2018 IEEE 14th International Conference on Intelligent Computer Communication and Processing (ICCP)最新文献

CoolCloudSim: Integrating Cooling System Models in CloudSim CoolCloudSim:在CloudSim中集成冷却系统模型

2018 IEEE 14th International Conference on Intelligent Computer Communication and Processing (ICCP) Pub Date : 2018-09-01 DOI: 10.1109/ICCP.2018.8516648

Cristian Pintea, Eugen Pintea, Marcel Antal, Claudia Pop, T. Cioara, I. Anghel, I. Salomie

{"title":"CoolCloudSim: Integrating Cooling System Models in CloudSim","authors":"Cristian Pintea, Eugen Pintea, Marcel Antal, Claudia Pop, T. Cioara, I. Anghel, I. Salomie","doi":"10.1109/ICCP.2018.8516648","DOIUrl":"https://doi.org/10.1109/ICCP.2018.8516648","url":null,"abstract":"This paper addresses the problem of Data Centers (DCs) energy efficiency from a thermal perspective by extending the CloudSim framework to allow simulation and testing of thermal aware resource allocation policies aiming to minimize the cooling system energy consumption. The proposed framework, CoolCloudSim, can be used to develop and test new thermal aware Virtual Machine (VM) allocation strategies aiming to optimize the energy consumption of both cooling system and IT resources while meeting Service Level Agreements (SLAs). The default CloudSim architecture is extended by adding classes which contain mathematical models of the thermal processes within the server room. Furthermore, four new VM allocation policies that consider the cooling system energy consumption are developed based on the thermal and cooling system models. Finally, experiments are run to evaluate various metrics on a set of default CloudSim allocation algorithms and the proposed allocation algorithms. The results show that the proposed algorithms outperform the default CloudSim allocation strategy, Power Aware Best-Fit Decreasing (PABFD), in terms of overall energy consumption and the number of VM migrations, and have on average better results than other existing allocation strategies.","PeriodicalId":259007,"journal":{"name":"2018 IEEE 14th International Conference on Intelligent Computer Communication and Processing (ICCP)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115067742","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: 7

Choice Function-based Constructive Hyper-Heuristic for Generating Personalized Healthy Menu Recommendations 基于选择函数的生成个性化健康菜单推荐的建设性超启发式算法

2018 IEEE 14th International Conference on Intelligent Computer Communication and Processing (ICCP) Pub Date : 2018-09-01 DOI: 10.1109/ICCP.2018.8516650

V. Chifu, C. Pop, Adrian Birladeanu, Nicolae Dragoi, I. Salomie

引用次数: 3

A Deep Learning Approach For Pedestrian Segmentation In Infrared Images 红外图像行人分割的深度学习方法

2018 IEEE 14th International Conference on Intelligent Computer Communication and Processing (ICCP) Pub Date : 2018-09-01 DOI: 10.1109/ICCP.2018.8516630

R. Brehar, F. Vancea, T. Mariţa, S. Nedevschi

引用次数: 3

Optimizing Convolutional Neural Networks for low-resource devices 低资源设备的卷积神经网络优化

2018 IEEE 14th International Conference on Intelligent Computer Communication and Processing (ICCP) Pub Date : 2018-09-01 DOI: 10.1109/ICCP.2018.8516645

C. Rusu, G. Czibula

{"title":"Optimizing Convolutional Neural Networks for low-resource devices","authors":"C. Rusu, G. Czibula","doi":"10.1109/ICCP.2018.8516645","DOIUrl":"https://doi.org/10.1109/ICCP.2018.8516645","url":null,"abstract":"Convolutional neural networks are effective supervised learning models which are widely used nowadays in various applications ranging from computer vision tasks such as image detection and classification, image captioning, to video classification. Even if the convolutional models are highly performant, a major drawback is given by their computationally expensiveness from the viewpoint of the required memory, additions and multiplications operations and thus are hardly portable on limited-resource devices. The purpose of this paper is to demonstrate the applicability of convolutional neural networks for low resource devices and to study their performance in real life scenarios. In this respect, with the major goal of preserving the performance, we propose a convolutional neural network model, called SimpLeNet, using distillation for image tagging that can run on low-resource devices such as smartphones, smartwatches, tablets or TVs. Experiments performed on MNIST data set for image classification emphasize the effectiveness of SimpLeNet, both in terms of model’s size reduction, as well as in terms of classification accuracy","PeriodicalId":259007,"journal":{"name":"2018 IEEE 14th International Conference on Intelligent Computer Communication and Processing (ICCP)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124509298","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

Machine Learning Based Technique for Detecting Daily Routine and Deviations 基于机器学习的日常日常和偏差检测技术

2018 IEEE 14th International Conference on Intelligent Computer Communication and Processing (ICCP) Pub Date : 2018-09-01 DOI: 10.1109/ICCP.2018.8516598

Emil Stefan Chifu, V. Chifu, C. Pop, A. Vlad, I. Salomie

引用次数: 3

KarySOM: An Unsupervised Learning based Approach for Human Karyotyping using Self-Organizing Maps KarySOM:一种基于无监督学习的人类自组织地图核型分析方法

2018 IEEE 14th International Conference on Intelligent Computer Communication and Processing (ICCP) Pub Date : 2018-09-01 DOI: 10.1109/ICCP.2018.8516580

