State Of Unit Research Articles

Evaluating the reliability of microgrids consisting of renewable energy sources using stochastic scheduling based on the data-driven uncertainty set

The paper addresses challenges arising from the unpredictable and probabilistic nature of wind speed and solar irradiance, leading to variable power production in renewable sources. Power imbalance issues in grids result from the difficulty in accurately estimating these variables. The study introduces an improved data-driven uncertainty set, utilizing a neural network trained with extensive historical data from a multi-microgrid system. This set efficiently identifies strong coupling and extracts characteristic scenarios, ensuring high reliability with a minimal number of scenes. The proposed probabilistic model, implemented in the coordination of pumped storage, wind, and solar power plants, incorporates energy limits and storage characteristics. A four-mode IEEE model for storage units allows quick response to system needs. Simulating storage unit states every hour, considering system load, output power, and energy limitations, optimizes power dispatch based on battery capacity. Additionally, the paper addresses the uncertainty in electric power demand in microgrids, proposing an improved data-driven uncertainty set obtained from a neural network. By eliminating unrealistic worst-case scenarios, the approach accurately captures system characteristics, maintaining high reliability while significantly reducing convergence time. The proposed uncertainty set is applied in a novel two-stage robust optimization dispatch model, transforming the optimization problem into a mixed-integer linear programming problem, and solving it using a column and constraint generation algorithm. Numerical case studies verify the feasibility and superiority of the proposed approach in terms of economy and convergence performance compared to existing robust optimization methods.

Spiropyran-Based Photoisomerizable α-Amino Acid for Membrane-Active Peptide Modification.

Photoisomerizable peptides are promising drug candidates in photopharmacology. While azobenzene- and diarylethene-containing photoisomerizable peptides have already demonstrated their potential in this regard, reports on the use of spiropyrans to photoregulate bioactive peptides are still scarce. This work focuses on the design and synthesis of a spiropyran-derived amino acid, (S)-2-amino-3-(6'-methoxy-1',3',3'-trimethylspiro-[2H-1-benzopyran-2,2'-indolin-6-yl])propanoic acid, which is suitable for the preparation of photoisomerizable peptides. The utility of this amino acid is demonstrated by incorporating it into the backbone of BP100, a known membrane-active peptide, and by examining the photoregulation of the membrane perturbation by the spiropyran-containing peptides. The toxicity of the peptides (against the plant cell line BY-2), their bacteriotoxicity (E. coli), and actin-auxin oscillator modulation ability were shown to be significantly dependent on the photoisomeric state of the spiropyran unit.

Sentiment analysis based on long short-term memory model

An important area of natural language processing is text emotion analysis. Emotion analysis is a very meaningful research work and has a broad application prospect, such as social media monitoring, reputation research of commodity brands, market research, and so on. By analyzing the time and content factors of the data, considering the final application scenario, and finally comparing the advantages and disadvantages of the methods, There are three main techniques for analyzing emotions in text: emotion analysis using an emotion dictionary, emotion analysis using machine learning, and emotion analysis using deep learning. Among them, Convolutional neural networks (CNN), recurrent neural networks (RNN), and long short-term memory networks (LSTM) are examples of deep learning-based techniques. For processing temporal relate issues such as video, speech, and text, CNN algorithms often consume a large amount of computational time, especially for processing image datasets, which may encounter specific problems. To address this issue, RNN is more suitable for solving temporal related issues such as video, voice, and text. In natural language, word order is an extremely important feature. RNN may potentially process sequences of any length, add memory units based on the original neural network, handle pre- and post-word dependencies, and process sequences of any length. The main work is as follows: LSTM adds or deletes unit states through a structure called gate, Determine the experimental thinking of text analysis, Crawl and train data sets through AI Studio, pycharm and other tools.

