Automatic Operation Research Articles

Identify the needs of people with disabilities to form a behavioral model for the automatic operation of smart homes with methods based on support vector machine

Identify the needs of people with disabilities to form a behavioral model for the automatic operation of smart homes with methods based on support vector machine

A text mining and network analysis of topics and trends in major nursing research journals.

This study is set to determine the main topics of the nursing field and to show the changing perspectives over time by analysing the abstracts of several major nursing research journals using text mining methodology. Text mining and network analysis. Text analysis combines automatic and manual operations to identify patterns in unstructured data. Detailed searches covering 1998-2021 were conducted in PubMed archives to collect articles from six nursing journals: Journal of Advanced Nursing, International Journal of Nursing Studies, Western Journal of Nursing Research, Nursing Research, Journal of Nursing Scholarship and Research in Nursing and Health. This study uses a four-phase text mining and network approach, gathering text data and cleaning, preprocessing, text analysis and advanced analyses. Analyses and data visualization were performed using Endnote, JMP, Microsoft Excel, Tableau and VOSviewer versions. From six journals, 17,581 references in PubMed were combined into one EndNote file. Due to missing abstract information, 2496 references were excluded from the study. The remaining references (n = 15,085) were used for the text mining analyses. Eighteen subjects were determined into two main groups; research method topics and nursing research topics. The most striking topics are qualitative research, concept analysis, advanced practice in the downtrend, and literature search, statistical analysis, randomized control trials, quantitative research, nurse practice environment, risk assessment and nursing science. According to the network analysis results, nursing satisfaction and burnout and nursing practice environment are highly correlated and represent 10% of the total corpus. This study contributes in various ways to the field of nursing research enhanced by text mining. The study findings shed light on researchers becoming more aware of the latest research status, sub-fields and trends over the years, identifying gaps and planning future research agendas. No patient or public contribution.

Online characterization of solid biofuels via pocket sized and low-cost near-infrared devices during small-scale boiler operation: Model development, evaluation and application tests

Optimal boiler settings are required to ensure high efficiencies during biomass combustion and to enable compliance with emission thresholds despite lower and fluctuating fuel qualities. Thus, an affordable and simple near-infrared retrofit solution for online fuel characterization was developed and tested to allow for automatic adjustments of the boiler control with respect to actual characteristics of the used fuel assortments. To this end, a low-cost online near-infrared spectroscopy device (SCiO™, Consumer Physics), with investment costs less than 1000 € was employed. Partial least squares regression models based on 13 different spectral preprocessing methods for the prediction of moisture, ash, nitrogen and potassium content, as well as the net calorific value were developed and validated in the lab. The resulting model predictions of biomass properties agree in terms of root mean square error and R2 with the reference values obtained from laboratory analysis. Furthermore, a near-infrared retrofit solution was successfully coupled to the fuel feeding system of a commercially available small-scale biomass boiler for automatic and continuous operation. The automatically predicted values obtained by the near-infrared retrofit solution during continuous boiler operation are stable while the predicted values for moisture and net calorific value are in the range of the reference values obtained from the laboratory analysis but with a small offset. Non-reliable results were obtained for ash and nitrogen content.

Autonomous and intelligent optical tweezers for improving the reliability and throughput of single particle analysis

Optical tweezers are an interesting tool to enable single cell analysis, especially when coupled with optical sensing and advanced computational methods. Nevertheless, such approaches are still hindered by system operation variability, and reduced amount of data, resulting in performance degradation when addressing new data sets. In this manuscript, we describe the deployment of an automatic and intelligent optical tweezers setup, capable of trapping, manipulating, and analyzing the physical properties of individual microscopic particles in an automatic and autonomous manner, at a rate of 4 particle per min, without user intervention. Reproducibility of particle identification with the help of machine learning algorithms is tested both for manual and automatic operation. The forward scattered signal of the trapped PMMA and PS particles was acquired over two days and used to train and test models based on the random forest classifier. With manual operation the system could initially distinguish between PMMA and PS with 90% accuracy. However, when using test datasets acquired on a different day it suffered a loss of accuracy around 24%. On the other hand, the automatic system could classify four types of particles with 79% accuracy maintaining performance (around 1% variation) even when tested with different datasets. Overall, the automated system shows an increased reproducibility and stability of the acquired signals allowing for the confirmation of the proportionality relationship expected between the particle size and its friction coefficient. These results demonstrate that this approach may support the development of future systems with increased throughput and reliability, for biosciences applications.

