2022 8th International Engineering Conference on Sustainable Technology and Development (IEC)最新文献

Simulation Study of Solar Assisted Absorption Cooling System using Flat Plate Collectors at Erbil City-Iraq 伊拉克埃尔比勒市平板集热器太阳能辅助吸收冷却系统的模拟研究

2022 8th International Engineering Conference on Sustainable Technology and Development (IEC) Pub Date : 2022-02-23 DOI: 10.1109/IEC54822.2022.9807567

R. S. Abdullah, Sally Afram Polus

{"title":"Simulation Study of Solar Assisted Absorption Cooling System using Flat Plate Collectors at Erbil City-Iraq","authors":"R. S. Abdullah, Sally Afram Polus","doi":"10.1109/IEC54822.2022.9807567","DOIUrl":"https://doi.org/10.1109/IEC54822.2022.9807567","url":null,"abstract":"Solar energy can be used for cooling spaces when it is coupled with an air conditioning system. In this study, solar flat plate collector system supported by absorption cooling system was modeled and simulated using TRNSYS 16. The main objective of this study is to evaluate the ability of an existing thermal solar flat plate collector system to run an absorption chiller during summer time in Erbil, Kurdistan region of Iraq. First the simulation model (TRNSYS 16) results were verified with the solar thermal heating system installed in the Research Center of Solar Energy at Erbil Polytechnic University. The existing solar heating system consists of 10 flat plate solar collectors, each of 2 m2 surface area, 1 m3 storage tank, heat exchangers, 3 fan coils and pumps. The flat plate collector system is used to run a 4.7 kW water lithium bromide absorption chiller. The system is provided with 10 kW external auxiliary heaters from 8:00 AM to 3:00 PM during summer. The results reveal that, during July and August, the flat plate collector system can supply hot water without using auxiliary heater to the generator when the set point temperature of the auxiliary heater was at the lowest range (80 °C). The proposed system can save up to 2926.61 kWh electricity consumption while operating in July and August.","PeriodicalId":265954,"journal":{"name":"2022 8th International Engineering Conference on Sustainable Technology and Development (IEC)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122695949","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

Modeling and Simulation of 10 KW Solar Power Generator in a Microgrid System for Rural Area 农村微网系统中10kw太阳能发电机的建模与仿真

2022 8th International Engineering Conference on Sustainable Technology and Development (IEC) Pub Date : 2022-02-23 DOI: 10.1109/IEC54822.2022.9807607

H. H. Abbas, H. Bevrani, Qobad Shafie

引用次数: 0

Investigation of Indoor Air Quality in Several Offices in Baghdad – Case Study 巴格达几个办公室室内空气质量调查-个案研究

2022 8th International Engineering Conference on Sustainable Technology and Development (IEC) Pub Date : 2022-02-23 DOI: 10.1109/IEC54822.2022.9807508

Ihab Jabbar Al Rikabi, Mohamed Ali Abuelnour, T. Abed, A. M. Jawad, Adil A. M. Omara

引用次数: 1

Face Mask Detection System using Convolutional Neural Network 基于卷积神经网络的人脸检测系统

2022 8th International Engineering Conference on Sustainable Technology and Development (IEC) Pub Date : 2022-02-23 DOI: 10.1109/IEC54822.2022.9807543

Alaa Adham Ibrahim, Yara Arjuman Hashim, Truska Mustafa Omer, Rebin M. Ahmed

引用次数: 1

Urine Sediment Analysis by Using Convolution Neural Network 基于卷积神经网络的尿液沉积物分析

2022 8th International Engineering Conference on Sustainable Technology and Development (IEC) Pub Date : 2022-02-23 DOI: 10.1109/IEC54822.2022.9807482

