检索结果-内蒙古大学图书馆

international conference on Advanced Computing, ICAC

作者： Ipsita Dalai Prateek Mudali Arunima Sambhuta Pattanayak Bhawani Shankar Pattnaik Computer Science and Engineering National Institute of Science and Technology Under Biju Patnaik University of Technology Electronics and Communication Engineering RV College of Engineering Bangalore India

ISBN: (数字)9781728152868

ISBN: (纸本)9781728152875

Energy is the lifeblood of modern societies. In the past decades, due to the increases in population and comfort demands of people, energy consumption and associated CO2 emissions increased rapidly in the world. the next hour's energy prediction would help in facilitating future energy management, planning, and conservation in the industries and community setups. data-driven models provide a practical approach to energy consumption prediction. this paper offers an Internet of things (IoT) based Solution by running a data-driven model between edge and cloud layer to predict the energy consumption of a building in real-time. To achieve a real-time response from the system, the Machine learning (ML) inference algorithm runs in the edge node to predict the next hour energy demand of the building. A Microgrid IoT setup is established in the authors' organization for experimental purposes. this paper provides a substantial review of the four main ML approaches, including linear regression, multilayer perceptron, SMO Regression and Support Vector Machine (LibSVM) which have commonly been applied in forecasting and improving building energy performance. Considering the performance measures like Mean Absolute Error (MAE) and Root Mean Square Error (RMSE), this paper concludes that SVM performance is better in terms of prediction accuracy among all others. In terms of prediction time, it is concluded that the edge node takes less time for real-time prediction compared to the cloud.

关键词：

来源：评论

学校读者我要写书评

暂无评论

On Preferences of Novice Software engineering Students: Temperament Style and Attitudes Towards Programming Activities 11th

On Preferences of Novice Software Engineering Students: Temp...

引用

11th international conference on Informatics in Schools - Situation, Evolution, and Perspectives (ISSEP)

作者： Jevsikova, Tatjana Dagiene, Valentina Dolgopolovas, Vladimiras Vilnius Univ Inst Data Sci & Digital Technol Akad 4 LT-04812 Vilnius Lithuania

ISBN: (纸本)9783030027506;9783030027490

Educators' experience shows that learning programming is in many aspects problematic for novice software engineering students. On the other hand, software engineering processes and the view of programming has been changing during the recent years. In this paper, we address socio-cognitive aspects of computer science and software engineering in order to contribute to programming education enhancement: the research is focused on students' temperament style and favorite programming learning activities. the study of 158 first and second year students, studying programming specialties in five higher education institutions, has been presented. the "psychological portrait" of the surveyed students reflects the evolution of the temperament style in programming during last decades. the attitudes towards the programming activities, presented in this paper, may contribute to the development of enhancement of existing programming courses in higher education.

关键词： Temperament style and programming Personal characteristics and programming Programming students Novice programmers

来源：评论

学校读者我要写书评

暂无评论

Design of a Smart Desk for an IoT Testbed: Improving learning Efficiency and System Security 11th

Design of a Smart Desk for an IoT Testbed: Improving Learnin...

引用

11th international conference on Innovative Mobile and Internet Services in Ubiquitous Computing (IMIS)

作者： Yamada, Masafumi Cuka, Miralda Liu, Yi Oda, Tetsuya Matsuo, Keita Barolli, Leonard Fukuoka Inst Technol FIT Grad Sch Engn Higashi Ku 3-30-1 Wajiro Higashi Fukuoka 8110295 Japan Okayama Univ Sci OUS Dept Informat & Comp Engn Kita Ku 1-1 Ridaicho Okayama 7000005 Japan Fukuoka Inst Technol FIT Dept Informat & Commun Engn Higashi Ku 3-30-1 Wajiro Higashi Fukuoka 8110295 Japan

ISBN: (纸本)9783319615424;9783319615417

Due to the opportunities provided by the Internet, people are taking advantage of e-learning courses and enormous research efforts have been dedicated to the development of e-learning systems. So far, many e-learning systems are proposed and used practically. However, in these systems the e-learning completion rate is low. One of the reasons is the low study desire and motivation. In this work, we present an IoT-Based E-learning testbed using Raspberry Pi mounted on Raspbian. We carried out some experiments with a student of our laboratory for brain waves and, encryption and decryption time of cipher algorithms. We used MindWave Mobile (MWM) to get the data. For experimental results, when using our system the concentration is 80 [%] from 0 [s] to 400 [s]. But, for 400 [s] to 1000 [s], concentration is decreased. In the case of not using our system, concentration is almost the same (about 40 [%]) from 0 [s] to 1000 [s]. From simulation results, we found that the decryption time is faster than encryption time, however for 3-DES and Raspberry Pi decryption time is higher than encryption time.