Forest digital twin: A new tool for forest management practices based on Spatio-Temporal Data, 3D simulation Engine, and intelligent interactive environment

Existing forest digitization studies focus on one-way forest management practice visualization simulation, lacking decision-making feedback and virtual-real interaction synchronization. This paper presents the vision of the forest digital twin paradigm. We construct a forest digital twin to explore a new digital carrier of forest resources using remote sensing data, forest inventory data, the Cesium Digital Earth Engine, forest planning theory and parametric 3D modeling technology. The two-way interaction and thinning experiments showed that the forest digital twin could provide a novel pattern for in-depth analysis of forest spatial structure, individual tree dynamic growth and human-digital twin interaction effects. The successful recognition rate in matching the forest structure seen on real forest structure images with the forest digital scene was 91.3%, indicating that the forest digital twin can characterize the real forest structure significantly. The prediction accuracy of the multi-grade growth model integrating the Bayesian method for DBH, H was more than 90.4%. In addition, ASS-FDT interaction is superior to the assessors (ASS) and forest digital twin (FDT) for stand spatial structure overall optimization. The multi-dimensional stand spatial structure index (F-index) increased by 22.82%. The constructed forest digital twin model shows superior performance in optimizing the stand growth model and enhancing the overall stand spatial structure under the decision-making feedback and real-time interaction strategies. The automatic operation pattern provides a user-friendly forest management practice solution.

Optimal electrode configuration and system design of compactly-assembled industrial alkaline water electrolyzer

The production of green hydrogen by water electrolysis is an effective way to realize the utilization of renewable energy and decarbonization, efficient water electrolysis equipment is of great significance to reduce energy consumption and accelerate the deployment of hydrogen production infrastructure. This paper reports the optimal electrode configuration adapted to actual industrial conditions. The aim of this paper is to study the electrode structure effect and system design of compactly-assembled industrial electrolyzer under the actual operating conditions, so as to find out more suitable electrolyzer configuration parameters. Firstly, the principle and structural parameter design of industrial electrolyzer are carried out, and compactly-assembled industrial alkaline water electrolyzer (22 cells) was manufactured. Secondly, a balance of plant (BOP) system was built and the fully automatic operation process was designed. Finally, a system-level alkaline water electrolysis (AWE) hydrogen production system (including compactly-assembled electrolyzer and BOP) was established. The effect of different expanded meshes and woven meshes on the energy consumption of AWE equipment was tested, and the system component cost was analyzed. Industrial system-level test results show that the electrode dominant parameters affecting AWE performance are different under varying current densities. From the perspective of industrial application, it is more suitable for the strand width and pore width of the woven mesh to be about 0.2 mm, it achieved 4.3kWh/m3H2@4000 A/m2 based on the optimized configuration parameters. This study is helpful to the selection of electrode parameters and optimal configuration for industrial AWE equipment, at the same time, it provides a reference for the design of electrolyzer, BOP and system control process.

A data-driven approach for railway in-train forces monitoring

Railway in-train forces are an essential element in assessing multiple aspects of rolling stocks. Conventional methods for obtaining the forces can be time-consuming and require significant investment in manpower and domain expertise, while only gathering the force data for specific service conditions one at a time. However, automatic train operation (ATO) systems can measure real-time information for trains and tracks by on-board and trackside devices, which could provide an opportunity for in-train forces monitoring. This paper presents a data-driven approach that uses ATO-measured data and a neural network model to monitor in-train forces under service conditions. To develop this approach, longitudinal train dynamics simulations (LTSs) for a freight train were conducted to establish the relationship between ATO measurements and in-train forces on specific couplers, which was embedded in a large amount of training data. After that, a specially developed self-attention-based causal convolutional neural network (SA-CNN) was employed to learn the underlying relationship and estimate the in-train force histories considering temporal dependencies. The comparative evaluation between the SA-CNN against four alternative neural network models revealed that the SA-CNN exhibits a slightly higher level of accuracy. Furthermore, the generalisation capability of the well-trained SA-CNN model was confirmed by numerical LTSs under four different service conditions. The results indicated that the data-driven approach has superior compatibility for arbitrarily combined inputs with significantly reduced computational time compared to LTSs. This approach holds the potential for achieving reliable in-situ monitoring of railway in-train forces, which is beneficial to both in-train force-related research and industrial applications.

