2020 10th Annual Computing and Communication Workshop and Conference (CCWC)最新文献_第10页

A Fuzzy Linguistic Programming for Sustainable Ecotourism Activities 可持续生态旅游活动的模糊语言规划

2020 10th Annual Computing and Communication Workshop and Conference (CCWC) Pub Date : 2020-01-01 DOI: 10.1109/CCWC47524.2020.9031187

Esra Çakır, H. Ulukan

引用次数: 1

Zoning Effect on the Capacity and Placement Planning for Battery Exchange Stations in Battery Consolidation Systems 分区对电池整合系统中电池交换站容量和布局规划的影响

2020 10th Annual Computing and Communication Workshop and Conference (CCWC) Pub Date : 2020-01-01 DOI: 10.1109/CCWC47524.2020.9031261

Dara Nyknahad, Rojin Aslani, W. Bein, L. Gewali

{"title":"Zoning Effect on the Capacity and Placement Planning for Battery Exchange Stations in Battery Consolidation Systems","authors":"Dara Nyknahad, Rojin Aslani, W. Bein, L. Gewali","doi":"10.1109/CCWC47524.2020.9031261","DOIUrl":"https://doi.org/10.1109/CCWC47524.2020.9031261","url":null,"abstract":"The concept of the battery exchange station (BES) as a part of the battery consolidation system (BCS) has certain criteria which makes it a significant player of better adaptation plan of electric vehicles (EVs) in the grid toward the smart grid (SG). In this paper, we focus on finding an efficient joint capacity and placement planning of BESs among candidate zones and exchange decisions of EV drivers at a BCS (CPPED) with the aim of minimizing the implementation cost of BESs placement, the distance traveled by EVs to exchange their batteries, and the waiting time of EVs at the BESs subject to the limitation on the number of BESs in the system. The formulated multi-objective optimization problem is non-convex with a combination of binary and continues variables which introduces a disjoint feasible solution set. To have a polynomial time complexity solution, we employ abstract Lagrangian duality and maximization-minimization methods to handle the binary variables. Our proposed method reaches to a robust solution of joint CPPED problem. Through simulation results, we investigate the effect of the number of zones and number of BESs on the performance of the proposed method for addressing the CPPED problem. Our numerical results show that increasing the number of zones and maximum number of BESs have significant effects on optimizing the CPPED problem, however after some certain points, their effects are less significant.","PeriodicalId":161209,"journal":{"name":"2020 10th Annual Computing and Communication Workshop and Conference (CCWC)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114993054","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: 5

Using Attribute-based Feature Selection Approaches and Machine Learning Algorithms for Detecting Fraudulent Website URLs 使用基于属性的特征选择方法和机器学习算法检测欺诈性网站url

2020 10th Annual Computing and Communication Workshop and Conference (CCWC) Pub Date : 2020-01-01 DOI: 10.1109/CCWC47524.2020.9031125

Mustafa Aydin, I. Butun, K. Bicakci, N. Baykal

引用次数: 9

Topic Analysis of Climate-Change News 气候变化新闻的主题分析

2020 10th Annual Computing and Communication Workshop and Conference (CCWC) Pub Date : 2020-01-01 DOI: 10.1109/CCWC47524.2020.9031122

S. Chawathe

引用次数: 1

Deep Reinforcement Learning Pairs Trading with a Double Deep Q-Network 基于双深度q -网络的深度强化学习配对交易

2020 10th Annual Computing and Communication Workshop and Conference (CCWC) Pub Date : 2020-01-01 DOI: 10.1109/CCWC47524.2020.9031159

Andrew Brim

引用次数: 18

Combinatorial-Based Event Sequences for Reduction of Android Test Suites 基于组合的事件序列减少Android测试套件

2020 10th Annual Computing and Communication Workshop and Conference (CCWC) Pub Date : 2020-01-01 DOI: 10.1109/CCWC47524.2020.9031238

Ryan Michaels, David Adamo, Renée C. Bryce

引用次数: 6

A Simple Model for Reasoning about Limits on Coupling in Object-Oriented Software 面向对象软件中耦合极限的一个简单推理模型

2020 10th Annual Computing and Communication Workshop and Conference (CCWC) Pub Date : 2020-01-01 DOI: 10.1109/CCWC47524.2020.9031128

