Optimal Operating Points Research Articles

Enhancing electric vehicle penetration and grid operation performance in old residential communities through hybrid AC/DC microgrid reconstruction

The widespread popularity of electric vehicles (EVs) imposes great challenges for distribution grids, such as feeder congestion, voltage deviation, and so forth. Although building new electrical facilities is feasible to release more capacity for EV integration, this is not a wise scheme to be applied in old residential communities owing to huge costs and limited spaces. In light of this, the paper proposes an easy-to-implement and cost-effective hybrid AC/DC microgrid reconstruction scheme for low-voltage distribution grids of old residential communities. The advanced power electronic device called smart interlinking unit (SIU) is incorporated in prior power distribution rooms of residential communities, featuring integrated structure, dedicated interfaces, and flexible control capability. The SIU-based architecture reuses existing redundant transformers and power lines to form AC/DC subgrids. An outstanding transfer capacity improvement and power loss reduction via the DC renovation are quantified. Considering the varied efficiency characteristics of multiple power converters and transformers, a hierarchical coordinated operation strategy is proposed for this architecture to realize the overall energy-saving operation. The optimal operation point tracking and AC/DC voltage regulation are included in the strategy design. Regarding the target scenario in the case study, the adopted reconstruction scheme would achieve a significant 297% of the maximum EV penetration. The real-time voltage regulation for AC and DC feeders is also realized to avoid possible voltage violations. Additionally, the proposed architecture and operation strategy could obtain about 10% of load ratio improvement and 1.5% of average efficiency promotion compared to the traditional AC architecture.

SCOPF considering voltage and power fluctuation under renewable energy integration

Security-Constrained Optimal Power Flow (SCOPF) method could generate system optimal operation point, which guarantee system security by avoiding any overload whenever expected N-1 contingencies occurred. However, renewable power integration increases uncertainty of system security, which is not well treated in conventional SCOPF model. Then this study proposes an improved SCOPF model, which aims at optimizing outputs of generators to increasing security of system facing challenges by N-1 contingencies accompanied by renewable power fluctuations. Semi-invariant is employed to express the stochastic behavior of wind and photovoltaic power generation. Considering the influence of transformer transformation ratio on admittance matrix, the uncertainty of injected renewable power is combined with the adjustable characteristics of network parameters to obtain the fluctuation model of branch power and bus voltage. Therefore, the constraints for SCOPF are restated to treat the problem that expected value based traditional model makes errors to confirm overload. The probability analysis for branch overload allows this study to present a potential reduced contingency set, in which small-probability events are rationally excluded. Thus, the constraint quantities as well as the complexity of the optimization problem are effectively reduced to decrease the computation burden. A Benders decomposition method based interior point approach is used to solve the improved SCOPF optimization model. Case studies based on IEEE39 bus system demonstrate the efficiency of proposed method.

Adaptive design for the connection of multicell HHO generator with solar photovoltaic panels

Hydrogen is considered a new source of energy and one of the sources of clean energy. One of the most widely used methods of hydrogen production is water electrolysis. Water electrolysis causes water molecules to break down into hydrogen and oxygen by passing an electric current through water molecules and using an intercellular membrane. If the intercellular membrane is removed, hydrogen gas and oxygen are mixed to produce hydroxy gas. This gas, which contains hydrogen and oxygen molecules, has been considered an auxiliary fuel and combustion enhancer. In this research, the design and construction of a multi-cell electrolyzer producing hydroxy gas without a membrane was carried out using 316 stainless steel sheets. Potassium hydroxide (KOH) solution was used as an electrolyte to increase water conductivity, and solar panels were used to supply the energy required for the electrolyzer. Adjusting the number of electrolyzer plates with the voltage of the solar panels is one of the most critical points in the optimal design of this combined system. The effect of parameters on the amount of hydroxy gas production, energy intensity, and production per electrolyzer surface has been evaluated. The experimental results show that due to the internal resistance of each electrolyzer cell, the voltage of each cell varies between 2 and 3 V in different currents. The number of series cells in the electrolyzer determines its total voltage, which should be matched to the solar system to stay at the optimum point of operation. In the studied electrolyzer, optimized design of electrolizer have 18, 17 and, 15 plates, when one, two, and three 325W solar panels is used, respectively. In the next step, the daily test is performed for optimized electrolyzers. Daily production per surface of the solar panel for the three states was 530, 600 and 533L/m2, respectively. This indicates that daily production increases with increasing the number of panels but specific production per solar panel area may not increase and will have an optimal value. So, in the combination of an HHO generator with a solar system, the number of electrolyzer plates and also the surface area of solar panels should be optimally selected to achieve the optimum operation. This issue can be completed with accurate modeling in future studies.