Удосконалення методики визначення нормативів тягових властивостей автомобіля skoda fabia при стендовому діагностуванні

As you know, the main output parameters characterizing the state of the power unit are the power and torque values. These parameters determine the dynamic qualities of the car, and can also affect fuel consumption and the level of harmful emissions.
 The above parameters are checked using traction roller stands. Such stands allow you to reproduce real speed and load modes. Usually, the traction force on the drive wheels is measured on the stands. It is then easy to calculate the power at a given test speed. Traction force is determined, on the one hand, by the output torque of the engine, on the other hand, by mechanical losses in the transmission. In turn, the moment is determined by the perfection of the combustion process, the complete use of the energy of combustion products, and mechanical losses in the engine itself.
 When there is no reliable, experimentally obtained external speed characteristic of an internal combustion engine, Leiderman's formula is usually used for its approximate construction.
 This technique gives a satisfactory approximation of the dependence of power on revolutions. However, the moment curve is noticeably different from the experimental one. due to the fact that it uses only one experimental point - the maximum power and the corresponding revolutions. Therefore, a refined method of building the external speed characteristic using the example of the SKODA FABIA car is proposed using additional equations that specify the values of the empirical coefficients A, B, C.
 On the basis of the refined external speed characteristics of the engine, the revolutions of the crankshaft are converted into speed, and a graph of the permissible traction force against the speed is drawn according to the derived formula. Standards for diagnostic parameters of the power unit in acceleration mode under partial load are also defined.
 The proposed method of registering engine diagnostic parameters to determine their normative values provides an individual approach to diagnosing the technical condition of cars.

Quantum homogenization in non-Markovian collisional model

Collisional models are a category of microscopic framework designed to study open quantum systems. The framework involves a system sequentially interacting with a bath comprised of identically prepared units. In this regard, quantum homogenization is a process where the system state approaches the identically prepared state of bath unit in the asymptotic limit. Here, we study the homogenization process for a single qubit in the non-Markovian collisional model framework generated via additional bath-bath interaction. With partial swap operation as both system-bath and bath-bath unitary, we numerically demonstrate that homogenization is achieved irrespective of the initial states of the system or bath units. This is reminiscent of the Markovian scenario, where partial swap is the unique operation for a universal quantum homogenizer. On the other hand, we observe that the rate of homogenization is slower than its Markovian counter part. Interestingly, a different choice of bath-bath unitary speeds up the homogenization process but loses the universality, being dependent on the initial states of the bath units.

Telematics and diagnosis system for autonomous cargo vehicle

The article deals with the problem of managing the operation and maintenance of autonomous trucks KAMAZ using a telematics and diagnostics system. The functions and composition of the onboard module of a robotic vehicle are described. The main controlled values are determined. The presence of a developed system for monitoring the state of units and assemblies provides a solution to the problem of controlling the modes of the car operation. The structure of the telematics and diagnostics system has been developed and implemented. The main blocks of the proposed system are shown. The main diagnostic and forecasting functions are performed on a workstation with an analytical module. It is proposed to use an approach based on previous conditions matrixes. An example of matrix analysis for a car engine cooling system is given. Modeling of the engine cooling system was carried out and graphs of transient processes were obtained. The analytical module is designed using artificial neural networks to analyze time series of car parameter values. To conduct virtual tests of a car in various operating modes, a simulation model was developed on a stochastic time colored Petri net. The model simulates both the movement of the vehicle to the point of production operations and the change in the technical condition of the car. The model makes it possible to describe and study the influence of random factors on the time of execution of production tasks, to take into account the probabilistic characteristics of failure events or defects in vehicle components and assemblies. The use of an artificial neural network in the analytical module of the diagnostic system workstation makes it possible to predict the technical condition of vehicle components and systems in real time, followed by verification of the dynamics of processes on a simulation model on a Petri net.

Improving cascade reservoir inflow forecasting and extracting insights by decomposing the physical process using a hybrid model

