2019 IEEE 18th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)最新文献_第5页

Towards Automatic Recognition of Sounds Observed in Daily Living Activity 对日常生活活动中所观察到的声音的自动识别

2019 IEEE 18th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC) Pub Date : 2019-07-01 DOI: 10.1109/ICCICC46617.2019.9146067

A. Shaukat, Ammar Younis, M. Akram, M. Mohsin, Zartasha Mustansar

引用次数: 0

A High Resolution ADC Model Combined Pipeline and Sigma -Delta Oversampling Architecture 一种结合流水线和Sigma -Delta过采样结构的高分辨率ADC模型

2019 IEEE 18th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC) Pub Date : 2019-07-01 DOI: 10.1109/ICCICC46617.2019.9146045

Wei Ding, Tao Wu, Chengwang Liao, Junchang Wang, Yong Jiang

{"title":"A High Resolution ADC Model Combined Pipeline and Sigma -Delta Oversampling Architecture","authors":"Wei Ding, Tao Wu, Chengwang Liao, Junchang Wang, Yong Jiang","doi":"10.1109/ICCICC46617.2019.9146045","DOIUrl":"https://doi.org/10.1109/ICCICC46617.2019.9146045","url":null,"abstract":"Data acquisition system is a key instrument to convert analog signals to digital signals in geophysics. To match high resolution sensors in seismometers systems, high resolution analog-digital conversion (ADC) is deployed in data acquisition system. Classical high resolution ADC models are based on architecture of Σ-Δ oversampling or pipeline ADC architecture separately. Those ADC architectures have following problems: increase power consumption, reduce linearity of modulators, depend on design of complex circuit, and the resolution is not easily regulated. To solve those problems and promote resolution of ADC, a novel architecture design is presented, which design principle is based on mathematical formulations combined advantages both previous pipeline and oversampling ADC architecture. A simple and mathematical combined ADC analysis model is presented. Using this model, this paper analyzes theoretically the various sources of noise of the adverse effects of the whole ADC architecture. Based on an assessment of noise simulation algorithm, an amended theoretical model is proposed. The results represent that noises level of integrator and subtractor in first level of combined model determine the whole performance. By controlling the noise level from these two components to less than 10−7V/✓Hz, the resolution of whole data acquisition system can achieve reservation resolution of 150 dB.","PeriodicalId":294902,"journal":{"name":"2019 IEEE 18th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)","volume":"36 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":"131236753","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

Dynamic Path Optimization for Robot Route Planning 机器人路径规划的动态路径优化

2019 IEEE 18th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC) Pub Date : 2019-07-01 DOI: 10.1109/ICCICC46617.2019.9146050

Ying Huang, Yingxu Wang, Omar A. Zatarain

引用次数: 0

Unique Dragonfly Optimization Algorithm for Harvesting and Clustering the Key Features 独特的蜻蜓关键特征采集与聚类优化算法

2019 IEEE 18th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC) Pub Date : 2019-07-01 DOI: 10.1109/ICCICC46617.2019.9146092

Nagaraju Devarakonda, S. Anandarao, Raviteja Kamarajugadda, Yingxu Wang

{"title":"Unique Dragonfly Optimization Algorithm for Harvesting and Clustering the Key Features","authors":"Nagaraju Devarakonda, S. Anandarao, Raviteja Kamarajugadda, Yingxu Wang","doi":"10.1109/ICCICC46617.2019.9146092","DOIUrl":"https://doi.org/10.1109/ICCICC46617.2019.9146092","url":null,"abstract":"In many applications, the feature selection plays an important role, as best feature can bring out the accurate results. The features selected must represent the entire dataset. Here we have considered “Sequential Forward Selection” for feature extraction and used refined dragonfly algorithm to approach and to migrate from the best and worst features respectively. We improvised the conventional dragonfly algorithm by adding the convergence and fitness functions. To access the accuracy of the algorithm we introduced the fitness function. This paper has discussed about the general hunting behaviour of the dragonfly and dragonfly algorithm (DA) with convergence and fitness functions. A comparative study was shown for the best search agent position between modified DA and traditional DA, at the same time test function values of refined dragonfly algorithm (RDA) is compared with whale optimization algorithm (WOA) and Tornadogenesis Optimization algorithm (TOA). We have evaluated refined DA on the 23 benchmark function corresponding values are shown in experiment.","PeriodicalId":294902,"journal":{"name":"2019 IEEE 18th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)","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":"121294593","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

Fracability Evaluation in Deep Shale Reservoirs Based on a Fuzzy Grey Correlation Analysis Method 基于模糊灰色关联分析法的深层页岩储层可压性评价