Generalized analysis of optimal performance of chemical looping gasification

Thermodynamic modeling of chemical looping gasification (CLG) is carried out using Gibbs free energy minimization approach to, determine the optimal operating condition, analyze the optimal performance and generalize to any feedstock. Optimal operating condition is identified by analyzing the effect of mole Fe2O3/mole C (OC/C) on carbon conversion and Cold Gas Efficiency (CGE). Simulations using four different oxygen carriers (OC) reveal that, CGE shows a maximum with increase in OC/C. The OC/C at which CGE attains a maximum is proposed to be called as the Maximum CGE Point (MCP). It is shown that, unlike conventional gasification, the carbon conversion in CLG is less than 100 % at the point of maximum CGE. MCP is shown to be the optimal operation point since CGE is higher at MCP than that at carbon boundary point. Finally, it has been demonstrated that the optimal performance analysis at MCP can be generalized for feedstock of any H/C and O/C in the form of contour plots.

Ikelos-RWA. Validation of an Automatic Tool to Quantify REM Sleep Without Atonia.

Study Objectives. To present and evaluate an automatic scoring algorithm for quantification of REM-sleep without atonia (RWA) in patients with REM-sleep behaviour disorder (RBD) based on a generally accepted, well-validated visual scoring method, ("Montreal" phasic and tonic) and a recently developed, concise scoring method (Ikelos-RWA). Methods. Video-polysomnographies of 20 RBD patients (68.2 ± 7.2 years) and 20 control patients with periodic limb movement disorder (65.9 ± 6.7 years) were retrospectively analysed. RWA was estimated from chin electromyogram during REM-sleep. Visual and automated RWA scorings were correlated, and agreement (a) and Cohen's Kappa (k) calculated for 1735 minutes of REM-sleep of the RBD patients. Discrimination performance was evaluated with receiver operating characteristic (ROC) analysis. The algorithm was then applied on the polysomnographies of a cohort of 232 RBD patients (total analysed REM-sleep: 17,219 minutes) and evaluated, while correlating the different output parameters. Results. Visual and computer-derived RWA scorings correlated significantly (tonic Montreal: rTM = 0.77; phasic Montreal: rPM = 0.78; Ikelos-RWA: rI = 0.97; all p < 0.001) and showed good to excellent Kappa coefficients (kTM = 0.71; kPM = 0.79; kI = 0.77). The ROC analysis showed high sensitivities (95%-100%) and specificities (84%-95%) at the optimal operation points, with area under the curve (AUC) of 0.98, indicating high discriminating capacity. The automatic RWA scorings of 232 patients correlated significantly (rTM{I} = 0.95; rPM{I} = 0.91, p < 0.0001). Conclusions. The presented algorithm is an easy-to-use and valid tool for automatic RWA scoring in patients with RBD and may prove effective for general use being publicly available.

