2016 Third International Conference on Artificial Intelligence and Pattern Recognition (AIPR)最新文献_第3页

Application of intelligence of swarm in architecture 群智能在建筑中的应用

2016 Third International Conference on Artificial Intelligence and Pattern Recognition (AIPR) Pub Date : 2016-09-01 DOI: 10.1109/ICAIPR.2016.7585202

Jan Petrš

{"title":"Application of intelligence of swarm in architecture","authors":"Jan Petrš","doi":"10.1109/ICAIPR.2016.7585202","DOIUrl":"https://doi.org/10.1109/ICAIPR.2016.7585202","url":null,"abstract":"Last two decades some architects have used tools, which are based on nature principles. Thanks to globalization and multidisciplinary, contemporary architecture uses principles based on computing design, engineering, sociology or nature systems. Implementation of these principles brings new possibilities to architecture and design and new ways how to develop it. This paper focuses on intelligence of swarm (SI) in architecture and describes several principles and examples which are using intelligence of nature systems like ants, birds, locusts etc. Contemporary Architecture should count with many changes which are still in progress. It is very difficult to predict, what will be tomorrow. Using of the dynamic principles in architecture, design and civil engineering could help us to be ready for these changes. Like ant colony or schooling of fish can adapt on non-predicted changes, also city or building should be capable of this. This is not useful just for dynamic and kinetic architecture, but also for finding a form of static building or city. In this paper several SI algorithms and four types of SI use in architecture are described: space optimization, swarm urbanism, swarm modelling and swarm robotics.","PeriodicalId":127231,"journal":{"name":"2016 Third International Conference on Artificial Intelligence and Pattern Recognition (AIPR)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130806833","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

Dyadic lifting wavelet based signal detection 基于二进提升小波的信号检测

2016 Third International Conference on Artificial Intelligence and Pattern Recognition (AIPR) Pub Date : 2016-09-01 DOI: 10.1109/ICAIPR.2016.7585219

K. Kuzume, T. Tabusa

{"title":"Dyadic lifting wavelet based signal detection","authors":"K. Kuzume, T. Tabusa","doi":"10.1109/ICAIPR.2016.7585219","DOIUrl":"https://doi.org/10.1109/ICAIPR.2016.7585219","url":null,"abstract":"Local regularities of a signal contain important information such as edges in an image and QRS complexes in an Electrocardiogram (ECG). In order to detect such local regularities in the signal, wavelet transform has been focused on as a powerful tool for signal processing applications. Wavelet maxima at the time in which the signal abruptly changes are usually large in amplitude. However, with only the magnitude of the wavelet maxima the features of the signal cannot be known in detail. Mallat et al. proposed the Lipchitz regularity for observing signal cross scales in multiresolution signal analysis, but its computational cost was relatively expensive. This paper presents a novel method for signal detection using lifting dyadic wavelet transform, which has the time-invariant property. The lifting wavelet parameters contained in Swelden's formula were tuned, adapting them to the signals to be detected. The method for tuning these parameters was to learn the features of the target signals in the multiresolution analysis. To evaluate our methods we applied them to detect the QRS complexes contained in an ECG. The results showed that our methods were useful to detect target signals accurately.","PeriodicalId":127231,"journal":{"name":"2016 Third International Conference on Artificial Intelligence and Pattern Recognition (AIPR)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127113595","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

Mining useful macro-actions in planning 在规划中挖掘有用的宏观动作

2016 Third International Conference on Artificial Intelligence and Pattern Recognition (AIPR) Pub Date : 2016-09-01 DOI: 10.1109/ICAIPR.2016.7585227

Sandra Castellanos-Paez, D. Pellier, H. Fiorino, S. Pesty

引用次数: 1

Ultra-wideband scattered microwave signals for detection of breast tumors using artifical neural networks 基于人工神经网络的超宽带散射微波信号乳腺肿瘤检测

2016 Third International Conference on Artificial Intelligence and Pattern Recognition (AIPR) Pub Date : 2016-09-01 DOI: 10.1109/ICAIPR.2016.7585226

Nouralhuda A. Hassan, A. Yassin, M. Tayel, M. Mohamed

引用次数: 5

Dermatological disease detection using image processing and machine learning 使用图像处理和机器学习的皮肤病检测

2016 Third International Conference on Artificial Intelligence and Pattern Recognition (AIPR) Pub Date : 2016-09-01 DOI: 10.1109/ICAIPR.2016.7585217

Vinayshekhar Bannihatti Kumar, Sujay S. Kumar, Varun Saboo

引用次数: 93

Recognizing handwritten single digits and digit strings using deep architecture of neural networks 使用神经网络的深层结构识别手写单位数和数字字符串

2016 Third International Conference on Artificial Intelligence and Pattern Recognition (AIPR) Pub Date : 2016-09-01 DOI: 10.1109/ICAIPR.2016.7585206

Raid Saabni

{"title":"Recognizing handwritten single digits and digit strings using deep architecture of neural networks","authors":"Raid Saabni","doi":"10.1109/ICAIPR.2016.7585206","DOIUrl":"https://doi.org/10.1109/ICAIPR.2016.7585206","url":null,"abstract":"Automatic handwriting recognition of digits and digit strings, are of real interest commercially and as an academic research topic. Recent advances using neural networks and especially deep learning algorithms such as convolutional neural nets present impressive results for single digit recognition. Such results enable developing efficient tools for automatic mail sorting and reading amounts and dates on personal checks. Artificial- Neural-Networks is a powerful technology for classification of visual inputs in many fields due to their ability to approximate complex nonlinear mappings directly from input samples. In this paper we present an approach compromising between the full connectivity of traditional Multi Layer Neural Network trained by Back Propagation and deep architecture. This enables, reasonable training time using a four hidden layers Neural Network and keeps high recognition rates. Pre-trained layers using sparse auto encoders with predefined sequences of training process and rounds, are used to train the net to attain high recognition rates. We have extended the training set to include CVL, MNIST and manually crafted images of single digits from the ORAND-CAR and a private collection of bank checks. Sliding windows technique is used to handle digit strings recognition and obtain encouraging results on CVL and ORAND-CAR benchmarks and our private collection of local bank checks.","PeriodicalId":127231,"journal":{"name":"2016 Third International Conference on Artificial Intelligence and Pattern Recognition (AIPR)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130071583","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: 24

Synchronizing switching times of vacuum interrupters for medium voltage switchboards' techniques 中压配电盘真空灭弧开关时间同步技术

2016 Third International Conference on Artificial Intelligence and Pattern Recognition (AIPR) Pub Date : 2016-09-01 DOI: 10.1109/ICAIPR.2016.7585204

S. J. Gatan

引用次数: 1

Object-oriented change detection for multi-source images using multi-feature fusion 基于多特征融合的面向对象多源图像变化检测

2016 Third International Conference on Artificial Intelligence and Pattern Recognition (AIPR) Pub Date : 2016-09-01 DOI: 10.1109/ICAIPR.2016.7585215

Baoming Zhang, Jun Lu, Haitao Guo, Junfeng Xu, Chuan Zhao

引用次数: 2

Building recognition system based on deep learning 构建基于深度学习的识别系统

2016 Third International Conference on Artificial Intelligence and Pattern Recognition (AIPR) Pub Date : 2016-09-01 DOI: 10.1109/ICAIPR.2016.7585230

P. Bezák

引用次数: 12