Nozzle Pneumatic Extrusion Based Additive Manufacturing System for Fabricating a Sandwich Structure with Soft and Hard Material 基于多喷嘴气动挤出的软硬材料夹层结构增材制造系统

2019 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2019-07-01 DOI: 10.1109/ICMLC48188.2019.8949242

Kai-Wei Chen, M. Tsai

{"title":"Multi-Nozzle Pneumatic Extrusion Based Additive Manufacturing System for Fabricating a Sandwich Structure with Soft and Hard Material","authors":"Kai-Wei Chen, M. Tsai","doi":"10.1109/ICMLC48188.2019.8949242","DOIUrl":"https://doi.org/10.1109/ICMLC48188.2019.8949242","url":null,"abstract":"The additive manufacturing is an intelligent manufacturing technology that can quickly build a variety of complex objects with single or different functional materials. If additive manufacturing technology can be used to print mechanical structure with sensing or electronic feature, it will be able to break through the development bottleneck of a smart gripper and achieve the goal of rapid industrial development. In this study, a multi-nozzle pneumatic extrusion additive manufacturing system for printing soft and hard material structure was developed. The structure is made of a multi-material polymer which can be fabricated by using 3D printing machine. The liquid material is extruded through a tiny nozzle and then cured by a UV lighting source. The system architecture includes a CNC controller, which controls the nozzle through two stepping motors, both positive and negative pressures and curing light source are also manipulated with peripheral I/Os. A DA controller is also applied to flexibly control the air pressure for requirement of different injected flow speed. The program part is automatically executed with a numerical control software in CNC and PLC. Different pressures were set for extrusion nozzles with different materials. The G-code data was processed by Python Language and sent to the multi-nozzle pneumatic extrusion additive manufacturing system. This paper successfully printed a sandwich pad with soft and hard material structure, including double-layer material pad and three-layer material pad. A finer printing performance than a traditional FDM machine is achieved.","PeriodicalId":221349,"journal":{"name":"2019 International Conference on Machine Learning and Cybernetics (ICMLC)","volume":"33 7-8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131978695","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: 4

Electricity Consumption Forecasting of Buildings Using Hierarchical ANFIS and GRA 基于分层ANFIS和GRA的建筑用电量预测

2019 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2019-07-01 DOI: 10.1109/ICMLC48188.2019.8949177

Han-Yun Chen, Ching-Hung Le, Baolian Huang

引用次数: 0

Elderly Health Care System Based on High Precision Vibration Sensor 基于高精度振动传感器的老年医疗保健系统

2019 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2019-07-01 DOI: 10.1109/ICMLC48188.2019.8949237

Shuai Shao, Jinseok Woo, Kouhei Yamamoto, N. Kubota

引用次数: 2

Short-Text Question Classification Based on Dependency Parsing and Attention Mechanism 基于依赖解析和注意机制的短文本问题分类

2019 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2019-07-01 DOI: 10.1109/ICMLC48188.2019.8949314

An Fang

引用次数: 4

Performance Evaluation of a Mobile Deice System Using Fuzzy Logic Control with Multi-Hop in a Multi-Radio Opportunistic Network 基于模糊逻辑控制的多跳移动设备系统在多无线电机会网络中的性能评价

2019 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2019-07-01 DOI: 10.1109/ICMLC48188.2019.8949293

Young-Long Chen, Neng-Chung Wang, Jing-Fong Ciou, Gun-Wen Xiao, Yi-Shang Liu, Pin-Lun Huang

引用次数: 0

Cost-Sensitive Feature Selection Based on Label Significance and Positive Region 基于标签显著性和正区域的代价敏感特征选择

2019 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2019-07-01 DOI: 10.1109/ICMLC48188.2019.8949182

Jintao Huang, Wenbin Qian, Binglong Wu, Yinglong Wang

引用次数: 2