Zhwan Mohammed Khalid, Roojwan Scddeek Hawezi, Sara Raouf Muhamad Amin

{"title":"Urine Sediment Analysis by Using Convolution Neural Network","authors":"Zhwan Mohammed Khalid, Roojwan Scddeek Hawezi, Sara Raouf Muhamad Amin","doi":"10.1109/IEC54822.2022.9807482","DOIUrl":"https://doi.org/10.1109/IEC54822.2022.9807482","url":null,"abstract":"Urinary particles are important requirements in clinical urinalysis, particularly in the diagnosis and monitoring of patients suspected of renal diseases and urinary tract Infections. As a result, it is critical to identify urinary particles accurately in the clinical area. Also the outcome is hugely affected by the doctor's experience. However, because the traditional manual microscopic analysis relies on human operators who read the samples visually and identify them, this method is slow, time-consuming, and labor-intensive, In this research, presented a deep learning method for analyzing urinary particles. The authors prepare a dataset of urine sediment microscopic images, which includes approximately 820 cell annotations and four-cell classes: RBC, Calcium oxalate, cysteine calcium, and uric acid. Used for deep learning training and testing of various convolutional network models. The authors proposed Convolution neural network structure and five ConvNet models such as MobileNet, VGG16, DenseNet, ResNet50V, InceptionV3. According to these evaluations, the best models for true positive recall are MobileNet, and the proposed method ist he second one.. These models also achieve the highest accuracy of 98.3 percent. on the other hand, InceptionV3 and DenceNet have comparable accuracy results with 96.5 percent.","PeriodicalId":265954,"journal":{"name":"2022 8th International Engineering Conference on Sustainable Technology and Development (IEC)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117161047","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: 2

A Comparative Evaluation of Deep Learning Methods in Automated Classification of White Blood Cell Images 白细胞图像自动分类中深度学习方法的比较评价

2022 8th International Engineering Conference on Sustainable Technology and Development (IEC) Pub Date : 2022-02-23 DOI: 10.1109/IEC54822.2022.9807456

H. A. Muhamad, S. Kareem, A. Mohammed

{"title":"A Comparative Evaluation of Deep Learning Methods in Automated Classification of White Blood Cell Images","authors":"H. A. Muhamad, S. Kareem, A. Mohammed","doi":"10.1109/IEC54822.2022.9807456","DOIUrl":"https://doi.org/10.1109/IEC54822.2022.9807456","url":null,"abstract":"The identification and characterization of a patient’s blood sample are required for the diagnosis of blood-related disorders. As a result, the medical implications of automated methods for identifying and categorizing various kinds of blood cells are considerable. However, deep convolutional neural networks (CNN) and standard machine learning algorithms have performed well in the categorization of blood cell pictures. Red, White, and Platelets are all types of blood cells. Leucocyte, commonly known as the immune cell, is a type of blood cell that plays a vital part in human immune function. Depending on shape info and granulated data in leukocytes, white blood cells are usually split by hematologists into two different categories: non-granular cells (lymphocytes and monocytes) and granular cells (eosinophils, basophils, and neutrophils). The CNN portion receives the pre-trained weight parameters from the image dataset using the transfer learning approach. Also, We have used two different scenarios, the first scenario of using CNN directly gave us pictures. used SVM in the second scenario. Then we compare the best category results. The classification results demonstrated that the accuracy of CNN is 98.4 %, whereas the accuracy of Support Vector Machine (SVM) is 90.6 %. Other classifiers can be added to the suggested system to improve its performance.","PeriodicalId":265954,"journal":{"name":"2022 8th International Engineering Conference on Sustainable Technology and Development (IEC)","volume":"99 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125408248","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: 2

A Survey and Analysis of Architecture and Models of Network Slicing in 5G 5G网络切片架构与模型综述与分析

2022 8th International Engineering Conference on Sustainable Technology and Development (IEC) Pub Date : 2022-02-23 DOI: 10.1109/IEC54822.2022.9807564