A Deep Neural Network-Based Optimal Scheduling Decision-Making Method for Microgrids

With the rapid growth in the proportion of renewable energy access and the structural complexity of distributed energy systems, traditional microgrid (MG) scheduling methods that rely on mathematical optimization models and expert experience are facing significant challenges. Therefore, it is essential to present a novel scheduling technique with high intelligence and fast decision-making capacity to realize MGs’ automatic operation and regulation. This paper proposes an optimal scheduling decision-making method for MGs based on deep neural networks (DNN). Firstly, a typical mathematical scheduling model used for MG operation is introduced, and the limitations of current methods are analyzed. Then, a two-stage optimal scheduling framework comprising day-ahead and intra-day stages is presented. The day-ahead part is solved by mixed integer linear programming (MILP), and the intra-day part uses a convolutional neural network (CNN)—bidirectional long short-term memory (Bi LSTM) for high-speed rolling decision making, with the outputs adjusted by a power correction balance algorithm. Finally, the validity of the model and algorithm of this paper are verified by arithmetic case analysis.

Optimization of the Polarized Imaging Nephelometer (PI-Neph) for continuous monitoring of multiwavelength aerosol phase functions in support of space polarimetry missions

This work deals with the set up and optimization of a commercial multiwavelength polarized imaging nephelometer (PI-Neph, Airphoton PIN100) designed to measure F11 (phase function) and –F12/F11 (polarized phase function) from ambient aerosol samples. Such measurements are critical for the success of upcoming satellite missions based on polarimetry measurements because they will support and validate retrieval techniques. The main novelty of the instrument with respect to previous versions consists of using only one beam instead of a mirror system to fold it, avoiding internal reflections of light and loss of energy within the laser beam. The instrument also includes the automatization of the imaging technique that makes possible continuous measurements in near real time of the aerosol phase functions at different states of linear polarization directly from ambient air. Laser emits at three different wavelengths (405, 515 and 660 nm) and the beam goes through a wire grid polarizer for linearly polarizing the light, followed by two liquid crystal retarders that control the state of linear polarization. The detector is a CMOS camera perpendicular to the beam which allows to obtain final phase functions in the range 5°–175° (losses of some angles by physical limitations and stray light). The calibration of the instrument is discussed and includes geometric calibration (to account for different light paths to the camera) plus absolute calibration (to obtain physical units). Temporal stability of calibrations is observed, and different data quality tests are presented in order to obtain reliable values of F11 and –F12/F11. For an automatic operation of the instrument that permits real time phase matrix measurements, a data quality check algorithm that avoids non-physical measurements and filters outliers has been developed. Validation of the scattering matrix elements based on monodisperse aerosol measurements is also done with polystyrene latex spheres (PSL), showing good agreement with Mie theory. Finally, first results of the instrument operating at the University of Granada (UGR) station from the Andalusian Global ObseRvatory of the Atmosphere (AGORA) since April 2022 are presented. Very good agreements of the integrated scattering coefficients with an integrating nephelometer (TSI, model 3563) were obtained with high correlation coefficients for the three wavelengths (R2 > 0.81). The calculated root-mean-square error (RMSE) reveals that the performance of the PI-Neph in the measurement of the scattering coefficient is better in the 660 nm and 515 nm wavelengths than in the 405 nm one, although differences between datasets are still low. The observed differences in phase functions and especially polarized phase functions (both in shape and spectral differences) under changing atmospheric scenarios (Saharan dust, pollution and background aerosol conditions) indicated that these variables are sensitive and reliable to classify the type of ambient aerosol. Particularly, the combined measurements of F11 and –F12/F11 shows a unique potential of the PI-Neph for aerosol typology.