H. Melton

引用次数: 0

Two Methods for Authentication Using Variable Transmission Power Patterns 两种使用可变传输功率模式的认证方法

2020 10th Annual Computing and Communication Workshop and Conference (CCWC) Pub Date : 2020-01-01 DOI: 10.1109/CCWC47524.2020.9031210

Hosam Alamleh, A. A. AlQahtani

引用次数: 6

Identification of Wild Species in Texas from Camera-trap Images using Deep Neural Network for Conservation Monitoring 基于摄像机陷阱图像的德克萨斯州野生物种识别的深度神经网络保护监测

2020 10th Annual Computing and Communication Workshop and Conference (CCWC) Pub Date : 2020-01-01 DOI: 10.1109/CCWC47524.2020.9031190

Sazida B. Islam, Damian Valles

{"title":"Identification of Wild Species in Texas from Camera-trap Images using Deep Neural Network for Conservation Monitoring","authors":"Sazida B. Islam, Damian Valles","doi":"10.1109/CCWC47524.2020.9031190","DOIUrl":"https://doi.org/10.1109/CCWC47524.2020.9031190","url":null,"abstract":"Protection of endangered species requires continuous monitoring and updated information about the existence, location, and behavioral alterations in their habitat. Remotely activated camera or “camera traps” is a reliable and effective method of photo documentation of local population size, locomotion, and predator-prey relationships of wild species. However, manual data processing from a large volume of images and captured videos is extremely laborious, time-consuming, and expensive. The recent advancement of deep learning methods has shown great outcomes for object and species identification in images. This paper proposes an automated wildlife monitoring system by image classification using computer vision algorithms and machine learning techniques. The goal is to train and validate a Convolutional Neural Network (CNN) that will be able to detect Snakes, Lizards and Toads/Frogs from camera trap images. The initial experiment implies building a flexible CNN architecture with labeled images accumulated from standard benchmark datasets of different citizen science projects. After accessing satisfactory accuracy, new camera-trap imagery data (collected from Bastrop County, Texas) will be implemented to the model to detect species. The performance will be evaluated based on the accuracy of prediction within their classification. The suggested hardware and software framework will offer an efficient monitoring system, speed up wildlife investigation analysis, and formulate resource management decisions.","PeriodicalId":161209,"journal":{"name":"2020 10th Annual Computing and Communication Workshop and Conference (CCWC)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129105961","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: 9

Analysis of Human Behaviors in Real-Time Swarms 实时群体中的人类行为分析

2020 10th Annual Computing and Communication Workshop and Conference (CCWC) Pub Date : 2020-01-01 DOI: 10.1109/CCWC47524.2020.9031150

G. Willcox, Louis B. Rosenberg, Colin Domnauer

{"title":"Analysis of Human Behaviors in Real-Time Swarms","authors":"G. Willcox, Louis B. Rosenberg, Colin Domnauer","doi":"10.1109/CCWC47524.2020.9031150","DOIUrl":"https://doi.org/10.1109/CCWC47524.2020.9031150","url":null,"abstract":"Many species reach group decisions by deliberating in real-time systems. This natural process, known as Swarm Intelligence (SI), has been studied extensively in a range of social organisms, from schools of fish to swarms of bees. A new technique called Artificial Swarm Intelligence (ASI) has enabled networked human groups to reach decisions in systems modeled after natural swarms. The present research seeks to understand the behavioral dynamics of such “human swarms.” Data was collected from ten human groups, each having between 21 and 25 members. The groups were tasked with answering a set of 25 ordered ranking questions on a 1-5 scale, first independently by survey and then collaboratively as a real-time swarm. We found that groups reached significantly different answers, on average, by swarm versus survey ($mathrm{p}=0.02$). Initially, the distribution of individual responses in each swarm was little different than the distribution of survey responses, but through the process of real-time deliberation, the swarm's average answer changed significantly ($mathrm{p} < 0.001$). We discuss possible interpretations of this dynamic behavior. Importantly, the we find that swarm's answer is not simply the arithmetic mean of initial individual “votes” ($mathrm{p} < 0.001$) as in a survey, suggesting a more complex mechanism is at play—one that relies on the time-varying behaviors of the participants in swarms. Finally, we publish a set of data that enables other researchers to analyze human behaviors in real-time swarms.","PeriodicalId":161209,"journal":{"name":"2020 10th Annual Computing and Communication Workshop and Conference (CCWC)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127811426","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