Study of cooperative wake control for multiple wind turbines under variable wind speeds/directions

In the wind farm control field, wind turbines are normally manipulated to maximize the individual power production which is named the greedy control. However, this greedy control method can lead to massive losses of total wind farm power production, mainly caused by the wake interference between multiple wind turbines. To this end, the cooperative wake control, which seeks the maximum total power production by coordinating each individual wind turbine at the global optimum operation point, can greatly improve the wind farm output performance. In this paper, we investigate the effectiveness of two different cooperative wake control strategies, i.e., instantaneous control and wind-interval based (WIB) control under variable wind speeds/directions scenario. These two cooperative control strategies are achieved based on the power de-rating operation to the upstream wind turbines. Taking three in-line wind turbines as an example, the control parameters of the two upstream wind turbines are cooperatively optimized while the downstream third wind turbine operates at the maximum power coefficient. To account for the multiple wind turbines wake interference, an artificial neural network (ANN) wake model characterized by the fast computational efficiency and great accuracy, in combination with the best wake superposition model chosen to quantify multiple wake effect, is proposed for the control optimization. By comparing to the baseline greedy control, it shows that both cooperative control strategies are effective in improving the power production of the wind farm. More specifically, the WIB control can maintain the power production at the same level of instantaneous control with a maximum difference less than 3%, while it reduces the operating difficulty to a large extent which greatly facilitates its application under realistic more complex wind scenarios.

Preliminary analysis of noisy image lossy compression by discrete atomic transform-based coder

Remote sensing provides data (images) important for many modern applications. Image number and average size tend to increase. This makes their transfer via communication lines, storage, and dissemination problematic. Thus, compression should be applied where lossy compression is mostly used. Most methods of lossy compression assume that images do not contain noise. Meanwhile, images are often noisy, and this should be considered in the design and performance analysis of image compression techniques. Lossy compression has already been studied by several coders. However, it has not been investigated for the recently proposed atomic transform-based techniques that possess several advantages, in particular, the ability to provide privacy of compressed data. The main subject of this paper is the peculiarities of noisy image lossy compression by an atomic transform-based coder. Our goal is to analyze whether the considered compression method provides the noise filtering effect and the so-called optimal operation point. The task is to obtain rated distortion curves for the atomic transform-based coder applied to noisy images and to analyze their behavior for several performance characteristics such as quality metrics and compression ratio. In the first order, the monotonicity of the main dependence is of interest. The main results are as follows. First, it is shown that the dependencies have non-monotonic behavior and the appearance of analogs of optimal operation point is possible, at least, for such metric as maximal absolute error. Second, there is a specific dependence of compression ratio on a parameter called UBMAD that controls compression. Experiments have been performed on several noisy test images having different complexity and contaminated by noise of different intensities. In conclusion, it is demonstrated that one more coder might have optimal operation points for images having a rather simple structure. However, at the moment, it is difficult to predict its existence and the corresponding coder parameters.

Input significance ranking of microalgae continuous culture models

AbstractBackgroundMicroalgal cultures are evolving into a promising ecofriendly technology for a host of applications. To be sustainable, culture conditions need to be optimized and then controlled. One way to develop robust controllers for a cultivation system is by using mathematical growth models to simulate microalga‐based production. In this scenario, engineering design tasks begin by selecting the critical variables of these models.ResultsA new methodology for determining the significance ranking of a model's input factors under steady‐state operation (parameters (e.g. biological, geometrical) and/or process variables) was designed. The sensitivity of biomass response to its inputs was investigated in four different photobioreactor growth models within a nominal operational region. The methodology ranks models’ input factors based on the one‐at‐a‐time Morris method of elementary effects and variance‐based Sobol's method. Such information provided by the presented procedure is valuable as it reveals which input parameters explain most of the variance in model predictions.ConclusionThe methodology allowed the identification of controlled variables and biological parameters to be targeted for enhanced calibration. Furthermore, the presented methodology showed that in continuous reactors the dilution rate is a critical variable of the process. Therefore, it should be controlled. Additionally, most surprisingly, it is observed that controlling the light intensity within the optimum point of operation is not necessarily a crucial task. However, although its manipulation is still important, the accurate calibration of the parameters of the model may represent a greater influence on the biomass response. © 2023 Society of Chemical Industry (SCI).