C. Mohammed, Salar Kheder Shaikhah

{"title":"A Survey and Analysis of Architecture and Models of Network Slicing in 5G","authors":"C. Mohammed, Salar Kheder Shaikhah","doi":"10.1109/IEC54822.2022.9807564","DOIUrl":"https://doi.org/10.1109/IEC54822.2022.9807564","url":null,"abstract":"Emerging fifth-generation (5G) networks need to fulfill high-reliability and low latency criteria and increase current and future network capacity and security requirements. To support many tenants on the same physical infrastructure, mobile service providers are searching for a virtual solution that can be used to allocate network resources on-demand and gain cost and efficiency using Network Slicing (NS) in 5G networks. NS has been considered as a promising enabler for 5G networks to meet systems ever-increasing and various performance requirements. Through \"slicing\" it, it is possible to adjust the functionality and performance. Resource separation and practical resource usage are also crucial features of the slicing concept. In this paper, a comprehensive survey was conducted in two directions; first, End-to-End (E2E) 5G NS architecture is studied, different available architecture scenarios are presented, and new architecture is proposed. The proposed architecture is justified by showing a 5G use case. In the second part, the most recent works on the Radio Access Network (RAN) and Transport Network (TN) NS are studied. Common models and algorithms are analyzed to show a research trend as future work. Additional to aggregating the most common topics on NS to have a comprehensive survey in this field.","PeriodicalId":265954,"journal":{"name":"2022 8th International Engineering Conference on Sustainable Technology and Development (IEC)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116632462","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: 3

Morphological investigation of liver and spleen under the influence of static magnetic field: In vivo 静磁场作用下肝脏和脾脏在体内的形态学研究

2022 8th International Engineering Conference on Sustainable Technology and Development (IEC) Pub Date : 2022-02-23 DOI: 10.1109/IEC54822.2022.9807558

Bestoon T. Mustafa, Sardar P. Yaba, A. Ismail

引用次数: 0

Quantifying the Factors Impacting Production Performance in an Oil Well in Kurdistan Region of Iraq 量化伊拉克库尔德斯坦地区油井生产动态影响因素

2022 8th International Engineering Conference on Sustainable Technology and Development (IEC) Pub Date : 2022-02-23 DOI: 10.1109/IEC54822.2022.9807466

Frzan F. Ali

{"title":"Quantifying the Factors Impacting Production Performance in an Oil Well in Kurdistan Region of Iraq","authors":"Frzan F. Ali","doi":"10.1109/IEC54822.2022.9807466","DOIUrl":"https://doi.org/10.1109/IEC54822.2022.9807466","url":null,"abstract":"The performance of oil producing reservoirs decline with time, therefore production optimization will be required. In order to optimize oil production effectively, variety of scenarios and options should be considered, which would lead to a complex and time-consuming study.The purpose of this research was to identify and quantify the wellbore and reservoir parameters that can impact oil production performance. A parametric study was conducted on an oil well in Kurdistan Region of Iraq, by changing several wellbore and reservoir parameters individually, and calculate the resulted oil production rate. The results were analyzed by Nodal Analysis to identify the production performance of each case. Later, the results from the sensitivity analysis were examined by an empirical study to find the relationships between the wellbore and reservoir parameters and their effect on production rate. Finally, regression analysis was used as a statistical method to correlate between the change in wellbore and reservoir parameters and their impact on oil production performance.This study shows that different wellbore and reservoir parameters have different effect on oil production rate. By quantifying their impact and finding the cases with the highest oil production rate, production performance can be optimized effectively. The study concluded that tubing diameter, pay zone thickness, and reservoir permeability has significant impact, while drainage area and tubing roughness has insignificant impact on oil production performance. These results are quantified and turned into generic formulas which can be used for analyzing production performance of other oil wells.","PeriodicalId":265954,"journal":{"name":"2022 8th International Engineering Conference on Sustainable Technology and Development (IEC)","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114699022","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

New Hashing Algorithm and an Authentication Technique to Improve IoT Security 提高物联网安全性的新哈希算法和认证技术

2022 8th International Engineering Conference on Sustainable Technology and Development (IEC) Pub Date : 2022-02-23 DOI: 10.1109/iec54822.2022.9807459

引用次数: 0