Casian-Nicolae Marc, G. Czibula

引用次数: 3

Evaluating Forecasting Techniques for Integrating Household Energy Prosumers into Smart Grids 将家庭能源生产用户纳入智能电网的评估预测技术

2018 IEEE 14th International Conference on Intelligent Computer Communication and Processing (ICCP) Pub Date : 2018-09-01 DOI: 10.1109/ICCP.2018.8516617

Teodor Petrican, Andreea Valeria Vesa, Marcel Antal, Claudia Pop, T. Cioara, I. Anghel, I. Salomie

{"title":"Evaluating Forecasting Techniques for Integrating Household Energy Prosumers into Smart Grids","authors":"Teodor Petrican, Andreea Valeria Vesa, Marcel Antal, Claudia Pop, T. Cioara, I. Anghel, I. Salomie","doi":"10.1109/ICCP.2018.8516617","DOIUrl":"https://doi.org/10.1109/ICCP.2018.8516617","url":null,"abstract":"This paper tackles the problem of integrating household energy prosumers in Smart Energy Grids by analyzing a set of state-of-the-art energy forecasting techniques that allow individual or aggregated prosumers to evaluate their future energy demand and inform the Distributed System Operator (DSO) about potential grid imbalances. Thus, the DSO can perform a proactive strategy to manage the grid and avoid problems before they appear. The key element of this approach is the prediction technique, that must be accurate enough such that the resulting grid imbalances can be compensated in real-time. The paper evaluates a set of state-of-the-art statistical and Machine Learning (ML) prediction techniques, such as SARIMA, feed-forward and recurrent neural networks, support vector regression or ensemble prediction models, on real household historical energy demand logs by performing a feature selection process for each ML algorithm as to identify the best elements that influence the energy demand of a house. A set of experiments are performed on the REFIT Electrical Load Measurements data set evaluating each model’s performance with respect to the selected features. Among the evaluated algorithms, the Ensemble Prediction Model gives best prediction accuracy, showing a Mean Absolute Percentage Error (MAPE) of 14.4% followed by the SVM model with a MAPE of 15.4%.","PeriodicalId":259007,"journal":{"name":"2018 IEEE 14th International Conference on Intelligent Computer Communication and Processing (ICCP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132182980","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: 11

Interactive spaces A change in scenographic aesthetics 互动空间:舞台美学的变化

2018 IEEE 14th International Conference on Intelligent Computer Communication and Processing (ICCP) Pub Date : 2018-09-01 DOI: 10.1109/ICCP.2018.8516591

Iuliana Gherghescu

引用次数: 1

Genetic Algorithms for Trajectory Tracking of Mobile Robot Based on PID Controller 基于PID控制器的移动机器人轨迹跟踪遗传算法

2018 IEEE 14th International Conference on Intelligent Computer Communication and Processing (ICCP) Pub Date : 2018-09-01 DOI: 10.1109/ICCP.2018.8516587

A. Alouache, Qing-he Wu

引用次数: 14

Super-Resolution Generator Networks: A comparative study 超分辨率生成器网络的比较研究

2018 IEEE 14th International Conference on Intelligent Computer Communication and Processing (ICCP) Pub Date : 2018-09-01 DOI: 10.1109/ICCP.2018.8516603

C. Lungu, R. Potolea

{"title":"Super-Resolution Generator Networks: A comparative study","authors":"C. Lungu, R. Potolea","doi":"10.1109/ICCP.2018.8516603","DOIUrl":"https://doi.org/10.1109/ICCP.2018.8516603","url":null,"abstract":"Modern approaches that tackle super-resolution aim to train a generator network that transforms the low resolution image into a higher resolution one. The core learning capacity of these generator networks is given by stacks of well known image processing blocks such as VGG-16 [SZ14], ResNet[HZRS15] or Inception-v3 [SVI $^{+15]}$ blocks. In the light of recent advancements on the CIFAR-10 [KNH] benchmarks where DenseNet [HLW16] and later SparseNet [ZDD $^{+18]}$ proved superior performance over the architectures that used the formerly mentioned blocks, this paper aims to do a comparative study on the performance changes resulting when using DenseNet or SparseNet blocks in generator networks. We first replicate the results of [JAL16]. This work describes a generator network that uses a stack of four ResNet blocks. This stack is incorporated in two architectures for superresolution, one for x4 magnification and another one for x8. We then proceed and substitute them with DenseNet blocks and SparseNet blocks but keep the same overall training procedure. In order to ensure a fair comparison we adapt the number of blocks for each architecture in order to match the same amount of parameters on all architectures. In all cases the same optimization loss function is used, perceptual loss [JAL16], which for a given image yields a value that is a weighted sum of mean-squared-errors between filters of the target input and generated image evaluated on equivalent convolution layers of the last three blocks in the VGG-16 network (pretrained on the ImageNet [DDS $^{+09]}$ dataset). We monitor on all architectures the loss value, the number of epochs needed to reach the lowest loss, the artifacts generated by each network and the overall appearance of the reconstructions.","PeriodicalId":259007,"journal":{"name":"2018 IEEE 14th International Conference on Intelligent Computer Communication and Processing (ICCP)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131772654","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