Conceptual Model of Management of Technologically Integrated Industry Development Projects

Tryhuba, Anatoliy; Тryhuba, Inna; Bashynsky, Oleg; Kondysiuk, Igor; Koval, N.; Bondarchuk, Larysa

doi:10.1109/csit49958.2020.9321903

Cited by 14 publications

(10 citation statements)

References 9 publications

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“…Based on analysis of statistical data [10] and the necessary calculations, we assessed the stochastic characteristics of the market value of grain waste and the collection of said waste in individual agricultural enterprises of Ukraine (Table 3). Data on the market value of grain waste were obtained following an analysis of official statistical data provided by the National Statistics Office of Ukraine.…”

Section: Effectiveness Of Projects -mentioning

confidence: 99%

“…This leads to the strengthening of EU requirements for both the technologies used and the quality of raw materials from which the final product is obtained. This encourages producers of the final product to join forces by launching interdependent integrated projects [10,11]. This allows them to provide the necessary resources (production facilities, machinery and equipment, contractors, etc.…”

Section: Introductionmentioning

confidence: 99%

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Taxonomy and Stakeholder Risk Management in Integrated Projects of the European Green Deal

et al. 2022

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The article analyzed the state of the global problem of ecological safety of the EU and argued the need to create a methodology for planning technologically integrated projects of the “European Green Deal” (TIP “EGD”) that accounts for the risks to the stakeholders, i.e., the state, project executors, resource suppliers, project managers, and clients. Each of these has an individual set of values that determines the project risk for them. Herein is proposed a taxonomy of three ranks of TIP “EGD” involving agricultural waste, determined by their characteristics, products, and requirements for the stakeholders. The authors point out the need to create tools for quantitative risk assessment for the stakeholders of TIP “EGD” involving agricultural waste and distinguish four groups of risk components with regard to the value of such projects. A model of value risk formation is presented that addresses the risk management of each of these stakeholder values. The need to develop tools (models, methods, and algorithms) for quantitative risk assessment of the values of each type of project is discussed. Regularities in the formation of stakeholder values, which were a foundation of the model of formation, are established.

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Section: Effectiveness Of Projects -mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Taxonomy and Stakeholder Risk Management in Integrated Projects of the European Green Deal

et al. 2022

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“…), face detection is one of the first and most important tasks. Face detection (FD) [8,9] makes it possible for the system to detect the presence and position of a face in an image or in a video stream. The input of almost any face detection algorithm is an image.…”

Section: Introductionmentioning

confidence: 99%

“…Optimizing AI models in the face detection problem is always an urgent task and should be prioritized when there is a huge requirement for robustness and real time through the camera system. The face detection problem often encounters challenges that reduce the performance of the Face detection system, which currently does not have an AI model that can completely solve, such as occlusion, light, skin color, facial poses, facial expressions, accessories (glasses, masks...), face ratio [8,9]. The FD system can detect partially obscured faces, but it is difficult for the system to confirm whether the face is completely or partially obscured in the frame.…”

Section: Introductionmentioning

confidence: 99%

A survey on deep learning based face detection

Tran¹,

Nguyen²,

Tran³

2023

AAIT

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The article has focused on surveying face detection models based on deep learning, specifically examining different one-stage models in order to determine how to choose the appropriate face detection model as well as propose a direction to enhance our face detection model to match the actual requirements of computer vision application systems related to the face. The face detection models that were conducted survey include single shot detector, multi-task cascaded convolution neural networks, RetinaNet, YuNet on the Wider Face dataset. Tasks during the survey are structural investigation of chosen models, conducting experimental surveys to evaluate the accuracy and performance of these models. To evaluate and provide criteria for choosing face detection suitable for the requirements, two indicators are used, average precision to evaluate accuracy and frames-per-second to evaluate performance. Experiential results were analyzed and used for making conclusions and suggestions for future work. For our real-time applications on face-related camera systems, such as driver monitoring system, supermarket security system (shoplifting warning, disorderly warning), attendance system, often require fast processing, but still ensures accuracy. The models currently applied in our system such as Yolos, Single Shot Detector, MobileNetv1 guarantee real-time processing, but most of these models have difficulty in detecting small faces in the frame and cases containing contexts, which are easily mistaken for a human face. Meanwhile, the RetinaNet_ResNet50 model brings the highest accuracy, especially to ensure the detection of small faces in the frame, but the processing time is larger. Therefore, through this survey, we propose an enhancement direction of the face detection model based on the RetinaNet structure with the goal of ensuring accuracy and reducing processing time.

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“…Особливості виконання зазначеного процесу значною мірою залежать, як від видів проєктів, так і від характеристик їх проєктного середовища. Особливої уваги заслуговують гібридні проєкти, які виникають у операційній діяльності підприємств та організацій [1][2][3][4]. У аграрному виробництві є низка таких гібридних проєктів, які мають різну тривалість життєвого циклу та особливу предметну складову.…”

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Planning the Time of Performance of Works in Hybrid Projects

Tryhuba¹,

Kondysiuk²,

Koval³

et al. 2022

SM3P

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The aim of the work is to substantiate the approach to forecasting the time fund for work in hybrid projects, taking into account the changing nature and climatic components of the design environment based on the use of neural networks. The neural network architecture involves the use of a multilayer perceptron, teacher training, and the method of backpropagation. It is based on an algorithm that minimizes the prediction error by propagating error signals from the network outputs (predicted duration of naturally allowed forecasting the working time fund) to its inputs (values of the duration of naturally allowed forecasting the working time fund in previous days), in the direction opposite to the direct propagation of signals. Based on the prepared initial data, the training of an artificial neural network was performed, which ensured the creation of an artificial neural network that is able to predict the duration of naturally allowed time to perform work in a software environment written in Python. Studies based on neural network training show that when the number of epochs increases to more than 25,000, the error does not exceed 4.8%. To study the neural network, we used the statistical data of the summer months of 2020 on the naturally allowed forecasting the working time fund during certain days, which are typical for the conditions of the Volodymyr-Volynskyi district of the Volyn region. The obtained results indicate that the use of the proposed architecture of the artificial neural network gives a fairly accurate forecast and this is the basis for making quality management decisions on planning the content and timing of work in hybrid projects.

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Conceptual Model of Management of Technologically Integrated Industry Development Projects

Cited by 14 publications

References 9 publications

Taxonomy and Stakeholder Risk Management in Integrated Projects of the European Green Deal

Taxonomy and Stakeholder Risk Management in Integrated Projects of the European Green Deal

A survey on deep learning based face detection

Planning the Time of Performance of Works in Hybrid Projects

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