关键词： Testbeds

来源：评论

学校读者我要写书评

暂无评论

CNN+RNN Depth and Skeleton based Dynamic Hand Gesture Recognition 24

CNN+RNN Depth and Skeleton based Dynamic Hand Gesture Recogn...

引用

24th international conference on Pattern Recognition, ICPR 2018

作者： Lai, Kenneth Yanushkevich, Svetlana N. Department of Electrical and Computer Engineering Biometric Technologies Laboratory University of Calgary Alberta Canada

ISBN: (纸本)9781538637883

Human activity and gesture recognition is an important component of rapidly growing domain of ambient intelligence, in particular in assisting living and smart homes. In this paper, we propose to combine the power of two deep learning techniques, the convolutional neural networks (CNN) and the recurrent neural networks (RNN), for automated hand gesture recognition using both depth and skeleton data. Each of these types of data can be used separately to train neural networks to recognize hand gestures. While RNN were reported previously to perform well in recognition of sequences of movement for each skeleton joint given the skeleton information only, this study aims at utilizing depth data and apply CNN to extract important spatial information from the depth images. Together, the tandem CNN+RNN is capable of recognizing a sequence of gestures more accurately. As well, various types of fusion are studied to combine both the skeleton and depth information in order to extract temporal-spatial information. An overall accuracy of 85.46% is achieved on the dynamic hand gesture-14/28 dataset. © 2018 IEEE.

关键词： Recurrent neural networks

来源：评论

学校读者我要写书评

暂无评论

Comparative study of Brain Tumor Segmentation using Different Segmentation Techniques in Handling Noise

Comparative study of Brain Tumor Segmentation using Differen...

引用

Joint international conference on Computer engineering, Network and intelligent Multimedia (CENIM) / 11th AUN/SEED-Net Regional conference on Computer and Information engineering (RCCIE)

作者： Iriawan, Nur Pravitasari, Anindya Apriliyanti Fithriasari, Kartika Irhamah Purnami, Santi Wulan Ferriastuti, Widiana Inst Teknol Sepuluh Nopember Fac Math Computat & Data Sci Dept Stat Surabaya Indonesia Univ Airlangga Fac Med Dept Radiol Surabaya Indonesia

ISBN: (纸本)9781538675090

Image segmentation has been popularly performed for researchers in the field of Biomedical, Informatics engineering, and Statistical Computation. this study tries to compare several methods for brain tumor image segmentation, especially in handling noise. the methods are K-means Cluster, Fuzzy C-Means (FCM) Cluster, Gaussian Mixture Model (GMM), and Fernandez-Steel Skew Normal (FSSN) Mixture model. K-means and FCM are the popular Partitioning methods for clustering, while GMM is model-based clustering method. the FSSN mixture model is the new model-based clustering introduced in this study. Both GMM and FSSN are formed through a finite mixture model with Bayesian Markov Chain Monte Carlo (MCMC) optimization. the dataset is the MRI brain tumor image from General Regional Hospital (RSUD) Dr. Soetomo Surabaya. Gaussian noise and Salt pepper noise are generated to see the robustness of each method. Various evaluation parameters like Silhouette Index, Partition Coefficient Index, and Misclassification Ratio are calculated for the appropriate methods and comparative analysis is carried out. the results indicate for partitioning methods especially FCM is more robust in handling Gaussian noise, while GMM is more robust in handling Salt and pepper noise. the outstanding result shows that the FSSN mixture model could handle both Gaussian and Salt pepper noise better than other methods.