Analysis of the potential efficiency of post-filtering noisy images after lossy compression

An increase in the number of images and their average size is the general trend nowadays. This increase leads to certain problems with data storage and transfer via communication lines. A common way to solve this problem is to apply lossy compression that provides sufficiently larger compression ratios compared to lossless compression approaches. However, lossy compression has several peculiarities, especially if a compressed image is corrupted by quite intensive noise. First, a specific noise-filtering effect is observed. Second, an optimal operational point (OOP) might exist where the quality of a compressed image is closer to the corresponding noise-free image than the quality of the original image according to a chosen quality metric. In this case, it is worth compressing this image in the OOP or its closest neighborhood. These peculiarities have been earlier studied and their positive impact on image quality improvement has been demonstrated. Filtering of noisy images due to lossy compression is not perfect. Because of this, it is worth checking can additional quality improvement be reached using such an approach as post-filtering. In this study, we attempt to answer the questions: “is it worth to post-filter an image after lossy compression, especially in OOP’s neighborhood? And what benefit can it bring in the sense of image quality?”. The study is carried out for better portable graphics (BPG) coder and the DCT-based filter focusing mainly on one-component (grayscale) images. The quality of images is characterized by several metrics such as PSNR, PSNR-HVS-M, and FSIM. Possible image quality increasing via post-filtering is demonstrated and the recommendations for filter parameter setting are given.

Maximum power point tracking method based on impedance matching for a micro hydropower generator

An innovative maximum power point tracking method based on impedance matching is proposed and applied in a micro hydropower generator. The micro hydropower generator exploits the water head which is derived from the repeated diving of the marine animal to power the marine telemetry tag. The output voltage of the micro generator is intermittent and changes very fast. The present maximum power point tracking methods can’t deal with this type of intermittent and fast changing voltage of the generator. Hence, to track the optimum operation point and improve the energy efficiency, a new MPPT method which tracks the optimum operation point by directly matching the virtual load impedance of the generator to its internal resistance is proposed. The impedance matching law is developed based on the mathematical model. The experiment of the MPPT hardware is conducted, and the experiment result is used to validate the simulation model. Based on the validated model, the analysis results show that under the fluid pressure from 2 MPa to 8 MPa, the energy efficiency of the generator that uses the MPPT method increase to 1.8 to 2.7 times the one with the direct charging method. The results indicates the effectiveness and advantage of the MPPT method in tracking the optimum operation point and improving the energy efficiency in the context of intermittent and fast changing voltage.

BPG-Based Lossy Compression of Three-Channel Noisy Images with Prediction of Optimal Operation Existence and Its Parameters

Nowadays, there is a clear trend toward increasing the number of remote-sensing images acquired and their average size. This leads to the need to compress the images for storage, dissemination, and transfer over communication lines where lossy compression techniques are more popular. The images to be compressed or some of their components are often noisy. They must therefore be compressed taking into account the properties of the noise. Due to the noise filtering effect obtained during lossy compression of noisy images, an optimal operating point (OOP) may exist. The OOP is a parameter that controls the compression for which the quality of the compressed image is closer (closest) to the corresponding noise-free image than the quality of the noisy (original, uncompressed) image according to some quantitative criterion (metric). In practice, it is important to know whether the OOP exists for a given image, because if the OOP exists, it is appropriate to perform the compression in the OOP or at least in its neighborhood. Since the real image is absent in practice, it is impossible to determine a priori whether the OOP exists or not. Here, we focus on three-channel-remote-sensing images and show that it is possible to easily predict the existence of the OOP. Furthermore, it is possible to predict the metric values or their improvements with appropriate accuracy for practical use. The BPG (better portable graphics) encoder is considered a special case of an efficient compression technique. As an initial design step, the case of additive white Gaussian noise with equal variance in the three components is considered. While previous research was mainly focused on predicting the improvement (reduction) of the PSNR and PSNR-HVS-M metrics, here we focus on the modern visual quality metrics, namely PSNR-HA and MDSI. We also discuss what to do if, according to the prediction, an OOP is absent. Examples of lossy compression of noisy three-channel remote sensing images are given. It is also shown that the use of three-dimensional compression provides a compression ratio increase by several times compared with component-wise compression in the OOP.

