Search

Modeling of stator parts of power units of thermal and nuclear power plants is carried out according to several standard schemes. Common methods of modeling systems with non-uniform and non-stationary contact of stator elements lead to significant errors in calculating the amplitudes of forced vibration displacement at a given external load. The object of this paper is to analyze errors in calculating the displacement amplitudes of forced oscillations in different modeling schemes according to the standard approach and developing a method for constructing a quasi-dynamic model taking into account the time-varying contact of the system’s stator parts as well as other factors. To do this, the author has developed a series of models with different degrees of sampling and considering different system factors. Particular attention is paid to the impact of factors arising from the long operation of the power plant (subsidence of foundation). The modeling was carried out taking into account the calculation method. The finite element method was used to perform calculations. The results of the calculation of forced oscillation displacement amplitudes are available to show the areas of application of different types of three-dimensional models of the steam power unit. The results of research conducted on the developed models show the impossibility of using standard simulation schemes for systems with a variable contact between its stator elements, as shown by the example of a power unit with steam turbine. The efficiency of using the models taking into account the variable contact between the stator parts and other factors to calculate the displacement amplitudes of the forced oscillations with high accuracy is likewise presented. Every specialist should be aware of the factors that influence the research results. This paper is concerned with assessment of the influence of a number of factors on the results of calculating the vibrations of a complex mechanical system.

Abstract In this paper, we present how precise deep learning algorithms can distinguish loss circulation severities in oil drilling operations. Lost circulation is one of the costliest downhole problem encountered during oil and gas well construction. Applying artificial intelligence can help drilling teams to be forewarned of pending lost circulation events and thereby mitigate their consequences. Data-driven methods are traditionally employed for fluid loss complexity quantification but are not able to achieve reliable predictions for field cases with large quantities of data. This paper attempts to investigate the performance of deep learning (DL) approach in classification the types of fluid loss from a very large field dataset. Three DL classification models are evaluated: Convolutional Neural Network (CNN), Gated Recurrent Unit (GRU) and Long-Short Term Memory (LSTM). Five fluid-loss classes are considered: No Loss, Seepage, Partial, Severe, and Complete Loss. 20 wells drilled into the giant Azadegan oil field (Iran) provide 65,376 data records are used to predict the fluid loss classes. The results obtained, based on multiple statistical performance measures, identify the CNN model as achieving superior performance (98% accuracy) compared to the LSTM and GRU models (94% accuracy). Confusion matrices provide further insight to the prediction accuracies achieved. The three DL models evaluated were all able to classify different types of lost circulation events with reasonable prediction accuracy. Future work is required to evaluate the performance of the DL approach proposed with additional large datasets. The proposed method helps drilling teams deal with lost circulation events efficiently. Article Highlights Three deep learning models classify fluid loss severity in an oil field carbonate reservoir. Deep learning algorithms advance machine learning a large resource dataset with 65,376 data records. Convolution neural network outperformed other deep learning methods.

Abstract This paper proposes a wireless network traffic prediction model based on Bayesian Gaussian tensor decomposition and recurrent neural network with rectified linear unit (BGCP-RNN-ReLU model), which can effectively predict the changes in the upstream and downstream network traffic in a short period of time in the future. The research is divided into two parts: (i) The missing observations are imputed by an algorithm based on Bayesian Gaussian tensor decomposition. (ii) The recurrent neural network is used to forecast the true observations only rather than both true and estimated observations. The results show that, compared with other combined models of missing data imputation and neural networks, the BGCP-RNN-ReLU model proposed in this paper has the smallest prediction error for both the upstream and downstream traffic. The new model achieves better forecasting precision, and thus can help to regulate the load of communication station to reduce resource consumption. Highlights The problem of forecasting wireless network traffic with missing values is divided in two stages to handle. A newly propose d method can more efficiently impute missing values in wireless network traffic data. Simple recurrent neural network obtains better prediction performance than other complex networks.

