2019 International Conference on Machine Learning and Cybernetics (ICMLC)最新文献

{"title":"Using Feature Spatial Order in Progressive Image Feature Matching","authors":"C. Teng, Ben-Jian Dong","doi":"10.1109/ICMLC48188.2019.8949192","DOIUrl":"https://doi.org/10.1109/ICMLC48188.2019.8949192","url":null,"abstract":"Image feature matching is a very important and fundamental task in computer vision. In this paper, a spatial-order based progressive feature matching framework is proposed. With the model of spatial order, the searching space is partitioned into many intervals with each interval associated with a probability that a correct match is occurred in this interval. Using this information, many incorrect features could be filtered out and only the survived features are passed for subsequent matching. As the features are progressively matched, the model of spatial order is also progressively updated and the lengths of partitioned intervals are further shortened to filter out more features. To demonstrate the feasibility of proposed system, a series of experiments were conducted. A standard benchmark image data set was used to test the proposed system and the results showed that the proposed framework can indeed produce more efficient and accurate feature matching compared with traditional brute force technique.","PeriodicalId":221349,"journal":{"name":"2019 International Conference on Machine Learning and Cybernetics (ICMLC)","volume":"24 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":"123183934","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}

{"title":"Nanocarbon Electrode for Wearable Device With Flexible Material","authors":"Mai Kondo, T. Fujita, K. Kanda, K. Maenaka","doi":"10.1109/ICMLC48188.2019.8949223","DOIUrl":"https://doi.org/10.1109/ICMLC48188.2019.8949223","url":null,"abstract":"In this study, we aimed to develop a smart-cloth for workout. A flexible elastic electrode by using silicone rubber with special carbon black material, KETJENBLACK, was fabricated and tested. The flexible conductive electrode having an expansion ability of 100% or more was successfully fabricated. The compound of special carbon black, KETJENBLACK, can offer flexible electrodes and be suitable for wearable devices.","PeriodicalId":221349,"journal":{"name":"2019 International Conference on Machine Learning and Cybernetics (ICMLC)","volume":"18 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":"126974840","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}

{"title":"Short-Text Question Classification Based on Dependency Parsing and Attention Mechanism","authors":"An Fang","doi":"10.1109/ICMLC48188.2019.8949314","DOIUrl":"https://doi.org/10.1109/ICMLC48188.2019.8949314","url":null,"abstract":"Question texts analysis is a challenging task of the fine-grained classification due to the few annotation data and unbalanced categories. The existing approaches normally assume that each word contributes the same semantic to the question text, but ignore the different meanings of the words and the dependency relations within the text. In this paper, we propose a deep neural network with multi-layer attention mechanism to capture the extended semantic features by using a dependency parsing tree, which has the capacity to spot the central components of the question. The experimental results demonstrate that our proposed model obtains substantially improvement, comparing with several competitive baselines.","PeriodicalId":221349,"journal":{"name":"2019 International Conference on Machine Learning and Cybernetics (ICMLC)","volume":"59 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":"131885615","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}

{"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}

{"title":"Electricity Consumption Forecasting of Buildings Using Hierarchical ANFIS and GRA","authors":"Han-Yun Chen, Ching-Hung Le, Baolian Huang","doi":"10.1109/ICMLC48188.2019.8949177","DOIUrl":"https://doi.org/10.1109/ICMLC48188.2019.8949177","url":null,"abstract":"Because of the rise of environmental awareness, controlling and monitoring the electricity consumption become significant. The accuracy of the prediction of electricity consumption can directly influence the efficiency of power management. If the usage status of electricity can be predicted, it will be easy to discover if there is any unusual electricity consumption. The choice of suitable models or mathematic methods will be the essential of all. Adaptive network-based fuzzy inference system combines the concept of fuzzy and neural networks. It reserves the interpretability of fuzzy inference system and the learning ability of neural networks. We applied adaptive network-based fuzzy inference system (ANFIS) with hierarchical structure on electricity consumption prediction and grey relational analysis (GRA) on the influence of each input factors. The result showed that hierarchical ANFIS did achieve the purpose we set and GRA can effectively evaluate the magnitude of relation between factors and specific output.","PeriodicalId":221349,"journal":{"name":"2019 International Conference on Machine Learning and Cybernetics (ICMLC)","volume":"2 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":"131980691","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}

{"title":"Elderly Health Care System Based on High Precision Vibration Sensor","authors":"Shuai Shao, Jinseok Woo, Kouhei Yamamoto, N. Kubota","doi":"10.1109/ICMLC48188.2019.8949237","DOIUrl":"https://doi.org/10.1109/ICMLC48188.2019.8949237","url":null,"abstract":"In recent years, the aging population has become a major social problem. We hope to achieve health-care system for older persons through technical means. In this study, we developed an elderly health care system based on vibration sensors. By analyzing the vibrations of behavior such as walking and falling, the system can determine the current state of the elderly and send it to the robot. Experiments show that our system can estimate the behavior of the elderly with an accuracy of 89%, in which the accuracy of fall detection is 96%.","PeriodicalId":221349,"journal":{"name":"2019 International Conference on Machine Learning and Cybernetics (ICMLC)","volume":"65 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":"133879369","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}

