High Degree Of Automation Research Articles

Recent Patents on Piano Tuning System

Piano tuning plays a very important role in piano playing and music education. Regular tuning can prolong the service life of the piano, improve the sound saturation, and make the sound more concentrated and full. Automated mechanical equipment, controllers, sensors, and other equipment are used to automatically tune pianos to solve the problems of human error, overtone resonance interference, string breakage, etc., occurring in manual tuning. To solve the above problems, based on the synthesis of related patents and research results, the patents on automatic tuning system and methods for modulating piano are introduced in detail. An automatic tuning system can replace manual tuning, simplify the complex process of tuning, and consider the problem of overtone interference caused by the vibration of the strings to improve the accuracy of tuning; it can, at the same time, perform real-time monitoring of the string tension, protecting the strings from breaking due to tuning. The automatic tuning system has a simple structure and a high degree of automation; its method is highly accurate and it can replace professional tuning technicians, being highly innovative and widely practical.

Development and validation of automated methods for COVID-19 PCR Master Mix preparation

Polymerase chain reaction (PCR)-based assays were widely deployed during the SARS-CoV-2 pandemic for population-scale testing. High-throughput molecular diagnostic laboratories required a high degree of process automation to cope with huge testing demands, fast turnaround times, and quality requirements. However, process developers and optimizers often neglected the critical step of preparing a PCR Master Mix. The construction of PCR Master Mix depends on operator skill during the manual pipetting of reagents. Manual procedures introduce variation, inconsistency, wastage, and potentially risks data integrity. To address this, we developed a liquid-handler-based solution for automated, traceable, and compliant PCR Master Mix preparation. Here, we show that a fully automated PCR Master Mix protocol can replace manual pipetting, even in a diagnostic environment, without affecting accuracy or precision. Ultimately, this method eliminated operator-induced wastage and improved the consistency of the quality of results.

The HZB F2X‐Facility—An Efficient Crystallographic Fragment Screening Platform

ABSTRACTCrystallographic fragment screening (CFS) has recently matured into an important method for the early stages of drug discovery projects. It is based on high‐throughput structure determination and thus requires a high degree of automation as well as specialized workflows and robust analysis tools. Consequently, large‐scale research facilities such as synchrotrons have embraced the method, and developed platforms to perform CFS campaigns with the help of crystallography experts and specific tools. The BESSY II synchrotron, operated by the Helmholtz–Zentrum Berlin (HZB), is one of these synchrotron facilities that offer a CFS platform, named the F2X‐facility. Here, the specialized F2X workflow is described along with the relevant differences to other existing CFS platforms, and the ongoing developments aimed at supporting users of the facility. The different stages of a CFS campaign including requirements, beamline capabilities, and the software environment are detailed and explained. A unique F2X‐GO kit is featured, which allows users the possibility of performing all sample preparation in their home laboratories. Furthermore, at the HZB a computational workflow has been built to support users beyond the hit identification stage. The advantages of the F2X‐facility at HZB are described and references are provided to successfully conduct CFS.

