2020 Zooming Innovation in Consumer Technologies Conference (ZINC)最新文献_第5页

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2020 Zooming Innovation in Consumer Technologies Conference (ZINC) Pub Date : 2020-05-01 DOI: 10.1109/zinc50678.2020.9161793

引用次数: 0

Application of improved PSO algorithm in power grid fault diagnosis 改进粒子群算法在电网故障诊断中的应用

2020 Zooming Innovation in Consumer Technologies Conference (ZINC) Pub Date : 2020-05-01 DOI: 10.1109/ZINC50678.2020.9161774

Bian Li, Duan Yingli, Li Penghua

引用次数: 3

YOLOv3 Algorithm with additional convolutional neural network trained for traffic sign recognition 带有附加卷积神经网络的YOLOv3算法用于交通标志识别

2020 Zooming Innovation in Consumer Technologies Conference (ZINC) Pub Date : 2020-05-01 DOI: 10.1109/ZINC50678.2020.9161446

Branislav Novak, Velibor Ilic, Bogdan Pavković

引用次数: 13

RFM and Classification Predictive Modelling to Improve Response Prediction Rate RFM和分类预测建模提高响应预测率

2020 Zooming Innovation in Consumer Technologies Conference (ZINC) Pub Date : 2020-05-01 DOI: 10.1109/ZINC50678.2020.9161800

Tristan Lim

{"title":"RFM and Classification Predictive Modelling to Improve Response Prediction Rate","authors":"Tristan Lim","doi":"10.1109/ZINC50678.2020.9161800","DOIUrl":"https://doi.org/10.1109/ZINC50678.2020.9161800","url":null,"abstract":"In consumer electronics where sales cycle is about two to three years, and with increased competition and product differentiation faced by suppliers in online distribution channels, it is important to pay attention to targeted marketing in online consumer electronics sales through the use of predictive analytics, as marketing paradigm is becoming increasingly customer-focused and unsolicited marketing is often costly and ineffective due to low response rates. In this study, customer predictive analytical techniques, including the RecencyFrequency Monetary (or RFM) method and classical classification modelling methods – logistic regression, decision tree, neural network and ensemble models – are utilized to improve predictive accuracy. Results from the neural network model shows a significant improvement over RFM model, with positive response rates improving by more than 2x, from 42.9% to 87.2%. However, if stronger explanability power is preferred, decision tree model may be utilized, although predictive accuracy of about 2% is sacrificed. The study discusses predictive modelling useful to improve the performance of positive response rate targeting, alongside the benefits of improved sampling and reduced computing power, especially with significantly large datasets. In real life implementation, it is imperative that companies understand that classification power of the models and marketing campaign targeting are continuous improvement processes. These processes improve with every iteration from its baseline towards its objective threshold level set by the companies’ management. False positive transactions should be investigated, with the effect of incorporating the findings to the improvement of models going forward.","PeriodicalId":6731,"journal":{"name":"2020 Zooming Innovation in Consumer Technologies Conference (ZINC)","volume":"24 1","pages":"333-337"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73114556","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

Vehicle Detection in the Autonomous Vehicle Environment for Potential Collision Warning 自动驾驶汽车环境中潜在碰撞预警的车辆检测

2020 Zooming Innovation in Consumer Technologies Conference (ZINC) Pub Date : 2020-05-01 DOI: 10.1109/ZINC50678.2020.9161791

Mario Gluhaković, M. Herceg, M. Popovic, J. Kovacevic

引用次数: 12

GA-FBHCL: A method for the best HVAC location* GA-FBHCL:最佳暖通空调选址方法*

2020 Zooming Innovation in Consumer Technologies Conference (ZINC) Pub Date : 2020-05-01 DOI: 10.1109/ZINC50678.2020.9161819

Zahra Pezeshki, Ali Gholipour Soleimani, A. Darabi

引用次数: 2

Testing Environment for ADAS Software Solutions ADAS软件解决方案的测试环境

2020 Zooming Innovation in Consumer Technologies Conference (ZINC) Pub Date : 2020-05-01 DOI: 10.1109/ZINC50678.2020.9161772

Davor Barić, R. Grbić, M. Subotic, V. Mihic

引用次数: 0

Hardware Implementation of a Fast 3D Anaglyph Image Watermarking Framework for Integration in Consumer Electronics Devices 面向消费类电子设备集成的快速三维立体图像水印框架的硬件实现