Abstract This paper proposes a probabilistic small-signal stability analysis method based on the polynomial approximation approach. Since the correct determination of unknown coefficients has a direct effect on the accuracy of the polynomial approximation method, this paper presents a method for determining these coefficients, with high coverage on the probabilistic input domain of the problem. In this method, by increasing the number of random input variables, the proposed method can continue to maintain its efficiency. After determining the unknown coefficients, the load flow results and the system state matrix are determined for random changes of all loads based on the Hermite polynomial approximation. Then, the small-signal stability of the system is probabilistically evaluated based on stochastic analysis of the system eigenvalues. The consistency and validity of the proposed method are demonstrated based on the simulation studies in the MATLAB® software environment. In the simulation studies, the performance of the proposed method is examined by comparison with Point Estimation, Cumulant, Monte Carlo, and Chebyshev polynomial-based methods, for IEEE 14-bus and IEEE 39-bus test systems. Article highlights Probabilistic small-signal stability analysis of power systems Modeling of governing equations of power system based on Hermite polynomial approximation Forming the Collocation matrix based on a robust method

This paper presents an approach to access real time data from underground mine. Two advance technologies are presented that can improve the adverse environmental effect of underground mine. Visible light communication (VLC) technology is incorporated to estimate the location of miners inside the mine. The distribution of signal to noise ratio (SNR) for VLC system is also studied. In the second part of the paper, long range (LoRa) technology is introduced for transmitting underground information to above the surface control room. This paper also includes details of the LoRa technology, and presents comparison of ranges with existing above the surface technologies.

The two degree of freedom ball balancing table (BBT) is a well-known didactic tool used to evaluate the effectiveness and performances of many control algorithms for dynamic systems. The present paper proposes to control the ball position of the BBT system via a linear feedback controller based on a functional observer. The parameters of the linear functional observer are determined by applying the direct method which requires neither a Sylvester equation resolution nor canonical transformations. The use of a digital controller has motivated the elaboration of the equations in the discrete time case. In this work, the BBT is tested in real-time to evaluate the proposed controller performances when stabilizing a ball on a reference point. This paper is a continuity of the previous work [12], in which only simulation results have been carried out.

This paper presents a new bed for bedridden patients. It has provision for using a commode by pulling it up to the bed height by a specially designed mechanism. For commode use, it has an opening in the bed. A mattress on the platform covers the commode opening. After use, the commode is pushed back to its position below the bed. Then, the commode opening gets covered. This bed also has provision for side turning of the patient. The left and right-side turning mechanisms can operate independently to allow the patient to sleep on the left or right side. It is possible to turn both sides together to avoid touching the middle part of the back to the bed surface to avoid pressure ulcers. The side turning may be simultaneously operated with the backrest. A separate mechanism is there for this purpose. In addition, the bed can be converted into a chair position and has other utilities like a washbasin, folding table, and cistern attached to it. The bed design and development are in two stages. First, a bed with a commode is designed. Later, this design is improved to add a side turning mechanism. Computer modeling, simulation, fabrication, and testing are employed to plan and check bed operations and positions. The mechanical performance of the bed is compared with the simulation results, and the errors are negligible. The bed is compared with the reported designs in the literature. It is a novel product. It will be excellent equipment for bedridden patients and caregivers. Article Highlights Presents a new bed design for bedridden patients.. The bed has the provision of a commode and a facility for the side-turning of the patient. Reduces discomfort for the patients, chances of bedsores, and efforts of the caretaker.