Accurate and reliable inflow forecasting is essential for efficient reservoir operation, which serves various purposes such as flood control, hydropower generation, water supply and irrigation. Although data-driven models have been vastly used to improve forecasting, the lack of interpretability and physical insights, particularly for neural network models, limit their trustworthiness and usability. In this study, we introduce an interpretable hybrid model to decompose inflow into precipitation-runoff and routing processes. This approach not only improves model performance but also facilitates the extraction of valuable hydrological insights. When applied to the Missouri River Basin cascade reservoir systems, the hybrid model demonstrates strong performance when compared to the state-of-the-art hydrologic model HEC-HMS and the widely used Long Short-Term Memory (LSTM) model in predicting reservoir inflow, especially for lower and smaller reservoirs in the cascade reservoir systems. For the upper and larger reservoirs, it achieves substantial improvement in Nash-Sutcliffe efficiency, ranging from 0.05 to 0.08 and 0.28 to 0.32, except for Fort Peck reservoir, when compared with the LSTM and HEC-HMS models. The decomposition of physical processes enables the hybrid model to represent each component more effectively, thereby leading to improved results in simulation and forecasting. Furthermore, the hybrid model shows how meteorological information updates the memory state of the LSTM unit and routing parameters, which in turn influences inflow dynamics, particularly in situations involving snowmelt periods. The proposed approach also allows the extraction of valuable hydrological insights, including the assessment of the contributions of precipitation-runoff processes versus routing to inflow dynamics, dynamic water travel time, and routing coefficients. The case studies have demonstrated the promising capabilities of this interpretable hybrid model in improving reservoir inflow forecasting and providing meaningful hydrological insights into reservoir inflow dynamics.

Access to long-lived room temperature phosphorescence through auration of 2,1,3-benzothiadiazole.

A series of 2,1,3-benzothiadiazole-Au(I)-L complexes have been synthesised, structurally characterised and investigated for their photophysical properties. These are the first organometallic Au(I) complexes containing a C-Au bond on the highly electron-deficient benzothiadiazole unit. The complexes exhibit solution-phase phosphorescence at room temperature, assigned to the intrinsic triplet state of the benzothiadiazole unit that is efficently populated through its attachment to gold. Comparison with routinely reported Au(I) complexes, which include intervening alkenyl linkers, suggests that previous assignments of their phosphorescence as 1π → π*(CCR) might be incomplete. Our observations affirm that, in addition to the heavy atom effect, breaking symmetry in the involved aryl motif may be of importance in controlling the luminescence properties.

Reverse design and optimization of digital terahertz bandpass filters

In this paper, an ingenious reverse design method is applied to the design and optimization of terahertz bandpass filters in order to achieve standardized design of high-performance terahertz functional devices. An equivalent model of subwavelength metasurface mapped to digital space is established. Based on ideal objective functions and constraints, intelligent algorithms begin a bold journey to explore the vast potential structure in the solution space. Through iterative refinement, the algorithm reveals optimal structural patterns, unlocking areas of unparalleled performance. The direct binary search (DBS) algorithm and the binary particle swarm optimization (BPSO) algorithm are compared in optimization process. When using the DBS algorithm to optimize the design area, it takes a long time to poll the logic states of all pixel units point by point, and it is easy to get stuck in the local optimal value. However, BPSO algorithm has stronger global search capabilities, faster convergence speed, and higher accuracy. Through a comprehensive comparison of the device performance optimized by the two algorithms, the solution optimized by BPSO algorithm has better out-of-band suppression performance and a narrower full width at half peak, but slightly lower transmittance at the center frequency. The bandpass filter has a center frequency of 0.51 THz, a bandwidth of 41.5 GHz, and an insertion loss of -0.1071 dB. When considering computational efficiency, DBS algorithm lags behind, the simulation time is 11550 s, while BPSO algorithm only needs 9750 s. Compared with the traditional forward design, the reverse design method can achieve the narrower band, lower insertion loss, better out-of-band suppression and polarization stability. The fine structural changes of the optimal results have a significant influence on spectral performance, demonstrating the superiority and uniqueness of reverse design. This technology contributes to the design and optimization of high-performance and novel functional devices.