NUMERICAL MODELING OF DETERMINING THE ROCKWELL HARDNESS NUMBER OF A STEEL SAMPLE

In steel Hardness is a mechanical property that indicates the resistance of a material to local plastic deformation caused by mechanical indentation. In engineering, hardness is mainly used to determine the properties of various materials. In mechanical solids, the study of the hardness of materials is usually limited to the study of metals and their mechanical behavior. To understand the mechanism of hardness in metal structures, a basic understanding of materials science is necessary. Accurate hardness measurement is of great practical importance in the technical diagnosis of potentially hazardous industrial equipment (pipelines, pressure vessels, metal structures). This is primarily due to the fact that this characteristic has a strong correlation with strength and yield strength, which largely determine the possibility of further operation of the equipment. At the same time, one of the main advantages of hardness measurement is the speed of its determination and the non-destructive nature of its control. A finite element model was developed for the indentation process, and the contact radius and contact pressure were obtained. The Rockwell hardness measurement process was modelled in accordance with the test methodology of DSTU ISO 6508-1:2013. The indentation process was modelled using ANSYS software with different contact models and different loads. The relevance of solving the problem of modelling the process of determining the surface hardness number using the FEM software ANSYS is determined by the high degree of automation of the solution process. During the modelling, it is assumed that the material of the sample into which the indenter of any shape is pressed is homogeneous isotropic and elastoplastic according to the kinematic plasticity model. This model corresponds to the behavior of steels. In addition, the indenter material is isotropic and linearly elastic. In the developed numerical model, the indenter is taken in the form of a tungsten carbide ball, which corresponds to the scheme for determining the hardness number on the "B" scale. In conditions of limited access to laboratory equipment, numerical modelling of the process of determining the hardness number allows students to demonstrate all stages of the test at a qualitatively new level, and further, to compare with the results of full-scale tests. Keywords: structural materials, Rockwell method, finite element model, indenter, hardness.

Three-Dimensional Printing with Earthen Materials: A Settlement-Scale Design Experience

This case study originates as a design experiment for a sustainable housing system built on-site. The context is Niamey, the capital of Niger. The study takes into account the environmental issues in the construction sector and aims to find a solution capable of meeting housing, environmental, and economic needs. In the field of earthen construction, the most important developments have been achieved in manufacturing methods. In particular, the use of an additive digital manufacturing system, such as large-scale 3D printing, allows the construction of complex shapes derived from structural and thermal studies, maintaining a high degree of automation in the construction process, reducing construction times and labor costs. This paper investigates the possibility of responding to housing and environmental needs with a settlement system made almost entirely of printed earth, maintaining the highest possible degree of automation. Starting from a study on the state of the art of 3D printing in architecture and printable earthen compounds, the design choices of similar cases are analyzed to understand the construction techniques, potentials, and limitations of the medium. Finally, a design proposal is developed based on the definition of a fully printable functional module, which, upon aggregation, determines the characteristics of the final settlement. This implies a radical change of approach compared to previous prototyping of 3D-printed earthen buildings, as the design of the single functional module is not an exercise that finds completion in itself, but is oriented to the scale of the settlement right from the definition of its basic geometric characteristics. In other words, the settlement is no longer the result of the serial aggregation of independent basic units, but arises spontaneously from the juxtaposition of functional modules designed to interact with each other and merge into a single residential complex. The settlement is, therefore, the large-scale replication of the alternation between full and empty spaces, that characterizes the single functional module and, even more importantly, the replication can take multiple forms. In fact, the full and empty spaces of the functional module are planned to allow multiple combinations of aggregation. This introduces a certain degree of customization into the growth dynamics of the settlement, a factor that is entirely new compared to previous proposals by repeatable modules. No less important are the environmental implications, as designing for the scale of the settlement allows the low carbon footprint typical of earth-based construction to be extended from the single building to the entire settlement.

Continuous Slag Discharge Construction Technology for Shield Tunneling in Narrow Space Environment

The shield tunneling method is the main construction method in subway tunnel construction, which has the characteristics of safety, efficiency, mechanization, and high degree of automation. However, the shield tunneling equipment is huge, the supporting facilities are numerous, and the site requirements are high. In response to the problems of slow slag removal speed and low construction efficiency in shield tunneling under the construction conditions of small wellhead shield tunneling sections in urban subways, taking the shield tunneling section of Guangzhou Metro Line 13 Phase II as the background,This article introduces the main components, working principles, installation and transportation processes, fault handling, and benefit analysis of the continuous belt conveyor slag removal system,This article elaborates on the method of using a continuous belt conveyor in the tunnel combined with a vertical lifting system at the wellhead to achieve continuous slag removal during underground shield tunneling construction in a narrow wellhead construction site. This not only improves excavation efficiency but also reduces construction costs, solves construction problems, and provides reference for similar projects in the future.