{"title":"On Optimal Energy Consumption Control Method for Tail Fin Bionic Robotic Fish","authors":"Guihai Li, Gang Liu, Yu-Xuan Li, Song-Lin Chen","doi":"10.1109/ICMLC48188.2019.8949254","DOIUrl":"https://doi.org/10.1109/ICMLC48188.2019.8949254","url":null,"abstract":"The endurance ability is an important factor to be considered in the practical application of bionic robotic fish. By designing an optimal energy consumption control method, the energy consumption of robotic fish can be effectively reduced. In this paper, the structural characteristics of the tail fin bionic robotic fish are abstracted through the motion analysis of the tail fin fish. On this basis, a simplified dynamic and kinematic model of the robotic fish and a calculation method of energy consumption are established. Then, by changing the oscillation amplitude and frequency of the tail, the change law of the swimming speed is obtained. It is also found that the energy consumption is positively correlated with the swimming speed in general. In order to get the lowest energy consumption swimming mode of robotic fish at different swimming speeds, a series of optimal energy consumption points are obtained at the interval of 0.05m/s. The control method of optimal energy consumption of robotic fish is designed by analyzing its distribution law.","PeriodicalId":221349,"journal":{"name":"2019 International Conference on Machine Learning and Cybernetics (ICMLC)","volume":"7 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":"127878718","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}

{"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}

{"title":"A Development of a System to Measure Radioulnar Distance in Wrist-Joint Rotation Using Three-Dimensional Electromagnetic Sensor","authors":"K. Nagamune, Akito Nakano","doi":"10.1109/ICMLC48188.2019.8949284","DOIUrl":"https://doi.org/10.1109/ICMLC48188.2019.8949284","url":null,"abstract":"For sports such as baseball and tennis, there are actions to throw the ball and swing the racket. There are cases injured the wrist joint by repeating this action. One such injury to the wrist joint is triangular fibrocartilage complex (TFCC) injury. TFCC is a part that keeps stability on the ulnar side of the wrist joint scale. So, if the TFCC is injured, the distance between the ulna and the radius will widen due to the wrist rotation, when the injury is severe, pain occurs on the ulnar side of the wrist joint. In the current diagnosis, there is no diagnosis to evaluate the change in distance between the ulna and the radius in the wrist rotation. Therefore, in this study, to quantitatively evaluate the change of distance between the ulna and the radius in TFCC injury, we develop a system to measure the distance between the ulna and the radius in the wrist rotation.","PeriodicalId":221349,"journal":{"name":"2019 International Conference on Machine Learning and Cybernetics (ICMLC)","volume":"27 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":"124966343","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}

{"title":"Phase Retrieval via Wirtinger Flow Algorithm and Its Variants","authors":"Jian-wei Liu, Zhi Cao, Jing Liu, Xiong-lin Luo, Wei-min Li, Nobuyasu Ito, Longteng Guo","doi":"10.1109/ICMLC48188.2019.8949170","DOIUrl":"https://doi.org/10.1109/ICMLC48188.2019.8949170","url":null,"abstract":"Almost three-quarters of the underling information in the light wave field is embodied in the phase. However, the early optical detectors can only record the intensity or amplitude of the light wave field and cannot directly extract the phase information of the light wave field. Therefore, it is necessary to use the measured amplitude or strength to reconstruct the phase information of the object, this problem is denoted phase retrieval. Phase retrieval is a matter of cardinal significance in signal processing and machine learning. The phase retrieval by convex optimization algorithm is ideal but the computational complexity is high. In 2015, Candès proposed a very effective non-convex optimization algorithm-Wirtinger flow algorithm which used spectral initialization to get a better initial value and then gradient iteration to get a promised recovery effect. Subsequently, in line with the idea, a large number of variants are devised, such as: Wirtinger flow(WF), Truncated Wirtinger Flow (TWF), Truncated Amplitude Flow (TAF), Reshaped Wirtinger Flow (RWF), Incremental Truncated Wirtinger Flow (ITWF), Incremental Reshaped Wirtinger Flow (IRWF), Robust Wirtinger Flow (Robust-WF), Sparse Wirtinger Flow (SWF), Median-TWF, Median-RWF, Generalized Wirtinger Flow (GWF), Accelerated Wirtinger Flow (AWF), Thresholded Wirtinger Flow Revisited (THWFR), Thresholded Wirtinger Flow (THWF), Reweighted Wirtinger Flow (REWF), Wirtinger Flow Method With Optimal Stepsize (WFOS), Stochastic Truncated Wirtinger Flow Algorithm (STWF), Stochastic Truncated Amplitude Flow (STAF), Reweighted Amplitude Flow (RAF), Compressive Reweighted Amplitude Flow (CRAF), SPARse Truncated Amplitude flow (SPARTA) and Sparse Wirtinger Flow Algorithm with Optimal Stepsize (SWFOS), etc. This paper analyzes and summarizes these algorithms according to their characteristics such as: initialization method, step size, iteration times, sample complexity, computational complexity, etc., so that readers can intuitively and clearly see the characteristics of each algorithm. Finally, we provide the website of the source code of some algorithms, facilitate to access and use it for readers.","PeriodicalId":221349,"journal":{"name":"2019 International Conference on Machine Learning and Cybernetics (ICMLC)","volume":"17 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":"125432289","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}