Medicago falcata L. Population State and Prospects for Use in North-Western Region

Meadows play an important role in maintaining the floristic diversity of regional floras. The phytocenotic allocation of the Medicago falcata L. population has not been sufficiently studied, as well as patterns of distribution of its units and unit groups, viability in a specific habitat under various anthropogenic influences. The growth and development of Medicago falcata L. populations under optimal conditions are becoming increasingly relevant and are promising in theoretical and applied aspects.The purpose is to study morphological characteristics and search for valuable forms of Medicago falcata L. to create varieties with high seed and feed productivity in the process of breeding work. The morphological characteristics of plants were studied depending on the age state and distribution of units in the territory they occupied. We have established that the total number of plants in the juvenile state in the population varies little from year to year, although the quantitative ratios of young plants of individual species can fluctuate significantly. This indicates that units in the juvenile state occupy a common ecological niche in the cenosis. The population of Medicago falcata L., growing in the Shelon meadow in the Soltsydistrict, differ in the methods of vegetation resumption, with numerous well-leafed shoots and high ecological plasticity. The formed meadow ecosystems depend not only on the natural conditions in their locations, but also on the influence of anthropogenic factors, which in turn affects the biogeocenosis (living organisms and their interaction with the environment) and, accordingly, the species composition of the meadow. There are rosette-free monocyclic vegetative and generative shoots, with a well-developed root system vertically and horizontally in the soil profile.

Диагностика электротехнических компонентов гидротехнического оборудования: эффективность комплексного подхода

Diagnostics of electrical components of hydrotechnical equipment used in agricultural production within the planned maintenance intervals with continuous analysis of residual resource is useful for reducing equipment operation costs. The use of inexpensive universal diagnostic equipment in conjunction with primary analysis of the state of units makes it possible to detect 60 to 80% of pre-failure states. To ensure efficient application of complex approach to primary diagnostics of electrical components of hydraulic equipment used in agricultural production, the authors considered the most common electric motors of pumping units, electrohydraulic distributors and switching equipment. Complex diagnostics included the use of a thermal imager, vibration signal processing systems and current signal control systems. The vibrograms of equipment control points were analyzed, the state of bearing units, cavitation characteristics of pumps were assessed by methods of acoustic and vibration diagnostics, hydraulic equipment leaks were searched, power cables breakdown zones were detected in accordance with methodical instructions for determining the breakdowns of power cables with voltage up to 10 kV RD34.20.516‑90. The article shows the possibilities of applying the diagnostics methodology based on surface thermograms to determine typical defects. Particularly discussed are the parameters of current signals from the electromagnet coils observed in case of distribution equipment malfunction. The authors give comparative evaluation of diagnostics methods with calculated efficiency determination. It is established that the method of combined diagnostics using the analysis of external thermal fields can halve the time of preliminary diagnostics of faults with a stable forecast of the failure time 2‑3 months before the critical state. This reduces the costs for detection of unit failures and simplifies the planning of maintenance procedures.

Self-sensing concrete masonry structures with intrinsic abilities of strain monitoring and damage detection

Self-sensing cementitious materials are emerging technologies for Structural Health Monitoring (SHM) of civil structures. Their application for SHM of concrete masonry elements was not investigated in previous studies. The effects of triaxial strain/stress states of masonry joints and units on their self-sensing response are still unknown. Therefore, this work aimed to investigate mechanical and self-sensing properties of masonry elements produced with self-sensing mortar joints, solid concrete bricks and/or hollow concrete blocks fabricated with carbon black nanoparticles. The intrinsic abilities of strain monitoring and damage detection were evaluated in piezoresistive tests of masonry prisms, based on variations in the type of unit, mortar bedding approach, bonding arrangement, relative strength of mortar and units, joint thickness and location of self-sensing regions. These variations did not affect significantly the gauge factor of cementitious sensors embedded into the prism units. In contrast, the gauge factor of self-sensing horizontal or vertical joints was statistically affected by most of these variations. Stages of balance and abrupt increases in fractional changes in electrical resistivity (FCRs) during the plastic regime of the prisms provided an anticipated warming associated with the propagation of vertical and diagonal cracks associated with tensile splitting and crushing failure of units. The strong non-linearity on the electrical output of self-sensing joints provided an evidence of the mortar pore collapse. In conclusion, the self-sensing units and mortar joints were found to be promising alternatives for strain monitoring and damage detection in smart concrete masonry structures.

Internal Heavy-Atom Effect on Visible-Light-Induced Cyclization Reaction in Diarylethene-Perylenebisimide Dyads.