Digitization is developing fast and has become a powerful tool for digital planning, construction and operations, for instance digital twins. Now is the right time for constructive approaches and to apply ethics-by-design in order to develop and implement a safe and efficient artificial intelligence (AI) application. So far, no study has addressed the key research question: Where can corporate digital responsibility (CDR) be allocated, and how shall an adequate ethical framework be designed to support digital innovations in order to make full use of the potentials of digitization and AI? Therefore, the research on how best practices meet their corporate responsibility in the digital transformation process and the requirements of the EU for trustworthy AI and its human-friendly use is essential. Its transformation bears a high potential for companies, is critical for success and thus, requires responsible handling. This study generates data by conducting case studies and interviewing experts as part of the qualitative method to win profound insights into applied practice. It provides an assessment of demands stated in the Sustainable Development Goals by the United Nations (SDGs), White Papers on AI by international institutions, European Commission and German Government requesting the consideration and protection of values and fundamental rights, the careful demarcation between machine (artificial) and human intelligence and the careful use of such technologies. The study discusses digitization and the impacts of AI in construction engineering from an ethical perspective. This research critically evaluates opportunities and risks concerning CDR in construction industry. To the author’s knowledge, no study has set out to investigate how CDR in construction could be conceptualized, especially in relation to digitization and AI, to mitigate digital transformation both in large, medium- and small-sized companies. This study applies a holistic, interdisciplinary, inclusive approach to provide guidelines for orientation and examine benefits as well as risks of AI. Furthermore, the goal is to define ethical principles which are key for success, resource-cost-time efficiency and sustainability using digital technologies and AI in construction engineering to enhance digital transformation. This study concludes that innovative corporate organizations starting new business models are more likely to succeed than those dominated by a more conservative, traditional attitude.

Cyberphysical microelectrode-dot-array (CP-MEDA)-based digital microfluidic biochip (DMFB) is attracting more attention than its predecessor of traditional DMFB. Conventional DMFBs are mostly unable to recover from the errors incurred at assay run time, and thus, it leads to unacceptable results. Recent studies have revealed the vulnerability of CP-DFMBs to detect malicious intrusions during its design or operational phase. In this paper, we have analysed such vulnerable scenarios that have been utilized by the routing-based-synthesis approach on a CP-MEDA. We have also depicted the mixing operations based on routing for ensuring better security measures. Various attacking scenarios have been demonstrated on a MEDA-based DMFB, and moreover, checkpoint-based intrusion detection method has been proposed for the RBS technique. The effectiveness of our approach is compared with the available benchmark assays. Our proposed method has shown significant improvement over the existing state-of-the-art procedures in terms of assay execution time and intrusion detection rate.

Abstract In this paper, designing a hybrid stand-alone photovoltaic/wind energy system with battery storage (PV/WT/Batt) is presented to minimize the total cost of the hybrid system and considering reliability constraints for Zanjan city in Iran country considering generation and load uncertainties. The total cost includes the cost of the system components and load losses cost and also deficit power probability-hourly interruption probability index, which is defined as the reliability constraint. The optimal size of the system components is found using an improved crow search algorithm based on mutation and crossover operators of the genetic algorithm to prevent premature convergence. The optimal designing of the hybrid system is implemented considering the uncertainty of photovoltaic and wind resources as well as the load demand of the system based on the probability distribution functions of uncertain parameters using Monte Carlo simulation. The simulations are implemented in two deterministic and probabilistic scenarios. In the deterministic scenario, the optimal design of various combinations of the hybrid system is investigated in view of the system cost and deficit power probability probability-hourly interruption probability index based on the improved crow search algorithm with and without the cost of load loss. The results of the deterministic scenario showed that the system cost is increased considering the cost of load loss, but the system load is supplied with higher reliability. Also, the deterministic results cleared the superiority of the improved crow search algorithm compared with conventional crow search algorithm and particle swarm optimization methods. Moreover, the results of the system designing showed that considering the uncertainty, the system cost increases, and the reliability is significantly improved. The results cleared that the system cost in deterministic and probabilistic designing is obtained 1.0108 M$ and 1.210 M$, respectively for load supply equal to 153.87 MWh. So, due to the inherent nature of the uncertainty in the renewable resources power generation as well as continuous load changes, a probabilistic approach of the hybrid system designing is necessary to know the accurate cost and reliability. Article highlights Effective performance of the proposed method in system designing with lowest cost and higher reliability. Achieving to a more reliable hybrid energy system with considering cost of load losses in designing. Increasing the system the cost and improving the reliability considering uncertainties of generation and load.

