An increasing number of steel factories around the world are integrating two processes: continuous casting and hot rolling. The slab warehouse is the link between these two processes. Slabs of certain sizes are cast o...
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ISBN:
(数字)9798350349818
ISBN:
(纸本)9798350349825
An increasing number of steel factories around the world are integrating two processes: continuous casting and hot rolling. The slab warehouse is the link between these two processes. Slabs of certain sizes are cast on the continuous casting machine streams and then transported to the slab warehouse. In the warehouse, overhead cranes move the slabs to storage locations, into stacks. The sequence of slabs arriving at the slab warehouse plays an important role: slabs that arrive earlier should, as a rule, be stacked earlier. If several cranes may be operating in a warehouse in an adjacent space, the scheduling of cranes becomes much more difficult: a safe distance must be maintained between the cranes. This paper proposes a mathematical model of crane scheduling for a defined solution to the storage location assignment problem in a slab warehouse. This mathematical model considers a large number of constraints specific to the mentioned process, such as: orthogonality of crane movement; order of slab processing; minimum distance between cranes; maximum and minimum height of stacks; and many others. The developed mathematical model takes into account the space-time characteristics of the warehouse and crane trajectories, which allows its application to create a digital twin of the slab warehouse, which will allow to check the acceptability of the crane schedule for some solution of the problem of storage location assignment._
Optical speed sensors based on the spatial frequency filter method are a proven technology that offer high measurement accuracy over a wide speed range. Newly developed variants also enable measurements at very low sp...
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The main methods of analysis the accuracy of the geolocation system were studied. The aspects of technology for obtaining geographical coordinates using Google geolocation system (GPS) were considered. The best method...
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Fog computing extends the capabilities of cloud computing by enabling computing at the edge of the network, involving devices such as mobile collaborative devices or fixed nodes with integrated storage, computing, and...
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Selecting the most relevant subset of features from a dataset is a vital step in data mining and machine *** feature in a dataset has 2n possible subsets,making it challenging to select the optimum collection of featu...
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Selecting the most relevant subset of features from a dataset is a vital step in data mining and machine *** feature in a dataset has 2n possible subsets,making it challenging to select the optimum collection of features using typical *** a result,a new metaheuristicsbased feature selection method based on the dipper-throated and grey-wolf optimization(DTO-GW)algorithms has been developed in this *** can result when the selection of features is subject to metaheuristics,which can lead to a wide range of ***,we adopted hybrid optimization in our method of optimizing,which allowed us to better balance exploration and harvesting chores more *** propose utilizing the binary DTO-GW search approach we previously devised for selecting the optimal subset of *** the proposed method,the number of features selected is minimized,while classification accuracy is *** test the proposed method’s performance against eleven other state-of-theart approaches,eight datasets from the UCI repository were used,such as binary grey wolf search(bGWO),binary hybrid grey wolf,and particle swarm optimization(bGWO-PSO),bPSO,binary stochastic fractal search(bSFS),binary whale optimization algorithm(bWOA),binary modified grey wolf optimization(bMGWO),binary multiverse optimization(bMVO),binary bowerbird optimization(bSBO),binary hysteresis optimization(bHy),and binary hysteresis optimization(bHWO).The suggested method is superior 4532 CMC,2023,vol.74,no.2 and successful in handling the problem of feature selection,according to the results of the experiments.
Most of the sensor devices in the Internet of Things systems are based on energy-efficient microcontrollers, the computing resources of which are limited, as well as the amount of available memory. Increasing the secu...
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ISBN:
(数字)9798350384499
ISBN:
(纸本)9798350384505
Most of the sensor devices in the Internet of Things systems are based on energy-efficient microcontrollers, the computing resources of which are limited, as well as the amount of available memory. Increasing the security of the use of such devices with the help of neural networks is an important and urgent problem. The article describes the possibility of using artificial neural networks in small microcontrollers with limited resources. The purpose of this work is to check the possibility of calculating neural networks based on integer arithmetic to reduce the time of calculating a neural network and eliminate data normalization operations, as well as to evaluate the feasibility of using such neural networks in the field of security of the Internet of Things in comparison with traditional methods, such as black lists and white lists. The following results were obtained: when switching to integer arithmetic, compared to floating point, the accuracy of the result calculations is within the permissible error of neural network training, that is, it has not changed. Execution time decreased by $30-96 \%$ , depending on the architecture of the microcontroller. The program size is reduced by $22-48 \%$ , also depending on the microcontroller architecture. Conclusions: the possibility and expediency of using neural networks optimized for microcontrollers with limited resources was proved. This will increase the security of Internet of Things systems, especially against device authentication threats and intrusion detection. Prospects for further research are determined.
In this work, the effectiveness of using classical machine learning methods and modern deep neural network models for intrusion detection in computer networks has been investigated. The purpose of this work is to deve...
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ISBN:
(数字)9798350384499
ISBN:
(纸本)9798350384505
In this work, the effectiveness of using classical machine learning methods and modern deep neural network models for intrusion detection in computer networks has been investigated. The purpose of this work is to develop a model for detecting intrusions into computer networks based on the Transformer model using tabular input data. In this work, the UNSW-NB15 dataset is used as the source data. This dataset contains information about normal network behaviour as well as during synthetic intrusions. Models for intrusion detection in computer networks based on machine learning methods were investigated: Decision Tree, KNN, Logistic Regression, SVM, Gradient Boosting, Random Forest. A method of converting tabular data into images was developed, which made it possible to build intrusion detection models based on Vision Transformer and Vision Transformer for small-size datasets on modern Transformer architecture. The research results showed that developed model based on Vision Transformer and Vision Transformer for small-size datasets improves the quality of identification, and eliminates the need for a preprocessing step such as dimensionality reduction.
The work is devoted to solving the current scientific and technical problem of constructing a diagnostic decision support system in medicine based on a heterogeneous ensemble classifier model that implements two appro...
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ISBN:
(数字)9798350384499
ISBN:
(纸本)9798350384505
The work is devoted to solving the current scientific and technical problem of constructing a diagnostic decision support system in medicine based on a heterogeneous ensemble classifier model that implements two approaches to formulating a diagnostic conclusion: a probabilistic one based on the analysis of the training sample, and expert information on the structure of symptom complexes. The choice of prototype matching method as a probabilistic component is justified. Formalization of expert information on the structure of symptom complexes was carried out by representing symptom complexes of diseases with numerical intervals of linguistic variables. Options for taking into account expert assessments about the structure of symptom complexes in an ensemble classifier are considered. Test verification of the developed classifier was done on real medical data and confirmed the effectiveness of its work.
Nowadays, one of the most demanding areas of Artificial Intelligence application is to forecast the weather condition as rapid and accurate as possible. The ratio of air vapor pressure to saturation vapor pressure is ...
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Fairness concern behavior, a well-known cognitive bias, refers to a person’s attitude of dissatisfaction for unequal pay-offs in someone’s favor. Against environmental pollution, many firms are focused on green manu...
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