2019 IEEE 18th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC) Pub Date : 2019-07-01 DOI: 10.1109/ICCICC46617.2019.9146080

Hong Liu, Yuanyuan Huang, Xiaoyu Zhang, Zujie Bie, Jiayue Gu, Long He

{"title":"Fracability Evaluation in Deep Shale Reservoirs Based on a Fuzzy Grey Correlation Analysis Method","authors":"Hong Liu, Yuanyuan Huang, Xiaoyu Zhang, Zujie Bie, Jiayue Gu, Long He","doi":"10.1109/ICCICC46617.2019.9146080","DOIUrl":"https://doi.org/10.1109/ICCICC46617.2019.9146080","url":null,"abstract":"Fracability is used to evaluate if it's easy to form complex fracture networks by the volumetric fracturing in shale gas or not. The main parameters of shale gas fracability evaluation methods at home and abroad are analyzed and summarized, combining the characteristics of high temperature, high pressure and strong plasticity in deep shale gas layers, the comprehensive evaluation parameters of deep shale reservoirs fracability based on the geological and engineering factors are optimized. Fully considering the effects of formation confining pressure and rock anisotropy on parameters, the calculation method of fracability evaluation parameters is formed. Using fuzzy grey correlation analysis method, the correlation between the fracability index curve of different weight combinations and the fracture complexity index curve is analyzed. According to the maximum principle of correlation coefficient, the weight combination is optimized, the comprehensive evaluation mathematical model of fracability is established, and the comprehensive evaluation method of deep shale gas fracability is formed. It is of guiding significance to the optimization design of fracturing parameters for deep shale gas to improve fracture complexity.","PeriodicalId":294902,"journal":{"name":"2019 IEEE 18th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)","volume":"76 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":"123822159","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

Ample Probability in Cognition 认知中的充分概率

2019 IEEE 18th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC) Pub Date : 2019-07-01 DOI: 10.1109/ICCICC46617.2019.9146027

M. Burgin, P. Rocchi

引用次数: 5

AI and Us: Existential Risk or Transformational Tool? 人工智能与人类:生存风险还是转型工具?

2019 IEEE 18th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC) Pub Date : 2019-07-01 DOI: 10.1109/ICCICC46617.2019.9146091

N. Saavedra-Rivano

引用次数: 0

Awareness of Crime-related Information and Concealed Information Detection method 犯罪相关信息意识与隐藏信息侦查方法

2019 IEEE 18th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC) Pub Date : 2019-07-01 DOI: 10.1109/ICCICC46617.2019.9146100

Qi Liu, Hongguang Liu, Lei Zhang

{"title":"Awareness of Crime-related Information and Concealed Information Detection method","authors":"Qi Liu, Hongguang Liu, Lei Zhang","doi":"10.1109/ICCICC46617.2019.9146100","DOIUrl":"https://doi.org/10.1109/ICCICC46617.2019.9146100","url":null,"abstract":"Concealed information test based on P300 and machine learning has become increasingly popular in the fields of cognitive psychology. Numerous studies have set up mock crime scenario to identify changes in EEG cognitive components. However, only two kinds of subjects are taken into account in most previous studies. Therefore, if an innocent person had been aware of case-related information, i.e., probe items, which is likely to happen in practice, recognition capability will be significantly compromised. In order to simulate practical cases, three kinds of subjects needed to be discriminated, including guilty, innocent and informed. 36 subjects went through a mock crime scenario, and EEG signals obtained on 8 electrodes were analyzed. After preprocessing, the discrete wavelet packet decomposition was used to extract EEG features. Subsequently, a multi-scale wavelet kernel extreme learning machine classifier is proposed to recognize the group to which a specific subject belongs. To further reduce computation, Cholesky decomposition is introduced during the calculation of the output weights. Our results demonstrate that the proposed algorithm can achieve good recognition performance and has low computational burden.","PeriodicalId":294902,"journal":{"name":"2019 IEEE 18th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)","volume":"11 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":"132562361","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

Towards Evolving Symbiotic Cognitive Education Based on Digital Twins 基于数字孪生的进化共生认知教育

2019 IEEE 18th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC) Pub Date : 2019-07-01 DOI: 10.1109/ICCICC46617.2019.9146095

W. Kinsner, R. Saracco

引用次数: 5

Reinforcement Learning using Kalman Filters 使用卡尔曼滤波器的强化学习

2019 IEEE 18th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC) Pub Date : 2019-07-01 DOI: 10.1109/ICCICC46617.2019.9146066

Kei Takahata, T. Miura

引用次数: 2