SAIEE Africa Research Journal最新文献_第9页

Improved Q-learning for Energy Management in a Grid-tied PV Microgrid 用于并网光伏微电网能量管理的改进Q学习

IF 1.4

SAIEE Africa Research Journal Pub Date : 2021-03-17 DOI: 10.23919/SAIEE.2021.9432896

Erick O. Arwa;Komla A. Folly

{"title":"Improved Q-learning for Energy Management in a Grid-tied PV Microgrid","authors":"Erick O. Arwa;Komla A. Folly","doi":"10.23919/SAIEE.2021.9432896","DOIUrl":"10.23919/SAIEE.2021.9432896","url":null,"abstract":"This paper proposes an improved Q-learning method to obtain near-optimal schedules for grid and battery power in a grid-connected electric vehicle charging station for a 24-hour horizon. The charging station is supplied by a solar PV generator with a backup from the utility grid. The grid tariff model is dynamic in line with the smart grid paradigm. First, the mathematical formulation of the problem is developed highlighting each of the cost components considered including battery degradation cost and the real-time tariff for grid power purchase cost. The problem is then formulated as a Markov Decision Process (MDP), i.e., defining each of the parts of a reinforcement learning environment for the charging station’s operation. The MDP is solved using the improved Q-learning algorithm proposed in this paper and the results are compared with the conventional Q-learning method. Specifically, the paper proposes to modify the action-space of a Q-learning algorithm so that each state has just the list of actions that meet a power balance constraint. The Q-table updates are done asynchronously, i.e., the agent does not sweep through the entire state-space in each episode. Simulation results show that the improved Q-learning algorithm returns a 14% lower global cost and achieves higher total rewards than the conventional Q-learning method. Furthermore, it is shown that the improved Q-learning method is more stable in terms of the sensitivity to the learning rate than the conventional Q-learning.","PeriodicalId":42493,"journal":{"name":"SAIEE Africa Research Journal","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2021-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.23919/SAIEE.2021.9432896","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45814570","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

Copyright 版权

IF 1.4

SAIEE Africa Research Journal Pub Date : 2021-03-17 DOI: 10.23919/SAIEE.2021.9432891

引用次数: 0

Iterative Soft-Input Soft-Output Bit-Level Reed-Solomon Decoder Based on Information Set Decoding 基于信息集译码的迭代软输入软输出位级Reed-Solomon译码器

IF 1.4

SAIEE Africa Research Journal Pub Date : 2021-03-17 DOI: 10.23919/SAIEE.2021.9432893

Yuval Genga;Olutayo O. Oyerinde;Jaco Versfeld

引用次数: 1

Ear-based biometric authentication through the detection of prominent contours 基于耳朵的生物特征认证，通过检测突出的轮廓

IF 1.4

SAIEE Africa Research Journal Pub Date : 2021-03-17 DOI: 10.23919/SAIEE.2021.9432897

Aviwe Kohlakala;Johannes Coetzer

{"title":"Ear-based biometric authentication through the detection of prominent contours","authors":"Aviwe Kohlakala;Johannes Coetzer","doi":"10.23919/SAIEE.2021.9432897","DOIUrl":"10.23919/SAIEE.2021.9432897","url":null,"abstract":"In this paper novel semi-automated and fully automated ear-based biometric authentication systems are proposed. The region of interest (ROI) is manually specified and automatically detected within the context of the semi-automated and fully automated systems, respectively. The automatic detection of the ROI is facilitated by a convolutional neural network (CNN) and morphological postprocessing. The CNN classifies sub-images of the ear in question as either foreground (part of the ear shell) or background (homogeneous skin, hair or jewellery). Prominent contours associated with the folds of the ear shell are detected within the ROI. The discrete Radon transform (DRT) is subsequently applied to the resulting binary contour image for the purpose of feature extraction. Feature matching is achieved by implementing an Euclidean distance measure. A ranking verifier is constructed for the purpose of authentication. In this study experiments are conducted on two independent ear databases, that is (1) the Mathematical Analysis of Images (AMI) ear database and (2) the Indian Institute of Technology (IIT) Delhi ear database. The results are encouraging. Within the context of the proposed semi-automated system, accuracies of 99.20% and 96.06% are reported for the AMI and IIT Delhi ear databases respectively.","PeriodicalId":42493,"journal":{"name":"SAIEE Africa Research Journal","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2021-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.23919/SAIEE.2021.9432897","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44417116","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

Effect of Graphite Precursor Flake Size on Energy Storage Capabilities of Graphene Oxide Supercapacitors 石墨前驱体薄片尺寸对氧化石墨烯超级电容器储能性能的影响

IF 1.4

SAIEE Africa Research Journal Pub Date : 2021-03-17 DOI: 10.23919/SAIEE.2021.9432895

S. Perumal;A.L.L. Jarvis;M.Z. Gaffoor

引用次数: 2

Editors and Reviewers 编辑和审稿人

IF 1.4

SAIEE Africa Research Journal Pub Date : 2021-03-17 DOI: 10.23919/SAIEE.2021.9432890

引用次数: 0

Guest Editorial: SAUPEC/RobMech/PRASA 2020 客座编辑：SAUPEC/RobMech/PRASA 2020

IF 1.4

SAIEE Africa Research Journal Pub Date : 2021-03-17 DOI: 10.23919/SAIEE.2021.9432894

引用次数: 0

Class-Selective Mini-Batching and Multitask Learning for Visual Relationship Recognition 用于视觉关系识别的类选择小批量和多任务学习

IF 1.4

SAIEE Africa Research Journal Pub Date : 2021-03-17 DOI: 10.23919/SAIEE.2021.9432898