All-visible-light switchable diarylethene-perylenebisimide (DAE-PBI) dyads having bromine heavy atoms in the molecule were designed and synthesized. Very recently, we found a unique visible-light-induced cyclization reaction in a DAE-PBI dyad. The dyad exhibited reversible cyclization and cycloreversion reactions upon alternate irradiation with green (500-550 nm) and red (>600 nm) light. From the experimental results, it was suggested that the triplet state of DAE unit was generated via multiplicity conversion based on intramolecular energy transfer from the singlet excited state of PBI unit and that the cyclization reaction of DAE unit proceeded from the triplet state. In addition, it was revealed that the reactivity remarkably increased in a solvent containing heavy atoms such as carbon tetrachloride and iodoethane (i.e., external heavy-atom effect). Based on such results, in this study, we attempted to design and synthesize novel DAE-PBI dyads introducing bromine heavy atoms at different positions in the molecule. The synthesized dyads exhibited higher quantum yields of photocyclization reaction under visible-light irradiation even in a heavy-atom-free solvent compared to the previous dyad having no heavy atoms. The magnitude of enhancement well correlated to the contribution ratio of atomic orbital of bromine to the molecular orbital in LUMOs. These results indicated that the internal heavy atom effectively contributed to the visible-light-induced cyclization reaction in DAE-PBI dyads. Such an internal heavy-atom effect will pave the way for new molecular design to develop all-visible-light-activatable molecular switches.

The Influence of Hydraulic Characteristics on Structural Performance in a Pump-Turbine under No-Load Conditions

The operating state of a pump-turbine unit under no-load conditions is directly related to its safe and stable operation. In order to probe into the influence of hydraulic characteristics on structural performance, a pump-turbine assembled in China is selected for research by using CFD (computational fluid dynamics) and unidirectional FSI (fluid–structure interaction) methods. The vortex distribution and the law of pressure pulsation propagation are analyzed to capture the peculiar flow phenomena. The results show that the vortex distribution in the runner channel appears initially at the suction side of the blades but then propagates toward the pressure side with GVO. This produces rotating stall frequencies (0.7fn) and a combination of the RSI, asymmetry of the water ring in vaneless space, and high-amplitude pressure pulsations in the downstream channel close to the runner inlet and elbow section of the draft tube. This, in turn, is associated with the structural stress of the runner and guide vane. The stress level of the guide vane becomes alleviated under no-load conditions with large GVO, but the stress distribution of the runner is no longer symmetrical, which aligns with the vortex evolution in the runner passage. The stress concentration that develops further along the blade root increases the structural failure, which is also captured and verified as a crack in the prototype runner. The phenomena suggest that the RPT should avoid operating under no-load conditions with large GVO as far as possible. Therefore, in the design or optimization of the pump-turbine unit, the structures of the guide vanes and runner could be treated as a whole to investigate the resulting internal flow and structure characteristics.

Analysis of Solid-Liquid Two-Phase Flow in the Area of Rotor and Tailpipe

In order to study the internal flow state and wear law of a bulb cross-flow unit based on the particle non-uniform phase model in the Euler–Euler method, the solid-liquid two-phase flow condition of the hydraulic turbine under different solid-phase diameters, concentrations, and guide vane openings is calculated. The results show that (1) Under the same solid-phase physical parameters, the distribution of solid-phase concentration on the working surface of the blade is positively correlated with the opening degree of the guide vane, the concentration of the solid phase on the back of the blade is negatively correlated with the opening degree of the guide vane. (2) The addition of the solid phase changes the time-domain period of pressure pulsations at the rotor inlet and the tailpipe inlet under clear water conditions, and the tailpipe pressure pulsation coefficient decreases with increasing solid-phase concentration. The pressure pulsation coefficient increases with increasing solid-phase diameter and concentration at the inlet of the rotor. (3) Numerical simulation of the wear characteristics of cross-flow turbine by Finne’s wear model reveals that the two-phase flow condition with high concentration, large particle size and small openings has a more serious effect on turbine blade wear.

Digital Mechanical Metamaterial: Encoding Mechanical Information with Graphical Stiffness Pattern for Adaptive Soft Machines.