An Automatic Train Operation Based Real-Time Rescheduling Model for High-Speed Railway

With the continuous development of the Automatic Train Operation (ATO) system in high-speed railways, automatic driving is progressively supplanting manual operations, ushering in a new era of predictability and reliability for high-speed railway transport. Concurrently, the advent of the ATO system provides a notable impact on real-time rescheduling during disruptions, as it equips dispatchers with precise insights into train operation statuses. This paper is dedicated to a thorough analysis of how the transition to automatic driving in train operations influences the real-time rescheduling model. Based on the distinctive impact of the ATO system on real-time rescheduling, we have proposed a mixed-integer linear programming model that combines train re-timing, reordering, and the minimization of passenger delays. To validate the effectiveness of our model, we present several experiments conducted using data from the Beijing–Shanghai high-speed railway line. The results unequivocally demonstrate that our ATO-based model significantly mitigates train delay time, demonstrating its practical value in optimizing high-speed railway operations.

Automated Control Systems in Greenhouses

Objective. The purpose of the research is to develop an innovative automated program associated with a completely new irrigation regime based on a closed water supply system.Method. Methods of modeling, formalized description, optimal design and control of technological processes and production were used.Result. An automated control system in greenhouse farming based on Arduino controllers and ONI PLR Studio software for its operation are presented. The scientific novelty of the work lies in the creation of appropriate modes of the climate control system in greenhouses. The ONI PLR Studio program is written in FBD language. It solves important problems of controlling the cultivation of agricultural products, breeding new plant varieties, and controlling the technological process using certain signals. User input is limited to software adjustments and direct setting of controller parameters. The adjustment can be carried out remotely through a computer connected to the controller. The automatic operating mode is provided by sensors and a controller with electronic control circuits, which ensure that the actuators operate in the required mode.Conclusion. An automated control system (ACS) allows you to control air and soil temperature, soil and air humidity, soil nutrition, disinfection, air pressure, lighting, the presence of carbon dioxide in the greenhouse, and climate control.

GSOANR-based multi-objective train trajectory optimization

ABSTRACT Railway transport is an important mode of transportation in China, UK, and worldwide. It is expected to play an even more important role in transportation decarbonization in shifting more passenger trips and freight transportations to railway. It is strategically important to reduce the energy consumption and improve the overall energy efficiency of the railway systems. The introduction of automatic train operation (ATO) systems in electrified railway systems makes it possible to achieve energy consumption reduction while satisfying other railway operation criteria such as the need for passenger comfort, train punctuality, and safety. Although energy saving of railway systems has attracted substantial interest in recent years, ATO systems are not environmentally friendly as expected and there is a lack of a set of energy saving performance measures for railway system planning and operation. In this paper, the features of ATO systems are incorporated into the problem formulation, and a modified GSO algorithm with adaptive neighbourhood range (namely the GSOANR algorithm) is proposed as the solver of the problem. The proposed method is applied to the trajectory planning of a 58-km-long route between Heishan North Station and Fuxin Station of the Beijing-Shenzhen Line in China, and the results confirm that the train operation trajectory generated by the GSOANR algorithm-based optimization method can reduce the electrical energy consumption by about 5.6% compared with the results generated by the standard GSO algorithms, confirming the efficacy of the proposed energy-saving measures and the effectiveness of the novel optimization method, and the referential significance for the study of the train trajectory optimization.

Development of an automatic personal comfort system (APCS) based on real-time thermal sensation prediction

Most existing building thermal environment design methods focus on the overall environment without considering individual demands, which can result in inadequate comfort and unnecessary energy consumption. To address individual differences, appropriate measures must be followed at the individual level. This study presents the development of an automatic personal comfort system (APCS) for regulating the local environment of individuals by integrating two personal thermal comfort technologies, personal comfort system (PCS) and personal comfort model (PCM). The equipment enables thermal sensation prediction through infrared skin temperature and environmental parameters measurements, which is used to regulate the corresponding PCS device. The proposed thermal sensation prediction model based on random forest can achieve a prediction accuracy of 69%, while the PCS control strategy comprehensively considers thermal sensation vote (TSV) and airflow acceptability. The APCS performance test experiment involved 25 male adult subjects. The results indicated that PCS operating states were consistent under both manual and automatic control conditions. Moreover, TSV remained within the neutral zone for most participants throughout the experiment. These findings demonstrate that the APCS realized good automatic operation to ensure the comfort of the personnel. The APCS framework holds reference value for research on personal thermal comfort and future thermal environment construction, extending beyond the equipment itself.