2020 Zooming Innovation in Consumer Technologies Conference (ZINC) Pub Date : 2020-05-01 DOI: 10.1109/ZINC50678.2020.9161783

Subhadeep Koley

{"title":"Hardware Implementation of a Fast 3D Anaglyph Image Watermarking Framework for Integration in Consumer Electronics Devices","authors":"Subhadeep Koley","doi":"10.1109/ZINC50678.2020.9161783","DOIUrl":"https://doi.org/10.1109/ZINC50678.2020.9161783","url":null,"abstract":"Due to the unprecedented growth of the internet and portable consumer imaging devices, copyright protection of digital images has become a topical affair. In this paper, a fast Tensor-SVD based red-cyan anaglyph 3D image watermarking has been presented which provides high imperceptibility, and robustness. To integrate the Human Visual System modelling into our framework, integer lifting wavelet transform has been incorporated. After that, the watermark has been infused within the first diagonal matrix generated by Tensor-SVD. The proposed method is free from false positive issues due to its total insertion-based approach. Furthermore, the proposed method is also implemented in a low-power Single Board Computer for seamless integration in personal and industrial consumer imaging devices. Moreover, the watermark’s security has been further increased by encrypting it with Arnold’s Cat Map based cryptic algorithm. Qualitative and quantitative comparison with various state-of-the-art methods justifies the superiority of the suggested algorithm under most form of signal processing, and geometric impairments.","PeriodicalId":6731,"journal":{"name":"2020 Zooming Innovation in Consumer Technologies Conference (ZINC)","volume":"91 1","pages":"40-45"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77227769","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

Enhanced Grey Wolf Algorithm for Energy Efficient Wireless Sensor Networks 高能效无线传感器网络的改进灰狼算法

2020 Zooming Innovation in Consumer Technologies Conference (ZINC) Pub Date : 2020-05-01 DOI: 10.1109/ZINC50678.2020.9161788

M. Zivkovic, N. Bačanin, Tamara Zivkovic, I. Strumberger, Eva Tuba, M. Tuba

{"title":"Enhanced Grey Wolf Algorithm for Energy Efficient Wireless Sensor Networks","authors":"M. Zivkovic, N. Bačanin, Tamara Zivkovic, I. Strumberger, Eva Tuba, M. Tuba","doi":"10.1109/ZINC50678.2020.9161788","DOIUrl":"https://doi.org/10.1109/ZINC50678.2020.9161788","url":null,"abstract":"Wireless sensor networks have entered a period of a rapid development, due to several novel technologies which have emerged in the past few years, such as Internet of Things and cloud computing. Miniature sensor nodes are integral components of numerous complex systems. The biggest problem for any wireless sensor network, in any possible application domain, is to maximize the overall network lifetime by improving the total energy consumption of the network. A large number of clustering algorithms have been implemented in the past decade, with a main goal to balance the energy consumption of each node in the network and to increase energy efficiency - the term known in literature as load balancing. One important representative of these traditional algorithms for load balancing which is still in frequent use is LEACH. On the other hand, swarm intelligence meaheuristics have recently been successfully applied in solving a large number of NP hard problems from the wireless sensor networks domain. In this paper, we propose an improved version of grey wolf algorithm, that has been applied to improve the network lifetime optimization. Grey wolf algorithm was employed in forming the clusters and the cluster head selection process. As a part of our research, we have evaluated the performance of the proposed exploration enhanced grey wolf algorithm by comparing it to the traditional LEACH algorithm, basic grey wolf approach and particle swarm optimization, that were all tested under the same experimental conditions. Obtained results from conducted simulations have proven that our proposed metaheuristics performs better that other considered algorithms.","PeriodicalId":6731,"journal":{"name":"2020 Zooming Innovation in Consumer Technologies Conference (ZINC)","volume":"48 1","pages":"87-92"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79784182","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: 33

Advanced Lane Finding Prototype Based on Autoware Platform 基于Autoware平台的高级寻道原型

2020 Zooming Innovation in Consumer Technologies Conference (ZINC) Pub Date : 2020-05-01 DOI: 10.1109/ZINC50678.2020.9161818

Marko Dragojevic, Stevan Stevic, Momcilo Krunic, N. Lukic

引用次数: 0