S. Josias;W. Brink

{"title":"Class-Selective Mini-Batching and Multitask Learning for Visual Relationship Recognition","authors":"S. Josias;W. Brink","doi":"10.23919/SAIEE.2021.9432898","DOIUrl":"10.23919/SAIEE.2021.9432898","url":null,"abstract":"An image can be described by the objects within it, and interactions between those objects. A pair of object labels together with an interaction label is known as a visual relationship, and is represented as a triplet of the form (subject, predicate, object). Recognising visual relationships in images is a challenging task, owing to the combinatorially large number of possible relationship triplets, which leads to an extreme multiclass classification problem. In addition, the distribution of visual relationships in a dataset tends to be long-tailed, i.e. most triplets occur rarely compared to a small number of dominating triplets. Three strategies to address these issues are investigated. Firstly, instead of predicting the full triplet, models can be trained to predict each of the three elements separately. Secondly a multitask learning strategy is investigated, where shared network parameters are used to perform the three separate predictions. Thirdly, a class-selective mini-batch construction strategy is used to expose the network to more of the rare classes during training. Experiments demonstrate that class-selective mini-batch construction can improve performance on classes in the long tail of the data distribution, possibly at the expense of accuracy on the small number of dominating classes. It is also found that a multitask model neither improves nor impedes performance in any significant way, but that its smaller size may be beneficial. In an effort to better understand the behaviour of the various models, a novel evaluation approach for visual relationship recognition is introduced. We conclude that the use of semantics can be helpful in the modelling and evaluation process.","PeriodicalId":42493,"journal":{"name":"SAIEE Africa Research Journal","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2021-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.23919/SAIEE.2021.9432898","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47908457","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

Investigative analysis of channel selection algorithms in cooperative spectrum sensing in cognitive radio networks 认知无线电网络中协作频谱感知信道选择算法的研究分析

IF 1.4

SAIEE Africa Research Journal Pub Date : 2021-01-29 DOI: 10.23919/SAIEE.2021.9340532

J. Tlouyamma;M. Velempini

{"title":"Investigative analysis of channel selection algorithms in cooperative spectrum sensing in cognitive radio networks","authors":"J. Tlouyamma;M. Velempini","doi":"10.23919/SAIEE.2021.9340532","DOIUrl":"https://doi.org/10.23919/SAIEE.2021.9340532","url":null,"abstract":"The proliferation of wireless mobile devices has led to a number of challenges in mobile data communication. The world is experiencinganincreasingusage of finite spectrum bands for social media and other data communication services. It is due to this high usage that the Federal Communications Commission(FCC) sought to open up some spectrum bands to be used opportunistically by secondary users (SUs). However, the coexistence of Primary Users (PUs) and SUs may cause interference which leads to wastage of spectrum resources. This study investigates the impact of interferences between PUs and SUs. To ensure higher detection of PU signal, a cooperative rule was used to decide which SU to share and makea final decision about the availability of the spectrum band. To maximize the throughput of SU, a maximum likelihood function was designed to reduce delays in searching for the next available channel for data transmission. To discover more transmission opportunities and ensuring that a good number of free channels are detected, a parallel sensing technique was employed. Matlabwas used to simulate and generate the results in a distributed cognitive radio environment. The proposed extended generalizedpredictive channel selection algorithm (EXGPCSA) outperformed otherschemes in literature in terms of throughput, service timeandprobability of detection.","PeriodicalId":42493,"journal":{"name":"SAIEE Africa Research Journal","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2021-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.23919/SAIEE.2021.9340532","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67992283","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: 6

Detection of Bryde's whale short pulse calls using time domain features with hidden Markov models 利用时域特征和隐马尔可夫模型检测布氏鲸短脉冲叫声

IF 1.4

SAIEE Africa Research Journal Pub Date : 2021-01-29 DOI: 10.23919/SAIEE.2021.9340533

Oluwaseyi P. Babalola;Ayinde M. Usman;Olayinka O. Ogundile;Daniel J. J. Versfeld

{"title":"Detection of Bryde's whale short pulse calls using time domain features with hidden Markov models","authors":"Oluwaseyi P. Babalola;Ayinde M. Usman;Olayinka O. Ogundile;Daniel J. J. Versfeld","doi":"10.23919/SAIEE.2021.9340533","DOIUrl":"https://doi.org/10.23919/SAIEE.2021.9340533","url":null,"abstract":"Passive acoustic monitoring (PAM) is generally usedto extract acoustic signals produced by cetaceans. However, the large data volume from the PAM process is better analyzed using an automated technique such as the hidden Markovmodels (HMM). In this paper, the HMM is used as a detection and classification technique due to its robustness and low time complexity. Nonetheless, certain parameters, such as the choice of features to be extracted from the signal, the frame duration, and the number of states affect the performance of the model. Theresults show that HMM exhibits best performances as the number of states increases with short frame duration. However, increasing the number of states creates more computational complexity in the model. The inshore Bryde's whales produce short pulse calls with distinct signal features, which are observable in the time-domain. Hence, a time-domain feature vector is utilized to reduce the complexity of the HMM. Simulation results also show that average power as a time-domain feature vector provides the best performance compared to other feature vectors for detecting the short pulse call of inshore Bryde's whales based on the HMM technique. More so, the extracted features such as the average power, mean, and zero-crossing rate, are combined to form a single 3-dimensional vector (PaMZ). The PaMZ-HMM shows improved performance and reduced complexity over existing feature extraction techniques such as Mel-scale frequency cepstral coefficients (MFCC) and linear predictive coding (LPC). Thus, making the PaMZ-HMM suitable for real-time detection.","PeriodicalId":42493,"journal":{"name":"SAIEE Africa Research Journal","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2021-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.23919/SAIEE.2021.9340533","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67992473","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