2021 IEEE AFRICON最新文献_第6页

Sizing and Modelling of a Standalone PV System with Battery Management System for a Typical Household in Nairobi, Kenya 肯尼亚内罗毕典型家庭独立光伏系统与电池管理系统的尺寸和建模

2021 IEEE AFRICON Pub Date : 2021-09-13 DOI: 10.1109/africon51333.2021.9570999

Willy Stephen Tounsi Fokui, Michael Juma Saulo, L. Ngoo

引用次数: 1

Towards hydro-informatics modernization with real-time water consumption classification 以实时用水量分类迈向水文信息学现代化

2021 IEEE AFRICON Pub Date : 2021-09-13 DOI: 10.1109/africon51333.2021.9570909

Aristotelis Charalampous, Andreas Papadopoulos, Stavros Hadjiyiannis, P. Philimis

{"title":"Towards hydro-informatics modernization with real-time water consumption classification","authors":"Aristotelis Charalampous, Andreas Papadopoulos, Stavros Hadjiyiannis, P. Philimis","doi":"10.1109/africon51333.2021.9570909","DOIUrl":"https://doi.org/10.1109/africon51333.2021.9570909","url":null,"abstract":"According to the European Environment agency, water demand across Europe has steadily increased over the past 50 years, partly due to population growth and people moving to cities and towns, especially in densely populated areas. Household use is reported to account for 12% of total water use in Europe. Effective water management practices are being put in place EU-wide, but those that target residential end users are limited to public awareness campaigns, promoting the purchase and use of water-saving devices. Our system aims to bridge the gap between consumers and appliances by accurate and on-time monitoring of household water consumption, at individual appliance level, helping users ease into enduring water-saving practices. Our system’s platform incorporates advanced signal processing methodologies combined with supervised machine learning classifiers to classify water flows, thus identifying residential water appliances with high accuracy. Our experimentation confirms that our models achieve accuracy of ~91% in classifying the four most used household water appliances. This is crucial in assisting end users in reducing their households’ overall water consumption.","PeriodicalId":170342,"journal":{"name":"2021 IEEE AFRICON","volume":"44 5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116506939","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

A Comparative Evaluation of Spatio Temporal Deep Learning Techniques for Crime Prediction 时空深度学习技术在犯罪预测中的比较评价

2021 IEEE AFRICON Pub Date : 2021-09-13 DOI: 10.1109/africon51333.2021.9570858

Tawanda Matereke, Clement N. Nyirenda, Mehrdad Ghaziasgar

{"title":"A Comparative Evaluation of Spatio Temporal Deep Learning Techniques for Crime Prediction","authors":"Tawanda Matereke, Clement N. Nyirenda, Mehrdad Ghaziasgar","doi":"10.1109/africon51333.2021.9570858","DOIUrl":"https://doi.org/10.1109/africon51333.2021.9570858","url":null,"abstract":"This paper presents a detailed evaluation of three spatio-temporal deep learning architectures for crime prediction. These network architectures are as follows: the Spatio Temporal Residual Network (ST-ResNet), the Deep Multi View Spatio Temporal Network (DMVST-Net), and the Spatio Temporal Dynamic Network (STD-Net). The architectures were trained using the Chicago crime data set. The Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) were used as performance metrics to evaluate the models. Results show that the STD-Net achieved the best results of the three approaches, with an accuracy of 0.89, RMSE of 0.2870, and MAE of 0.2093. The ST-ResNet and DMVST-Net also showed considerable promise. The ST-ResNet achieved an accuracy of 0.83, RMSE of 0.4033 and an MAE of 0.3278 while the DMVST-Net achieved an accuracy of 0.79, RMSE of 0.4171 and an MAE of 0.3455. Future work will include training these algorithms with crime data, which is augmented with external data such as climate and socioeconomic data. Hyperparameter optimization of these algorithms using techniques, such as evolutionary computation, will also be explored.","PeriodicalId":170342,"journal":{"name":"2021 IEEE AFRICON","volume":"227 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129434314","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: 4

Training a Multi-Layered Perceptron using Moth Swarm Algorithm for Predicting Energy Demand of a Data Centre and Weights-Based Analysis of Input Parameters 用飞蛾群算法训练多层感知器预测数据中心的能源需求和基于权重的输入参数分析

2021 IEEE AFRICON Pub Date : 2021-09-13 DOI: 10.1109/africon51333.2021.9570996

O. Ajayi, Reolyn Heymann

{"title":"Training a Multi-Layered Perceptron using Moth Swarm Algorithm for Predicting Energy Demand of a Data Centre and Weights-Based Analysis of Input Parameters","authors":"O. Ajayi, Reolyn Heymann","doi":"10.1109/africon51333.2021.9570996","DOIUrl":"https://doi.org/10.1109/africon51333.2021.9570996","url":null,"abstract":"Multi-Layered Perceptron is a type of artificial neural networks and obtaining the optimal weights and biases of the model is critical to achieving good performance. In this study, Moth Swarm Algorithm has been proposed to train a Multi-Layered Perceptron neural network by finding the best combination of weights and biases that produce outputs with the least possible Mean Squared Error. The model has been applied for predicting the energy demand of a data centre. The simulations have been conducted using real life data obtained from an anonymous data centre operator in South Africa. The input parameters considered in the model are the ambient temperature, ambient relative humidity, chiller output temperature and CRAC air supply temperature. The performance of the proposed method has been evaluated based on the Mean Squared Error, Root Mean Squared Error, Mean Absolute Error, Mean Absolute Percentage Error and accuracy values obtained for the training and testing set. By comparing the results obtained with other models like Moth Flame Optimization, Ant Lion Optimization and Whale Optimization Algorithm, it was found that the Moth Swarm Algorithm-trained Multi-Layered Perceptron outperformed the other models. Further, a Percentage Relative Contribution analysis has been conducted to highlight the level of influence each of the input parameters considered has on the energy demand pattern of the data centre. Analyses show that the ambient temperature has the highest influence of 31.7% on the energy demand of the building.","PeriodicalId":170342,"journal":{"name":"2021 IEEE AFRICON","volume":"528 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130252877","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