Large heat pumps with hot water store in local heating systems – Investigation of operation strategies

In the context of the German energy transition, heat pump technology utilizing heat and electricity from renewable sources is an alternative to traditional fossil fuel based boilers, which enables a sustainable heat supply. Therefore, the transformation of a residential area in Germany into a zero-emissions district employing a heat-pump-based local heating system with hot water store is investigated. The heat pumps are supposed to use the return flow of an existing district heating network as heat source. Most commercially available heat pumps have a standard temperature spread from 3 K to 5 K in the evaporator. In this case, the given limited source flow rate leads to a relatively low heat capacity of the heat pumps (output), which requires an unusual large dimension of the hot water store and impairs the economic efficiency of the heating system. To ensure an optimal store dimension, a possible solution is to install a mixing circuit on the heat source side of the heat pump (with a 5 K temperature spread in the evaporator) to increase the temperature spread of the heat source by remixing the return water, whereby the coefficient of performance for heating becomes smaller. Another solution is to raise the temperature spread in the evaporator directly (design of heat exchanger), also in this case, the coefficient of performance for heating diminishes. Here, we present the theoretical investigation of two heat pump configurations as well as operating strategies: a) Installation of a mixing system in the heat pumps with standard temperature spread in the evaporator (5 K) and b) constant water flow through the evaporator with varying temperature spread up to 15 K. The heat pumps were simulated using the program EBSILON®Professional. The simulations show that the strategy a) achieves higher coefficient of performance εh, HP within low heating capacity range (300 kW to 750 kW), while the strategy b) has better coefficient of performance εh, HP within high heating capacity range as well as a larger total heating capacity range up to 1750 kW. The result is a better annual coefficient of performance for heating of the strategy b) in the system simulations (heat pumps and hot water store system operation) using the Program TRNSYS. The hot water store decouples heat pump operation from heat load to ensure that the heat pumps always work at the optimum operating points.

Producer Gas Composition Prediction using Artificial Neural Network Algorithm

Nowadays, methods to increase efficiency in producer gas have become major issues in biomass gasification research. Producer gas is a renewable energy source that does not take as much time to obtain as fossil fuels. It is typically a mixture of combustible gases like carbon monoxide, hydrogen and methane, and non-combustible gases like carbon dioxide and nitrogen. A high percentage volume of combustible composition in the producer gas output will have a high calorific value or heat of combustion. These combustible gases are determined by the design of the gasifier. In today's era of Industrial Revolution 4.0 and Society 5.0, the use of simulation is highly prioritised in all aspects of engineering, especially in gasification applications. Simulation is a useful tool for learning about the governing principles and optimal operating points of the gasification process. Artificial intelligence (AI), is a major focus of Industry Revolution 4.0. In this project, the producer gas composition prediction is studied by computer simulation. The goals are to predict the output producer gas using an algorithm and to compare the trained prediction result with actual experiment data for rice husk gasification. This simulation was created with MATLAB software's artificial neural network (ANN). Three parameters (the height of the gasifier, the diameter of the gasifier, and the weight of the rice husk) are set as input data, and six types of the composition of producer gas (carbon dioxide, carbon monoxide, methane, oxygen, hydrogen, and nitrogen) are set as output data. The algorithm is trained, tested, and verified with the experiment data. It is then used to predict the output gas composition from the parameters of a gasification experiment that has been used before in UiTM’s laboratory. The simulation results of producer gas composition between prediction and actual values revealed a relative error of 1.159 %, 0.370 %, and 0.330 %. These results were less than 9% and were found to give a very good fit to the neural network algorithm.