Automatic sprayer operation control

U radu je prikazana suvremena poljoprivredna tehnika u zaštiti bilja kroz pojašnjen pristup rada automatske kontrole rada prskalice. Prikazani su moderni sustavi za nivelaciju armature upotrebom senzora i ukazana važnost korištenja GIS navođenja kao glavne smjernice precizne poljoprivrede. U radu su dani konkretni primjeri prskalica na kojima su postavljeni automatski sustavi poput Amatron 3, Arag sustav, BoomTrac Pro gdje su ukazane sve prednosti navedenih. Korištenjem suvremene tehnike i dodatne digitalne opreme omogućena je kvalitetna aplikacija zaštitnih sredstava uz višestruke uštede.

Industry 4.0 as a strategy to contribute to the water supply universalization in developing countries

Access to safe water is still a challenge. Millions of people worldwide, mainly in rural and remote regions, still do not have access to safe drinking water. Point-of-use and decentralized water treatment systems have the potential to improve their livelihood, especially if these technologies are combined with the advantages brought by the Fourth Industrial Revolution. This paper presents and discusses opportunities for convergence between water treatment mainly using membrane separation processes and technologies related to Industry 4.0, focusing on a scenario of decentralized systems installed in developing countries. Emphasis is given to membrane-based applications due to their advantages of ease of operation, small area requirements, and reduced implementation time while maintaining a great capacity to produce high water quality regardless of raw water characteristics. Literature suggests that Industry 4.0 can be considered an ally to better support water management in remote and rural regions. Treatment units can now be integrated into digital cyber-physical systems to promote a greater comprehension of local necessities and provide transparent and effective communication, faster actions, or even remote and automatic operation, contributing to the capacitation of local operators and the performance and conservation of the infrastructure. It is also possible to monitor many parameters of water quality, ensuring process integrity and safe water. These advancements may result in affordable, adaptive, and resilient solutions, but it is necessary to consider the perspective of the Human Right to Water and Sanitation – unfortunately, not always present – and the difficulties inherent to developing countries.

Design Of An LPG Leak Detection System Using Iot Based MQ-2 Sensor

The IoT-based LPG gas leak detection system with MQ-2 and automatic regulator aperture is designed to protect the environment from the dangers of gas leaks and optimize gas use. The MQ-2 sensor is used to detect LPG gas accurately and sensitively. This system is connected to IoT which allows remote monitoring via smart devices. When the sensor detects that the LPG gas concentration exceeds a safe threshold, the system will send an alert with notification and automatically activate the regulator to cut off the gas supply. This helps prevent the accumulation of harmful gases. This design combines reliable gas detection with automatic operation to improve environmental safety and gas efficiency. LPG gas is very commonly used by the community because it has many advantages, but there are also many risks associated with using LPG gas, such as poisoning, shortness of breath and even fire. It is therefore important to have a leak detection system to prevent accidents that may occur, by integrating the programmable MQ-2 sensor and NoteMCU ESP8266.

Android-based Automatic Steak Grilling Tool

In an era of rapid technological development, technology is increasingly accessible and easily applied by humans. One of the significant developments is the Internet of Things (IoT), where physical devices such as sensors, equipment, and vehicles are equipped to communicate and interact via the Internet network. The application of IoT has expanded to various sectors, including culinary. In this regard, preparing and presenting food, especially steaks, becomes an exciting focus. There are multiple types of steaks, such as sirloin and tenderloin, and cooking involves various techniques, such as searing and grilling. However, suitability for maturity and risk during cooking is challenging for steak makers and connoisseurs. To overcome this, the application of IoT is needed in an automatic steak roaster to be a promising solution. This research is also equipped with real-time monitoring via an Android application. This aims to ensure proper doneness and consistent results in the steak cooking process. This research makes an automatic steak grill with a success rate of 83%, which shows that the tool's performance and functionality align with expectations. This tool also has an Android application to monitor and control the device remotely efficiently. This research gives confidence that this can be a solution that has been developed and provides significant benefits in roasting steaks with automatic monitoring and operation.