Amharic ("Image missing") is the official language of the Federal Government of Ethiopia, with more than 27 million speakers. It uses an Ethiopic script, which has 238 core and 27 labialized characters. It is a low-resourced language, and a few attempts have been made so far for its handwritten text recognition. However, Amharic handwritten text recognition is challenging due to the very high similarity between characters. This paper presents a convolutional recurrent neural networks based offline handwritten Amharic word recognition system. The proposed framework comprises convolutional neural networks (CNNs) for feature extraction from input word images, recurrent neural network (RNNs) for sequence encoding, and connectionist temporal classification as a loss function. We designed a custom CNN model and compared its performance with three different state-of-the-art CNN models, including DenseNet-121, ResNet-50 and VGG-19 after modifying their architectures to fit our problem domain, for robust feature extraction from handwritten Amharic word images. We have conducted detailed experiments with different CNN and RNN architectures, input word image sizes, and applied data augmentation techniques to enhance performance of the proposed models. We have prepared a handwritten Amharic word dataset, HARD-I, which is available publicly for researchers. From the experiments on various recognition models using our dataset, a WER of 5.24 % and CER of 1.15 % were achieved using our best-performing recognition model. The proposed models achieve a competitive performance compared to existing models for offline handwritten Amharic word recognition.

This study treats a multi-item single-machine dynamic lot size scheduling problem with sequence-independent setup cost and setup time. This problem has various heterogeneous decision features, such as lot sizing and lot sequencing. Traditionally, the problem has been treated by putting artificial constraints on the other feature in order to determine one of them. The proposed model is a Lagrange decomposition and coordination method that aims at simultaneous optimization of these decision features; however, smooth convergence to a feasible near-optimal solution has been a problem. So, in this paper, we propose a model that improves the constraint equation of the existing model and showed that it satisfies the Karush–Kuhn–Tucker (KKT) condition when we obtained a feasible solution. In addition, by applying the surrogate gradient method, which has never been applied to this problem before, it was shown that smoother convergence than before can be achieved through actual example of printed circuit board.

This paper addresses the m-machine no-wait Flow Shop Scheduling with Setup Times (NW-FSSWST). Two performance measures: total flow time and makespan are considered. The objective is to find a sequence that minimizing total flow time ($$\sum C_{j}$$ ∑ C j ) and makespan ($$C_{j}$$ C j ) simultaneously. A Hybrid Genetic Local and Global Search Algorithm (HGLGSA) is proposed to solve the NW-FSSWST for two performance criteria. The hybrid genetic algorithm is constructed by insert-search and self-repair algorithm with self-repair function. The proposed HGLGSA is tested on 192 benchmark problems of NW-FSSWST in the literature. A full factorial experimental design is made for determined the best parameter sets that improve the performance of the proposed algorithm. The computational results are compared with the benchmark solutions from the literature. The experimental results demonstrate the effectiveness and efficiency of the proposed HGLGSA for solving NW-FSSWST.