SUPPLEMENTS TO THE PROBLEM OF ENERGY CONSUMPTION IN REDUCING LIGNOCELLULOSES BIOMASS SIZE TO PRODUCE ENERGY

This article presents complementary results on experimental data on the process of reducing the size of plant biomass by mechanical cutting. According to the source of the experimental data, the problem of statistical modelling of the main parameters describing the process of biomass reduction was approached. A more comprehensive formulation of the list of main system parameters was also attempted, which should be considered in a minimal mathematical model. The results of this article are starting points for a systemic approach to this biomass processing process. A first systematization is fixing 13 parameters that are included in the model of the biomass size reduction phenomenon. The 13 model parameters are divided into three categories: input parameters (5), adjustment parameters (4) and output parameters (4). The performances of the interpolation formulas are evaluated using the global error and the maximum error, varying between 1% and 0.1% for the prior and 3.8% and 0.34% for the latter. Some mathematical models suggest the existence of optimal operating points. Their exploitation can only come as a result of new high-resolution experimental research, at least in terms of rotation speed.

Distributed feedback optimization in fully inverter based islanded microgrids for accurate reactive power sharing and fast response in P - f Droop

A novel distributed feedback optimization (FO) based control method is proposed to replace the existing reactive power- voltage (Q-V) droop control and the related distributed secondary voltage control of grid-forming inverters (GFoIs) in fully inverter based islanded microgrids (MGs). The aims of the proposed controller are to accurately share active and reactive power among GFoIs, regulate bus voltages of the MGs, and improve the transient frequency response of the conventional active power-frequency (P - f) droop control. The design problem is formulated as an optimization problem and FO with gradient descent method is used to optimize the output voltage of GFoIs based on the real-time measurements in the MGs. The proposed method is implemented in a distributed way, eliminating the need of a central controller with a complex communication infrastructure. Comparing with the existing Q - V droop control with its distributed secondary control, simulation results show that the proposed method improves the reactive power sharing accuracy in GFoIs and accelerates the transient frequency response. Moreover, it drives the MGs to their optimal operating points.

Development of a machine learning-based predictive model for prediction of success or failure of medical management for benign prostatic hyperplasia.

To develop a novel predictive model for identifying patients who will and will not respond to the medical management of benign prostatic hyperplasia (BPH). Using data from the Medical Therapy of Prostatic Symptoms (MTOPS) study, several models were constructed using an initial data set of 2172 patients with BPH who were treated with doxazosin (Group 1), finasteride (Group 2), and combination therapy (Group 3). K-fold stratified cross-validation was performed on each group, Within each group, feature selection and dimensionality reduction using nonnegative matrix factorization (NMF) were performed based on the training data, before several machine learning algorithms were tested; the most accurate models, boosted support vector machines (SVMs), being selected for further refinement. The area under the receiver operating curve (AUC) was calculated and used to determine the optimal operating points. Patients were classified as treatment failures or responders, based on whether they fell below or above the AUC threshold for each group and for the whole data set. For the entire cohort, the AUC for the boosted SVM model was 0.698. For patients in Group 1, the AUC was 0.729, for Group 2, the AUC was 0.719, and for Group 3, the AUC was 0.698. Using MTOPS data, we were able to develop a prediction model with an acceptable rate of discrimination of medical management success for BPH.

Autonomous Liquid–Liquid Extraction Operation in Biologics Manufacturing with Aid of a Digital Twin including Process Analytical Technology

Liquid–liquid extraction has proven to be an aid in biologics manufacturing for cell and component separation. Because distribution coefficients and separation factors can be appropriately adjusted via phase screening, especially in aqueous two-phase systems, one stage is frequently feasible. For biologics separation, aqueous two-phase systems have proven to be feasible and efficient. The simple mixer–settler equipment type is still not standard in biologics manufacturing operations. Therefore, a scalable digital twin would be of aid for operator training, process design under the regulatory demanded quality by design approach for risk analysis, design and control space definition, and predictive maintenance. Autonomous operation is achieved with the aid of process analytical technology to update the digital twin to real time events and to allow process control near any optimal operation point. Autonomous operation is first demonstrated with an experimental feasibility study based on an industrial type example of pDNA manufacturing via lysis from E. coli with and without cell separation performance.