BER Performance of Antenna Sequence Modulation (ASM) 天线序列调制(ASM)的误码率性能

2021 IEEE AFRICON Pub Date : 2021-09-13 DOI: 10.1109/africon51333.2021.9570995

J. Obadha, P. Akuon, Vitalis Oduol Kalecha

引用次数: 2

Warehouse Management System Enhancement: A Case Study of ATOZ Textiles Limited.* 仓库管理系统的改进:以ATOZ纺织有限公司为例*

2021 IEEE AFRICON Pub Date : 2021-09-13 DOI: 10.1109/africon51333.2021.9570993

Beatus Mbunda, D. Machuve, S. Mirau

引用次数: 1

Assessing the use of mobile educational tools for enhancing innovative teaching and learning in higher education institutions in Tanzania 评估在坦桑尼亚高等教育机构中使用移动教育工具加强创新教学的情况

2021 IEEE AFRICON Pub Date : 2021-09-13 DOI: 10.1109/africon51333.2021.9570965

G. I. Mwandosya

{"title":"Assessing the use of mobile educational tools for enhancing innovative teaching and learning in higher education institutions in Tanzania","authors":"G. I. Mwandosya","doi":"10.1109/africon51333.2021.9570965","DOIUrl":"https://doi.org/10.1109/africon51333.2021.9570965","url":null,"abstract":"mobile technologies have impacted people’s lives by enhancing the way activities are performed in industries in different sectors including the education sector. The use of mobile devices by teachers and students in Higher Education Institutions (HEIs) have brought up online sharing and exchange of educational materials. This mode of educational communication has influenced (or impacted) the performance of students and teachers specifically in the academic field through innovative teaching and learning. However, in Tanzania little is known on the extent of the use of mobile education tools (METs) for enhancing innovative teaching and learning. This study, therefore, applied the modified technology acceptance model (TAM) to assess the use of METs for innovative teaching and learning in Four HEIs in Tanzania. The study was mainly quantitatively combined with an initial qualitative study whereby nine (9) teachers and twenty-one (21) students were purposefully selected for semi-structured interviews. In total 600 participants from the four HEIs in Dar es Salaam participated in the study. The qualitative data were thematically analyzed and the quantitative data were analyzed using IBM Statistical Package for Social Sciences (SPSS) version 23 descriptive analysis based on means and standard deviations. Findings revealed that electronic learning (e-learning) systems are generally operative in all of the four HEIs as well as learning management systems which have some educational tools embedded for the improvement of exchange and sharing of educational materials between teachers and students. Apart from e-learning systems, the use of METs is not formal so that teachers can use their own educational tools for teaching purposes.","PeriodicalId":170342,"journal":{"name":"2021 IEEE AFRICON","volume":"35 12","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133269167","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

Probabilistic Distributions for Modelling Seasonal Load Profiles of Commercial Areas in South Africa 南非商业区域季节性负荷分布模型的概率分布

2021 IEEE AFRICON Pub Date : 2021-09-13 DOI: 10.1109/africon51333.2021.9570879

Kgaogelo Mampa, A. Alonge

{"title":"Probabilistic Distributions for Modelling Seasonal Load Profiles of Commercial Areas in South Africa","authors":"Kgaogelo Mampa, A. Alonge","doi":"10.1109/africon51333.2021.9570879","DOIUrl":"https://doi.org/10.1109/africon51333.2021.9570879","url":null,"abstract":"One of the most significant commodities of today’s world is energy. Energy usage depends on various factors such as season, day of the week, temperature etc. It is imperative that the distribution, transmission, and generation of electricity is effective while equally producing required results to electricity customers. With an expectation for increasing power outages in South Africa in the nearest future, there is a renewed focused on electricity distribution and consumption. This paper examines the electric load profile at a commercial location in Johannesburg, South Africa, for which the overall dataset (in KWh) is classified into four seasonal regimes: summer, spring, winter, and autumn. Two probabilistic models – normal and lognormal distributions – are applied to investigate the medium-term behaviour of the time series dataset over a period of two years, between 2019 and 2020. Results from this investigation suggest that normal distribution gives a better approximation to the seasonal datasets, except during the spring season. The lognormal distribution is observed to give minimal fitting errors during the spring season. Additionally, the load profile during summer and spring seasons are observed to exhibit similar characteristics, likewise, both autumn and winter seasons are found to exhibit the same trend for the same period.","PeriodicalId":170342,"journal":{"name":"2021 IEEE AFRICON","volume":"53 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130551164","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

Power Delivered in AC Networks as an Algebraic Sum, P + Q not Phasor Sum, P + jQ when power is expressed as VmIm sin(ωt) sin(ωt − θ) 交流网络中以代数和形式输出的功率，P + Q非相和，P + jQ，当功率表示为VmIm sin(ωt) sin(ωt−θ)时

2021 IEEE AFRICON Pub Date : 2021-09-13 DOI: 10.1109/africon51333.2021.9570892

P. Akuon

引用次数: 0

Multi-Tier Multi-Domain Network Slicing: A Resource Allocation Perspective 多层多域网络切片:一个资源分配的视角

2021 IEEE AFRICON Pub Date : 2021-09-13 DOI: 10.1109/africon51333.2021.9570854

S. Oladejo, S. Ekwe, L. Akinyemi

引用次数: 1