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Evaluation of Ethiopian Influenza Sentinel Surveillance System
Desalegn Takele,
Shikur Mohamed
Issue:
Volume 9, Issue 1, March 2021
Pages:
1-5
Received:
30 November 2020
Accepted:
14 December 2020
Published:
22 January 2021
Abstract: Background: Ethiopia conducts influenza sentinel surveillance since 2008 in eight sites through the coordination of Ethiopian Public Health Institute although little is known whether the system meets its objective. Hence, this evaluation is conducted to evaluate the sentinel surveillance attributes, purposes and its operation system. Method: A cross-sectional descriptive study was conducted from February 15-30, 2017 in all eight Sentinel sites. Data were collected using US-CDC updated surveillance guideline and Interview with influenza sentinel surveillance focal persons, regional public health emergency officers and national surveillance officers. Case based reports of influenza like illness and severe acute respiratory illness were also reviewed. Secondary data were collected from the national public health emergency management center based at EPHI. We analyzed and compiled the data. Results: Not all the visited health facilities have posted the ILI and SARI cases definition. None of the sentinel sites have been reporting influenza data to their next higher level but to the national PHEM (NIL). All focal persons have responded that they are expected to do so. Data is only being analyzed by national PHEM. Supportive supervision was done this month (February, 2017) since 2014. Laboratory feedback (test result) has been provided irregularly since May 2016 by the national influenza laboratory to sentinel sites and respective regional PHEM. All of focal persons have taken training on influenza surveillance. The positive predictive value (PPV) was 21.35% (n=4922). Among a total of 5,097 case based reports from 2008-2016, 47 (0.9%) age variable, 385 (7.5%) temperature variable, and 103 (2%) date of specimen collection were not filled. Conclusion: Although focal persons are satisfied with the forms and procedures involved, they are not filling formats properly as expected and reporting regularly as scheduled neither to the national PHEM nor to the next higher level. The influenza sentinel surveillance system has proven to be useful in providing virological data used to characterize and monitor influenza trends in Ethiopia. Continuous supportive supervision should be in placed using checklist to increase the quality of data. Data should be continuously analyzed and feedback should be given periodically to health care provider and partners.
Abstract: Background: Ethiopia conducts influenza sentinel surveillance since 2008 in eight sites through the coordination of Ethiopian Public Health Institute although little is known whether the system meets its objective. Hence, this evaluation is conducted to evaluate the sentinel surveillance attributes, purposes and its operation system. Method: A cros...
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Upwind Horizontal Axis Wind Turbine Output Power Optimization via Artificial Intelligent Control System
Endalew Ayenew Haile,
Getachew Biru Worku,
Asrat Mulatu Beyene,
Milkias Berhanu Tuka
Issue:
Volume 9, Issue 1, March 2021
Pages:
6-21
Received:
6 January 2021
Accepted:
16 January 2021
Published:
25 January 2021
Abstract: Power capturing capacity is one of the key performance indicators of wind turbines. This article presents a study done on the optimization of output power of upwind horizontal axis wind turbine using artificially intelligent control system. The study shows how blade tip speed ratio (λ) and pitch angle (β) are optimized to increase wind turbines power conversion coefficient (Cp) which increases the output power. An artificial intelligence system named Mandani fuzzy inference system (MFIS) was applied to optimize the power conversion coefficient in combination with blade pitch actuator control. To this end, a novel optimization technique is designed that maximizes the power harvesting ability of wind turbines by updating the parameters of the membership functions of fuzzy logic found in the MFIS. With the application of this optimization method, a power conversion coefficient Cp of 0.5608 value is achieved at optimal values of λ and β. As a result, the energy harvesting ability of the wind turbine considered is improved by 16.74%. This study clearly shows that the wind energy harvesting capacity of wind turbines can be enhanced via optimization techniques that could be further implemented in wind turbine blade pitch drive system. Thus, this novel optimization method creates further insights for the wind energy industry in reducing the cost of energy generation.
Abstract: Power capturing capacity is one of the key performance indicators of wind turbines. This article presents a study done on the optimization of output power of upwind horizontal axis wind turbine using artificially intelligent control system. The study shows how blade tip speed ratio (λ) and pitch angle (β) are optimized to increase wind turbines pow...