An optimal charging scheduler-based charging strategy of a real life hybrid energy storage for a solar and wind equipped student residence

With increasing renewable energy penetration globally, Energy Storage Systems (ESS) act as a vital component for transforming the current energy sector. In the form of Grid connected ESS, Lithium-Ion Battery (LIB) technology is presently the most popular form of ESS, especially because of its fast response capability, efficiency, and reducing market prices, but is not always preferred for long-term storage, due to its relatively shorter lifetime. A Redox Flow Battery (RFB) on the other hand has a higher lifetime and better long-term storage capability, but has a higher upfront cost and reduced round trip efficiency. A Hybrid ESS (HESS) consisting of LIB and RFB offers the advantages of both the technologies, thus making the individual ESS more economical and flexible to use while also improving its cycle lifetime. Such a grid-connected HESS is planned and installed for a student residence at Bruchsal accommodating 150 students and equipped with 220kWp photovoltaics (PV) and 10.5kWp wind-power. In order to control this conglomerate, an Energy Management System (EMS) is deployed which not only controls but also optimizes its operations in real-time. The EMS aims at achieving multiple objectives which include reducing the ESS aging, operating the system at reduced losses, and the most important, improving building self-sufficiency. This paper focuses on the charging strategy of the HESS which is optimized in two folds. First the HESS is operated with a fixed priority-based strategy where the operation efficiency of the High Energy ESS, i.e. RFB is improved. Secondly, based on generation and consumption forecasts of the setup the EMS optimizes the charging of the High Power ESS, i.e. LIB. With the forecasts available, the EMS strategically schedules delayed charging of the individual ESS, which avoids longer relaxation periods at higher SOC and thus the aging caused due to it. Additionally, the optimization algorithm iteratively searches and operates with the possible optimal operation point of the ESS, where conversion losses are minimal under the given circumstances. Results of real-life operation of the setup based on these operation strategies are provided in this work.

Turning any bed into an intensive care unit with the Internet of things and artificial intelligence technology. Presenting the enhanced mechanical ventilator.

The recent Coronavirus disease 2019 (COVID-19) pandemic displayed weaknesses in the healthcare infrastructures worldwide and exposed a lack of specialized personnel to cover the demands of a massive calamity. We have developed a portable ventilator that uses real-time vitals read from the patient to estimate -- through artificial intelligence -- the optimal operation point. The ventilator has redundant telecommunication capabilities; therefore, the remote assistance model can protect specialists and relatives from highly contagious agents. Additionally, we have designed a system that automatically publishes information in a proprietary cloud centralizer to keep physicians and relatives informed. The system was tested in a residential last-mile connection, and transaction times below the second were registered. The timing scheme allows us to operate up to 200 devices concurrently on these lowest-specification transmission control protocol/internet protocol (TCP/IP) services, promptly transmitting data for online processing and reporting. The ventilator is a proof of concept of automation that has behavioral and cognitive inputs to cheaply, yet reliably, extend the installed capacity of the healthcare systems and multiply the response of the skilled medical personnel to cover high-demanding scenarios and improve service quality.

Модель приоритетов обслуживания подач вагонов

Purpose: for the definition of optimal switch point of crane operation between platforms, to perform task simulation of priority choice in car delivery service, the task should feature penalty consideration for excessive idle delivery at freight front. Methods: Methods of probability theory, queuing and mathematical statistics as well as expert assessments are used to find distribution. Results: Priority model for car delivery service has been worked out. The dependences of average cost of idle time of two car delivery under freight operations - from the moment of switch between deliveries at distribution of Erlang of kth order of freight operation implementation time and the distribution special case — exponential one at mixed priority of car delivery service - are determined. Practical significance: Rational distribution of crane operation is proposed allowing to reduce terminal's costs for penalties for excessive idle delivery at freight front.