Inspired by the adaptive features exhibited by biological organisms like the octopus, soft machines that can tune their shape and mechanical properties have shown great potential in applications involving unstructured and continuously changing environments. However, current soft machines are far from achieving the same level of adaptability as their biological counterparts, hampered by limited real-time tunability and severely deficient reprogrammable space of properties and functionalities. As a steppingstone toward fully adaptive soft robots and smart interactive machines, an encodable multifunctional material that uses graphical stiffness patterns is introduced here to in situ program versatile mechanical capabilities without requiring additional infrastructure. Through independently switching the digital binary stiffness states (soft or rigid) of individual constituent units of a simple auxetic structure with elliptical voids, in situ and gradational tunability is demonstrated here in various mechanical qualities such as shape-shifting and -memory, stress-strain response, and Poisson's ratio under compressive load as well as application-oriented functionalities such as tunable and reusable energy absorption and pressure delivery. This digitally programmable material is expected to pave the way toward multienvironment soft robots and interactive machines.

Abstract A017: Computer simulation and quantification of the mass-action law-base pharmacodynamics parameters for anti-cancer and anti-viral therapeutics for single drugs and their combinations

Abstract The biophysical and biochemical fundamental principle of the mass-action law (MAL) was used as the solo model for broad-spectrum system analysis. These included linear and circular sequential analysis, pattern analysis, combinatorial analysis, and mathematical induction-deduction, for deriving unified general pharmacodynamics/biodynamics/combination index/and bioinformatics (PD/BD/CI/BI). The two unified general dose-effect dynamics theories are: “action” (Median-effect Eq., MEE) and “interaction” (Combination index Eq. CIE). All terms of MEE and CIE are dimensionless relativity ratios; thus, the applicability of the dynamics is regardless of unit (e.g., uM, IU, rad), mechanisms (e.g., sequential, ordered, random; competitive, non-competitive, uncompetitive), and physical states (e.g., chemicals, biologicals, radiation, photonic, and thermal effects), and their interactions. The unified “action” dynamics (MEE) is applicable for R&D for MAL identity’s basic-PD parameters: Dm, the median-effect dose, signifying potency; and m, the dynamic-order, signifying shapes of dose-effect curves (DEC), for single drugs or in combinations. The Dm and m values can be determined by computer simulation based on MEE and CIE algorithms computer software, which allows digital determination of synergism (CI<1), additive effect (CI=1), and antagonism (CI>1) by automated simulations, instantly, after Dose and Effect paired data entries. The MEE algorithm and plot (MEP) linearized DEC into straight lines. This linearization principle led to the minimum two data points theory (MTDPT), which adds “dose-zero” and “Dm)” two dose-data points to the PD algorithm by default, for dynamics simulations. The MTDPT is the theoretical basis for Econo-green, efficient, cost-effective biomedical research in vitro, animals, humans, environmental, agricultural, marine, and food sciences. This allows using small-size experimental studies, reducing cost, and increasing efficiency by computer simulation. This paper illustrates the examples (experimental or clinical trial protocol) of PD design, step-by-step, for dose-dependent PD computer analysis, for anti-cancer and anti-viral agents, e.g., fludelone +/- panaxytriol in vitro; microtubule-stabilization agent +/- T607 antineoplastic in colon carcinoma HCT116 tumor-bearing nude mice; as well as anti-HIV clinical trials [nucleoside reverse transcriptase inhibitor +/- Interferon], are used for detailed illustrations for study design, computerized data analysis, and automated print-out. Thus, the unified general MAL- PD/BD/CI/BI can be used in vitro, in animals, and in clinical trials, for digital informatics or ranking. It is worth noting that the Google Scholar and Web of Science bibliometrics (as of May 29, 2023), on the MAL-PD/CI original article (Chou TC & Talalay P, 1984), Review (Chou TC, 2006), and Perspectives (Chou TC, 2010) have received a total of 18,046 citations in over 1,500 journals. For relevant 2023 references, please visit https://doi.org/10.1016/j.jbc.2023.103419 and dx.doi.org/10.1124/jpet.122.185320. Citation Format: Ting-Chao David Chou. Computer simulation and quantification of the mass-action law-base pharmacodynamics parameters for anti-cancer and anti-viral therapeutics for single drugs and their combinations [abstract]. In: Proceedings of the AACR-NCI-EORTC Virtual International Conference on Molecular Targets and Cancer Therapeutics; 2023 Oct 11-15; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2023;22(12 Suppl):Abstract nr A017.