Application Research of an Automatic Control Seawater Reverse Osmosis (SWRO) System Based on the Siemens PLC

To solve the global environmental problem of a shortage of freshwater resources, seawater desalination is considered one of the most promising solutions. In this research, the main novelty of the seawater desalination system lies in its utilization of a reverse osmosis unit as the core process for producing drinking water. By optimizing the pretreatment section in the process flow, a seawater reverse osmosis (SWRO) control system based on Siemens PLC with a high degree of automation was developed, which has the advantages of convenient maintenance and monitoring. In addition, through research on reverse osmosis systems, the results showed that within two years of operation, the total desalination rates of the primary and secondary reverse osmosis systems were not less than 99% and 97.5%, respectively. Furthermore, the water quality after desalination was tested. When the doses of CaCl2, MgCl2 and NaHCO3 were 20 mg/L, 15 mg/L, and 50 mg/L, respectively, high-quality drinking water was obtained. Finally, a reasonable process plan and corresponding estimates were given for the complex water source conditions. Compared with traditional seawater desalination systems, our system has the advantages of easy operation, efficient water production and lower price. Accordingly, this study will help to solve drinking-water problems in some freshwater-scarce regions.

Construction of integrated network order system of main distribution network based on power grid operation control platform

This study presents a major advance in grid management: the development and deployment of an integrated network command system for the main distribution network. The system integrates cutting-edge information technology, including modules such as command issuance, intelligent routing, security assurance and in-depth data analysis, opening a new era of refined and intelligent power grid management. The research focuses on the application of core technologies such as information communication technology, distributed control system, artificial intelligence and big data analysis, and strengthens the system operation foundation. The chapter on system architecture details the innovative integration of DDQN algorithm and attention mechanism, and carefully constructs intelligent scheduling engine and status monitoring and early warning system, which significantly improves real-time response, decision optimization and active security defense capabilities. Simulation experiments and actual case analysis verify the effectiveness of the system, specifically, the response time is reduced by 75.7%(from 2.1 s to 0.51 s in the traditional system), the data processing speed is still maintained at a high level under high load (100,000 data processing rate is 300/s), and the system stability is as high as 99.97%. The new system also achieved a high degree of automation, reducing annual operation and maintenance costs by 20%, and increasing user satisfaction to 90%, an increase of 28.6% over the previous period. These improvements not only optimize power quality and grid efficiency, but also further confirm that the fault response time is reduced by 30% and the user outage time is reduced by 25%. Therefore, this study not only highlights the innovation of the proposed system, but also demonstrates its significant contribution to accelerating the modernization of power grid management and ensuring safe operation with empirical data.

Computer aided diagnostic system with reduced electrode set for depression detection using spatio-temporal attention mechanism

Computer-aided diagnostic systems (CAD) have been extensively used in medical applications to identify neurological abnormalities. It enhances diagnostic accuracy, decreases misdiagnosis rates, and provides reliable decision support. The proposed manuscript has introduced an automated CAD system that uses EEG signals to detect depression. The proposed automated CAD system is designed to work with a reduced set of electrodes. This manuscript introduces a method for electrode ranking based on the Fisher score. Electrodes are selected based on the threshold criteria. EEG is spatio-temporal data. The spatial and temporal self-attention mechanism is used to extract spatial and temporal features in the spatial and temporal domain. The proposed automated CAD system reduces the requirements of electrodes by an order of magnitude without compromising the performance of the model. The proposed CAD system has a higher degree of automation. It reduces the cost of the scan, experimental time, and complexity of the system.

Robust marker detection and identification using deep learning in underwater images for close range photogrammetry

The progressing industrialization of oceans mandates reliable, accurate and automatable subsea survey methods. Close-range photogrammetry is a promising discipline, which is frequently applied by archaeologists, fish-farmers, and the offshore energy industry. This paper presents a robust approach for the reliable detection and identification of photogrammetric markers in subsea images. The proposed method is robust to severe image degradation, which is frequently observed in underwater images due to turbidity, light absorption, and optical aberrations. This is the first step towards a highly automated work-flow for single-camera underwater photogrammetry. The newly developed approach comprises several machine learning models, which are trained by 10,122 real-world subsea images, showing a total of 338,301 photogrammetric markers. The performance is evaluated using an object detection metrics, and through a comparison with the commercially available software Metashape by Agisoft. Metashape delivers satisfactory results when the image quality is good. In images with strong noise, haze or little light, only the novel approach retrieves sufficient information for a high degree of automation of the subsequent bundle adjustment. While the need for offshore personnel and the time-to-results decreases, the robustness of the survey increases.