关键词： image segmentation K-means FCM GMM Fernandez-Steel Skew Normal mixture Bayesian

来源：评论

学校读者我要写书评

暂无评论

Analysis of Users' Emotions through Physiology 11th

Analysis of Users' Emotions Through Physiology

引用

11th international conference on Genetic and Evolutionary Computing (ICGEC)

作者： Myroniv, Bohdan Wu, Cheng-Wei Ren, Yi Tseng, Yu-Chee Natl Chiao Tung Univ Dept Comp Sci Hsinchu Taiwan

ISBN: (纸本)9789811064876;9789811064869

Most of the existing studies focus on physical activities recognition, such as running, cycling, swimming, etc. But what affects our health, it is not only physical activities, it is also emotional states that we experience throughout the day. these emotional states build our behavior and affect our physical health significantly. therefore, emotion recognition draws more and more attention of researchers in recent years. In this paper, we propose a system that uses off-the-shelf wearable sensors, including heart rate, galvanic skin response, and body temperature sensors to read physiological signals from the users and applies machine learning techniques to recognize their emotional states. We consider three types of emotional states and conduct experiments on real-life scenarios with ten users. Experimental results show that the proposed system achieves high recognition accuracy.

关键词： Emotion recognition Machine learning Physiological signals Wearable devices

来源：评论

学校读者我要写书评

暂无评论

Guest Editorial for Special Section on the 11th international conference on intelligent Computing (ICIC)

引用

IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2017年第5期14卷 1104-1105页

作者： Huang, De-Shuang Bevilacqua, Vitoantonio Gromiha, M. Michael Tongji Univ Machine Learning & Syst Biol Lab Shanghai Peoples R China Polytech Bari Elect & Informat Engn Dept I-70126 Bari Italy Indian Inst Technol Dept Biotechnol Madras 600036 Tamil Nadu India

the papers in this special section were presented at the 11th international conference on intelligent Computing ICIC held in Fuzhou, China, on August 20-23, 2015. this conference was formed to provide an annual forum dedicated to the emerging and challenging topics in artificial intelligence, machine learning, bioinformatics, etc. It aims to bring together researchers and practitioners from both academia and industry to share ideas, problems, and solutions related to the multifaceted aspects of intelligent computing.

关键词： Special issues and sections Artificial intelligence Machine learning Bioinformatics Biomedical engineering Predictive models intelligent systems

来源：评论

学校读者我要写书评

暂无评论

Ground-motion prediction by ANN using machine learning for the Tohoku region, Japan 11

Ground-motion prediction by ANN using machine learning for t...

引用

11th National conference on Earthquake engineering 2018: Integrating Science, engineering, and Policy, NCEE 2018

作者： Ohno, S. Tsuruta, R. International Research Institute of Disaster Science Tohoku University Aramaki Aza-Aoba 468-1 Aoba-ku Sendai980-0845 Japan Tohoku Electric Power Co. Inc. 1 Maeda Tsukahama Oshika-gun Onagawa-cho Miyagi986-2293 Japan

ISBN: (纸本)9781510873254

the purpose of this study is developing new ground motion prediction method that is applicable to mega earthquakes with small prediction errors, comparted with ordinary ground motion prediction equations (GMPEs). We construct the strong-motion database, 38871 records from 342 stations and 652 earthquakes, from NIED K-NET data in Tohoku region, Japan. Using this database, artificial neural network (ANN) by using deep learning method are evaluated. the input variables are: Mw, hypocentral coordinate, station coordinate, station Vs30, and the outputs are PGA, PGV and acceleration response spectra. Several techniques of deep learning method are applied in order to find the appropriate evaluation procedure, and "normalization", "dropout", "pre-training", "ReLU (Rectified Linear Unit)" are found to be efficient to construct reliable ANN avoiding over-training. Finally, the ANN having two hidden layers of 500 units is obtained. We compared the ANN's estimates with those by ordinary GMPEs, whose parameters are Mw, source-to- site distance, Vs30. the prediction standard errors of the evaluated ANN are about 0.16 (in log 10 scale), approximately independent of periods, which are clearly smaller than those of the ordinary GMPE: 0.25-0.3. Also, the ANN can evaluate site-specific spectral peaks, large spectral amplitudes due to deep intra-slab earthquakes, which are difficult to estimate by ordinary GMPEs without adding special parameters. In conclusion, ANN with deep learning method can be used to ground motion prediction with smaller prediction errors compared with ordinary GMPE, while ANN's applicability to extended faults and the stability are still to be investigated. Copyright © NCEE 2018: Integrating Science, engineering, and *** rights reserved.