DECISION-MAKING METHOD FOR TEMPERATURE CONTROL IN THE SMART HOME

The current challenge is to provide automatic decision support in a smart home. A study of the top solutions of well-known smart homes has shown that existing solutions usually do not provide for fully automatic control in a smart home, but are focused either on automatic control in conjunction with manual control or user-controlled control. Therefore, the goal of this study is to support decision-making for fully automatic temperature control in a smart home. Human well-being and performance depend on the meteorological conditions of the environment in which a person is located. The most important condition for high performance, rest, and health is the creation and maintenance of an optimal home microclimate. One of the main parameters of the indoor microclimate is temperature. The room temperature control subsystem ensures the optimal temperature level and allows for individual adjustment for each family member. The developed rules for determining the optimal room temperature allow you to evaluate the existing temperature parameters for further automatic operation of the smart home temperature control subsystem in residential premises of various types. The purpose of the temperature control subsystem is to provide comfortable conditions in residential premises of various types in terms of their temperature regime. The developed decision-making method for temperature control in a smart home, which is the basis of the smart home temperature control subsystem, provides a comfortable and optimal (taking into account building and sanitary and hygienic standards) temperature in the corresponding living space. The results of the functioning of the smart home temperature control decision-making method have shown that the developed method provides for the recognition of situations (optimal temperature, low temperature, high temperature) and support for decision-making on the temperature regime in a certain type of residential space (turning on heating devices, turning on cooling devices, no action, etc.).

A Distributed Multiparticle Precise Stopping Control Model Based on the Distributed Model Predictive Control Algorithm for High-Speed Trains

Abstract The fixed-point stopping of high-speed trains in stations is generally accomplished through manual operation in China. This situation often leads to a failure to stop at fixed-point signs and causes a fluctuation in the longitudinal acceleration due to the lack of experience of the drivers. To achieve precise, stable, and automatic stopping, a distributed multiparticle precise stopping control model based on the distributed model predictive control (MPC) algorithm is developed in this paper. A two-level hierarchical control structure for the subcontroller of each vehicle is adopted to bring itself to a controlled stop. In the upper control of subcontroller, the MPC algorithm is designed in turn based on the multiparticle mechanism model of the train. In the lower control of subcontroller, the target input from the upper control is converted and distributed. The controlled object, a comprehensive numerical computing model including the spatial dynamic model of the train and its electropneumatic blending braking model, is established and controlled by the corresponding subcontroller and employed to verify the performance of the controller. The influence of the model control parameters on the stopping performance is discussed, and the optimal combination of control parameters is selected. The proposed control model using the optimization parameters is tested and verified through the comprehensive numerical computing model. The results indicate that the stopping error is 0.0075 m, which is much less than the accuracy requirements for fixed-point stopping. The computing time of each subcontroller in real-time is stable at 0.09 s. The coupler impact force between two adjacent vehicles can also be effectively inhibited and eliminated. Its control performance outperforms a proportional–integral–derivative (PID) algorithm. The proposed precise stopping model and comprehensive numerical computing model provide references for the application and algorithm optimization of automatic train operation technology in high-speed trains.

A systematic knowledge graph-based smart management method for operations: A case study of standardized management

Standardized routine operation management (SROM) has been widely accepted and applied by kinds of enterprises and played a key supporting role. With full use of the emerging knowledge-based smart management technology, SROM will further increase comprehensive efficiency and save human resources greatly at the same time, especially for small and medium enterprises (SMEs). Hence, we propose a systematic knowledge-based smart management method to transfer SROM activities from human operations to automatic response by means of knowledge explicitation, organization, sharing and reusing, which can be further achieved by employing knowledge graph. We took a typical SROM instance, ISO 9000 implementation management, as an example to validate the transformation from human activities to knowledge graph-based automatic operation. We firstly analyzed characteristics of domain knowledge and constructed an ontology model according to the knowledge stability. Secondly, a hybrid knowledge graph construction and dynamic updating framework together with related algorithms were designed by deliberately integrating semantic similarity calculation and natural language processing. Thirdly, we developed a question-answering mechanism and reasoning system based on the ISO 9000 implementation knowledge graph to support automatic decision and feedback for ISO 9000 routine operation management including knowledge learning and processes auditing. Finally, the practicability and effectiveness of SROM knowledge graph has been validated in a SME in China, realizing the application of question-answering, job responsibility recommendation, conflict detection, semantic detection, multidimensional statistical analysis. The proposed method can also be generalized to support auxiliary optimization decision, vertical risk control, operation mode analysis, optimization model improvement experience and so on.