Abstract This paper investigates the problem of load frequency control by the application of metaheuristic optimisation techniques. In order to analyse the effect of the system model on the performance of the algorithm, two different two-area systems are considered, modelled using the Matlab/Simulink package. The first one consists of a reheat thermal area and a hydro area while the second one is made up of identical reheat thermal areas. To take into consideration the effects of practical constraints, nonlinearities such as Generation Rate Constraint, Governor Deadband and Boiler Dynamics are introduced. The Shuffled Frog Leaping Algorithm and Teaching Learning Based Optimisation are applied followed by a proposed hybrid of both algorithms to tune Proportional-Integral-Derivative (PID) controllers for the different areas of the systems, taking into account step load changes as inputs. The aim of this proposed algorithm is to merge the qualities of the individual algorithms to provide a more efficient one, converging faster to the optimal gains of the controllers. The results obtained proved the satisfactory performances of all algorithms and superiority of the hybrid Shuffled Frog Leaping Algorithm -Teaching Learning Based Optimisation technique in controlling frequency level in both systems investigated, where the main control measures such as peak values, settling times and steady-state values have been considered. Article Highlights When considering a system of electrical mechanisms installed to produce, distribute and use electric energy, it is important to match supply with demand in order to keep frequency almost constant so as to ensure the safe and reliable use of equipment and maintain stability. The combination of two different algorithms efficiently improved the performance of the control system since the new system benefitted from the qualities of both strategies. Algorithm-based controllers provided robust and reliable frequency control and may be used to increase the quality of electrical energy under increasing and constantly changing demand.

Abstract Available transfer capability (ATC) plays an important role for both buyers and suppliers in the deregulated power system. As the integration of renewable energy sources is taking place very quickly in the system. It is not suggested to place wind generation (WG) at any point of the system. In this paper, the optimal location of buses for WG using a novel sensitive based formula is proposed. The zones are formed using the transmission congestion distribution factor (TCDF) values. The average transmission congestion distribution factor (ATCDF) is used to locate the optimal point of the system. When the WG is integrated into the system its obvious ATC increases but at which point the ATC values will be more is considered as the main objective. The WG is placed at every bus in the zone created by the TCDF value and the ATC value is calculated. The test is performed on the IEEE-30 bus system in MATLAB coding environment. The comparison result shows the ATC value enhances more when the WG is placed according to the proposed method. The proposed method also helps to find multiple locations for WG installation according to ATCDF value. Highlights Average TCDF value is proposed to find the optimal bus location for getting more ATC value. Integration of WG according to highest ATCDF value in the system. Comparison of ATC obtain from the integration of WG at different buses in sensitive zones. Validation of a proposed method by comparing the ATC value obtain from integrating WG at different buses. The optimal bus location based on the ATCDF value gave enhance ATC value compare to the other bus location. ATCDF value helps in finding the multiple locations for integration of WG in the system.

We present a generalized mapping framework that can withstand the challenges incurred by working in unstructured outdoor environments, such as a snowy forest. The proposed method takes advantage of a sensor fusion scheme, where sensors such as cameras and lidars are used in order to reconstruct the surrounding natural environment. Although mapping techniques such as SLAM and ICP cannot themselves properly handle the complexity of natural scenes, they do have the potential to contribute to the global solution in a proposed sensor fusion scheme, based on a factor graph architecture. In this paper, we propose an innovative map registration scheme for visual maps, and show how it can improve the reconstruction quality after data fusion. We also analyze the behavior and sensitivity of factor graphs to uncertainties, by comparing the residual error with different parameter combinations such as variances, using an exhaustive grid search with ground truth comparison. Finally, we suggest an ICP-inferred loop closure, capable of compensating position and attitude drift. The experiments are carried out by recording in a snowy forest using a wearable sensor suite. In the experiments, ground truth was acquired using a millimeter-accurate total station. The proposed framework is shown to be robust and likewise capable of providing estimates that are otherwise unattainable using classic techniques, such as visual SLAM and ICP for lasers. Finally, a visible improvement in the map reconstruction quality is shown, and the proposed framework achieves a translation error of 0.36 m.

The implementation of a dedicated communication mechanism between micro- and macroscale models of the fully coupled multiscale numerical approach is the topic of the paper. The presented solution is based on the System V IPC message queue that is part of modern Linux-based operating systems. Developed and implemented approach is validated with a selected case study of a 3D compression test with the microscale model based on an implicit Johnson–Cook model, which is solved as a separate external process. The communication mechanism can be the basis of the efficient fully coupled FE2 model.

A correction to this paper has been published: https://doi.org/10.1007/s42452-021-04606-4