关键词： Neural networks

来源：评论

学校读者我要写书评

暂无评论

引用

international conference on Advanced Computing, ICAC

作者： D. Sangeetha S. Selvi M. Siva Anandha Ram Dept. of Information Technology MIT Campus Anna University Chennai India Dept. of Computer Science and Engineering Government College of Engineering Krishnagiri India

ISBN: (数字)9781728152868

ISBN: (纸本)9781728152875

Cardiovascular arrhythmia can be described as quickened, hindered, or irregular pulse brought by abnormalities caused by the muscular tissue of the heart. the Proposed Heartbeat classification technique uses deep two-dimensional Convolutional Neural Network (CNN) which gives extraordinary results in the field of pattern recognition and it is named as Cardiac Arrhythmia Prediction using ECG signals (CAPE). Each heartbeat was converted into a binary image. the converted binary image is used as the input data for the CNN classifier. Finally, the CNN classifier is enhanced to classify seven distinct kinds of ECG beats as pursues: Normal Beat (NOR), Paced Beat (PAB), Premature Ventricular Contraction Beat (PVC), Left Bundle Branch Block Beat (LBB), Right Bundle Branch Block Beat (RBB), Atrial Premature Contraction Beat (APC) and Ventricular Escape Beat (VEB). In order to achieve better accuracy, the developed CNN model incorporates optimization techniques such as data augmentation, batch normalization, dropout and Xavier initialization. the proposed model was compared with the popular CNN models: GoogleNet and VGGNet. In comparison, VGGNet takes lesser training time but gives higher accuracy. MITBIH arrhythmia database was used for the assessment of the classifier. the modified CNN classifier (VGGNet) with the converted ECG images accomplished a classification accuracy of 93.57% without any ECG pre-processing techniques such as noise filtering, feature extraction, and feature reduction.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Automatic Analysis of Potential Hazard Events Using Unmanned Aerial Vehicles

Automatic Analysis of Potential Hazard Events Using Unmanned...

引用

international conference on Electronics, Computers and Artificial Intelligence (ECAI)

作者： Radu Rădescu Mihaela Dragu Applied Electronics and Information Engineering University “Politehnica” of Bucharest Bucharest Romania

ISBN: (数字)9781728116242

ISBN: (纸本)9781728116259

this paper is motivated by the possibility of developing a wide variety of applications and domains in which Unmanned Aerial Vehicles (UAVs) can be used globally for various purposes. UAVs are currently used by public administrations and security forces such as police, fire brigades, civil protection, research institutions, construction, and agriculture entities. the purpose of this paper is to facilitate the handling of UAVs to retrieve various data from the environment. the drone (UAV) visits some points to collect data (image and/or video input) from sensors like GPS, camera, gyroscope, and accelerometer. GPS sensor coordinates are used to compare the data taken with subsequent results through processing with specialized software. the drone is used as an access gate with built-in sensors. Certain hazard events (fires, floods, avalanches, landslides) are not limited to narrow geographical areas, but can impact the environment by triggering negative chain events. 3D modeling offers a wide range of possibilities to prevent potential hazard events, or, if such an event has occurred, makes it possible to monitor the affected area and assess the damage by comparing the area in the pre-event configuration with the after-event one. After image processing and data acquisition, a report is generated that includes the map and the 3D model of the analyzed object. A hazard is an agent that has the potential to cause damage to a particular target. Terms such as risk or danger can be used in similar contexts. TensorFlow is an open source software library in high-performance computing. Flexible architecture allows easy deployment of computing on a variety of platforms (CPU, GPU, TPU), from desktop to server or mobile devices. We used the learning transfer: at first we used a model that was already prepared for another problem, and then we re-qualified it on a similar problem. Deep learning from scratch can take several days, but learning transfer can be done shortly. We applied

关键词：

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：