Intelligent intertraverse dynamograph of the mesdoza of sucker-rod deep-pumping units

Rod downhole pumping units widely used in oil production are described. It is shown that oil production depends on the mode of operation of these units, which is selected based on the results of the analysis of closed dynamometer charts. Dynamograms describe the load on the suspension depending on the course of the polished rod of the installation, the quality of the construction of the dynamograms is decisive for diagnosing the technical condition of the installation. The trends in the development of existing dynamometer systems and methods for diagnosing the state of a sucker rod pumping unit are analyzed. One of the ways to create an intelligent intertraverse mesdoza dynamograph based on the mesdoza of a pocket dynamograph using modern pressure, acceleration, temperature sensors, STM32 controllers, a graphic liquid crystal display with an I2C interface, radio communication protocols and digital technologies for processing periodic sensor signals is presented. Based on the analysis of existing dynamometer systems and force and stroke sensors installed in various places of the installation design, and taking into account modern advances in technology and technologies for analyzing the noise of the measured signals, the possibility of developing an intelligent stationary dynamograph is shown. This dynamograph must provide early diagnostics of the technical condition of the installation and meet the following criteria: low cost; ease of installation; high sensitivity, reliability, flexibility and accuracy. A block diagram of an intelligent intertraverse dynamograph of the mesdoza of a sucker-rod deep pumping unit, based on the mesdoza of a pocket dynamograph, is presented. The proposed intelligent intertraverse dynamograph will be useful for early diagnostics of the technical condition of downhole pumps and tubing pipes connecting them with ground equipment, which will ultimately help to improve the reliability of oil production equipment. The proposed intelligent intertraverse dynamograph can also be used in other areas of technology where there is a need to measure force.

Automated estimation of road facilities within 5D modeling paradigm

The paper is aimed at considering the possibility of automated estimation of road facilities on the basis of data obtained from the bill of quantities at the design or construction stages. The authors accepted Tomita Parser as a for extracting facts and parameters from the bill of quantities. Further comparison of quantities of work with Federal Unit Rates and State Itemized Cost Estimate Standards is carried out by the program algorithm developed by the authors for calculating and estimating costs. The presented manual method for creating a database compiled on the basis of comparing the bill of quantities with the above federal regulations may be considered reliable. However, large data sets require machine learning technologies to be applied. The program algorithm considers many estimation settings, in particular, assigning the index to construction and installation works, the total coefficient to the positions by types of work (for example, performance of work on one half of the roadway), etc. The finished estimate is generated in a *.xml file and can be further integrated into the road information model and converted into *.gge format for using by the Federal autonomous institution “Main Department of State Expertise”. The results showed that all key parameters from the bill of quantities were taken into account by the program algorithm. The generated estimate considered all coefficients and prices to accurately reflect the cost of the works described in this bill.

Improving cement production process with data-augmented sequence to sequence-Wasserstein generative adversarial networks model for accurate prediction of f-CaO.

This paper proposes a method to address the issue of insufficient capture of temporal dependencies in cement production processes, which is based on a data-augmented Seq2Seq-WGAN (Sequence to Sequence-Wasserstein Generate Adversarial Network) model. Considering the existence of various temporal scales in cement production processes, we use WGAN to generate a large amount of f-CaO label data and employ Seq2Seq to solve the problem of unequal length input-output sequences. We use the unlabeled relevant variable data as the input to the encoder of the Seq2Seq-WGAN model and use the generated labels as the input to the decoder, thus fully exploring the temporal dependency relationships between input and output variables. We use the hidden vector containing the temporal characteristics of cement produced by the encoder as the initial state of the gate recurrent unit in the decoder to achieve accurate prediction of key points and continuous time. The experimental results show that the Seq2Seq-WGAN model can achieve accurate prediction of continuous time series of free calcium and offer direction for subsequent production planning. This method has high practicality and application prospects, and can provide strong support for the production scheduling of the cement industry.