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Formation of International Ethical Digital Environment with Smart Artificial Intelligence
Issue:
Volume 9, Issue 1, March 2021
Pages:
22-33
Received:
1 January 2021
Accepted:
11 January 2021
Published:
25 January 2021
Abstract: Intellectual agent ensembles allow you to create digital environment by professional images with language, behavioral and active communications, when images and communications are implemented by agents with smart artificial intelligence. Through language, behavioral and active communications, intellectual agents implement collective activities. The ethical standard through intelligent agents allows you to regulate the safe use of ensembles made of robots and digital doubles with creative communication artificial intelligence in the social sphere, industry and other professional fields. The use of intelligent agents with smart artificial intelligence requires responsibility from the developer and owner for harming others. If harm to others occurred due to the mistakes of the developer, then he bears responsibility and costs. If the damage to others occurred due to the fault of the owner due to non-compliance with the terms of use, then he bears responsibility and costs. Ethical standard and legal regulation help intellectual agents with intelligent artificial intelligence become professional members of society. Ensembles of intelligent agents with smart artificial intelligence will be able to safely work with society as professional images with skills, knowledge and competencies, implemented in the form of retrained digital twins and cognitive robots that interact through language, behavioral and active ethical communications. Cognitive robots and digital doubles through self-developing ensembles of intelligent agents with synergistic interaction and intelligent artificial intelligence can master various high-tech professions and competencies. Their use in the industry increases labor productivity and economic efficiency of production. Their application in the social sphere improves the quality of life of a person and society. Their widespread application requires compliance with an ethical standard so that their use does not cause harm. The introduction and use of an ethical standard for the use of cognitive robots and digital doubles with smart artificial intelligence increases the safety of their use. Ethical relationships between individuals and intellectual agents will also be governed by an ethical standard.
Abstract: Intellectual agent ensembles allow you to create digital environment by professional images with language, behavioral and active communications, when images and communications are implemented by agents with smart artificial intelligence. Through language, behavioral and active communications, intellectual agents implement collective activities. The...
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Localization Method Based on Image Processing for Autonomous Driving of Mobile Robot in the Linear Infrastructure
Hyunwoo Song,
Jun Nakahama,
Yogo Takada
Issue:
Volume 9, Issue 1, March 2021
Pages:
34-45
Received:
18 January 2021
Accepted:
17 March 2021
Published:
12 April 2021
Abstract: In recent years, the deterioration of infrastructure facilities such as bridges has become a problem. Precautionary measures such as visual inspection and repair by humans are in place as countermeasures for aging; however, there are issues with cost and safety in such inspections. If inspection by robots becomes possible, both these aspects will be improved, which will significantly contribute to the maintenance of infrastructure facilities. In this paper, we propose a complex image processing technique to specify the location of feature points as coordinates through smartphone cameras to obtain the location information of feature points needed for positioning BIREM-IV-P developed to support bridge inspection. The corners located in the bridge inspection environment are used as feature points, and the corners are specified using Harris corner detection, which is a conventional corner detection method, to obtain the position of the feature points. In addition, to compensate for the shortcomings of Harris corner detection, a line segment in the image is detected using the Hough transform, and the intersection points of the line segments are recognized as corners. By combining the results of the two detection methods in this manner, the target feature points can be accurately specified. Then, the position of the feature points of the specified image coordinate system can be changed to the world coordinate system. As a result, it was possible to detect the location of the target feature points in a three-dimensional coordinate system.
Abstract: In recent years, the deterioration of infrastructure facilities such as bridges has become a problem. Precautionary measures such as visual inspection and repair by humans are in place as countermeasures for aging; however, there are issues with cost and safety in such inspections. If inspection by robots becomes possible, both these aspects will b...
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Research on Face Recognition Algorithm Based on Improved Residual Neural Network
Tang Xiaolin,
Wang Xiaogang,
Hou Jin,
Han Yiting,
Huang Ye
Issue:
Volume 9, Issue 1, March 2021
Pages:
46-60
Received:
18 March 2021
Accepted:
30 March 2021
Published:
12 April 2021
Abstract: The residual neural network is prone to two problems when it is used in the process of face recognition: the first is "overfitting", and the other is the slow or non-convergence problem of the loss function of the network in the later stage of training. In this paper, in order to solve the problem of "overfitting", this paper increases the number of training samples by adding Gaussian noise and salt and pepper noise to the original image to achieve the purpose of enhancing the data, and then we added "dropout" to the network, which can improve the generalization ability of the network. In addition, we have improved the loss function and optimization algorithm of the network. After analyzing the three loss functions of Softmax, center, and triplet, we consider their advantages and disadvantages, and propose a joint loss function. Then, for the optimization algorithm that is widely used through the network at present, that is the Adam algorithm, although its convergence speed is relatively fast, but the convergence results are not necessarily satisfactory. According to the characteristics of the sample iteration of the convolutional neural network during the training process, in this paper, the memory factor and momentum ideas are introduced into the Adam optimization algorithm. This can increase the speed of network convergence and improve the effect of convergence. Finally, this paper conducted simulation experiments on the data-enhanced ORL face database and Yale face database, which proved the feasibility of the method proposed in this paper. Finally, this paper compares the time-consuming and power consumption of network training before and after the improvement on the CMU_PIE database, and comprehensively analyzes their performance.
Abstract: The residual neural network is prone to two problems when it is used in the process of face recognition: the first is "overfitting", and the other is the slow or non-convergence problem of the loss function of the network in the later stage of training. In this paper, in order to solve the problem of "overfitting", this paper increases the number o...
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