Development of Hardware-in-the-Loop Simulation Test Bed to Verify and Validate Power Management System for LNG Carriers

Liquefied natural gas carrier (LNGC) orders are increasing owing to marine environment regulations. The complexity of the integrated system applied to shipbuilding and software errors have increased with the high degree of automation. Direct on-site inspection methods are associated with high costs and safety risks, whereas software-based simulations rely heavily on the accuracy of the models of power system components. Hardware-in-the-loop simulation (HILS) can be utilized for designing and testing intricate real-time embedded systems. Specifically, HILS offers a reliable means of evaluating power management system (PMS) performance for LNGCs, which are high-value vessels commonly used in offshore plants. This study proposes a PMS–HIL test bed comprising a power supply unit, consumer, simulation control console, and main switchboard. The proposed HILS test bed utilizes the real equipment data of the shipbuilding industry to replicate the conditions associated with actual LNGCs. The proposed system is verified and validated through a software acceptance test procedure. Additionally, load-sharing, load-dependent start, blackout prevention, and preferential tests are performed for the PMS function evaluation. Test results indicate that the proposed system has great potential for conventional PMS commissioning. Therefore, it exhibits the potential to replace traditional factory acceptance tests. Additional development of the system will be conducted for ship automation, utilizing PMS control and an energy management system.

Wheat Fusarium Head Blight Automatic Non-Destructive Detection Based on Multi-Scale Imaging: A Technical Perspective.

Fusarium head blight (FHB) is a major threat to global wheat production. Recent reviews of wheat FHB focused on pathology or comprehensive prevention and lacked a summary of advanced detection techniques. Unlike traditional detection and management methods, wheat FHB detection based on various imaging technologies has the obvious advantages of a high degree of automation and efficiency. With the rapid development of computer vision and deep learning technology, the number of related research has grown explosively in recent years. This review begins with an overview of wheat FHB epidemic mechanisms and changes in the characteristics of infected wheat. On this basis, the imaging scales are divided into microscopic, medium, submacroscopic, and macroscopic scales. Then, we outline the recent relevant articles, algorithms, and methodologies about wheat FHB from disease detection to qualitative analysis and summarize the potential difficulties in the practicalization of the corresponding technology. This paper could provide researchers with more targeted technical support and breakthrough directions. Additionally, this paper provides an overview of the ideal application mode of the FHB detection technologies based on multi-scale imaging and then examines the development trend of the all-scale detection system, which paved the way for the fusion of non-destructive detection technologies of wheat FHB based on multi-scale imaging.

Application of CNC Robots in Deep Learning Intelligent Construction of Green Buildings

In order to achieve automated cleaning of photovoltaic devices in green buildings and reduce the labor intensity of workers, the author has developed a photovoltaic device intelligent cleaning CNC robot intelligent cleaning system. Through the analysis of the use of the green building photovoltaic device, the green building photovoltaic device cleaning robot and its cleaning system are designed according to the environmental situation and the use requirements. This intelligent cleaner system consists of a transmission guidance system, a cleaning system, and a control system, using only one motor to complete the overall action, at the same time, an independent walking mechanism is designed, and the walking wheel system is not in contact with the photovoltaic device, which does not harm the photovoltaic device, at the same time, it can cross two adjacent photovoltaic panels with included angles to achieve the function of cleaning complex photovoltaic panel surfaces for a single device, the cleaning layer design has a diagonal protrusion similar to a spiral output, which can automatically discharge accumulated dust from the cleaning roller while cleaning, providing a certain self-cleaning function, extending the replacement cycle of the cleaning layer, and can be used for both dry and wet purposes. Realize wet cleaning; when the cleaning solution is not used, use the cleaning layer and its surface oblique raised to remove dust, achieve dry cleaning. On the whole, the robot structure is reasonable, simple control process and high degree of automation, which can effectively solve the cleaning problem of green building photovoltaic devices, and can provide effective guarantee for the efficient work of photovoltaic devices.

Adaptive block imaging based on compressive sensing in AFM.

Atomic force microscopy (AFM) is a kind of high-precision instrument to measure the surface morphology of various conductive or nonconductive samples. However, obtaining a high-resolution image with standard AFM scanning requires more time. Using block compressive sensing (BCS) is an effective approach to achieve rapid AFM imaging. But, the routine BCS-AFM imaging is difficult to balance the image quality of each local area. It is easy to lead to excessive sampling in some flat areas, resulting in time-consuming. At the same time, there is a lack of sampling in some areas with significant details, resulting in poor imaging quality. Thus, an innovative adaptive BCS-AFM imaging method is proposed. The overlapped block is used to eliminate blocking artifacts. Characteristic parameters (GTV, Lu, and SD) are used to predict the local morphological characteristics of the samples. Back propagation neural network is employed to acquire the appropriate sampling rate of each sub-block. Sampling points are obtained by pre-scanning and adaptive supplementary scanning. Afterward, all sub-block images are reconstructed using the TVAL3 algorithm. Each sample is capable of achieving uniform, excellent image quality. Image visual effects and evaluation indicators (PSNR and SSIM) are employed for the purpose of evaluating and analyzing the imaging effects of samples. Compared with two nonadaptive and two other adaptive imaging schemes, our proposed scheme has the characteristics of a high degree of automation, uniformly high-quality imaging, and rapid imaging speed. HIGHLIGHTS: The proposed adaptive BCS method can address the issues of uneven image quality and slow imaging speed in AFM. The appropriate sampling rate of each sub-block of the sample can be obtained by BP neural network. The introduction of GTV, Lu, and SD can effectively reveal the morphological features of AFM images. Seven samples with different morphology are used to test the performance of the proposed adaptive algorithm. Practical experiments are carried out with two samples to verify the feasibility of the proposed adaptive algorithm.

Approaches to automating processes of working with ontological resources

Ontological models are extensively used in information support systems that offer resources and services for solving management, design, and scientific and technical problems. Specifically, domain ontologies are commonly utilized in decision support systems. In the ontological modeling of complex systems, there arises a need to automate the process-es of handling ontological resources. This work discusses the main software systems and methodologies for ontological modeling, approaches to automating the processes of creating, populating, and using ontological models, and considers the temporal aspect of the ontological representation of objects. The aim of the work is to explore methods for auto-mating the life cycle of ontological resources and to analyze the extent of their adaptation in applied ontologies. The work highlights a relatively high degree of automation in the process of populating ontologies and the use of large lan-guage models in this process. However, it points out the lack of methods for automating the conversion of information from tables and diagrams into ontological models, as well as for validating and processing the content of the model. Promising directions for automating work with ontological resources are also indicated.

Automated Measurement System of High Accuracy for Shunt Reactors

System for automated transformer tests (ATT) enables different measurements on power transformers, from simple to the most complex, with a high degree of automation. ATT system is now expanded with modules for measurements on shunt reactors. The measurements related to shunt reactors that are covered with the system are: measurement of losses, impedance measurement, linearity test, zero impedance measurement, winding resistance measurement, temperature rise test and measurement of mutual reactance. Besides the measurement, ATT enables data analysis and report generation, all in accordance with [1,2]. Measurement of shunt reactor losses with satisfactory accuracy is a big challenge due to power factors of the reactors as low as 0,001. A system for loss measurements with high accuracy, consisting of measuring bridge, current comparator and standard capacitor is integrated into ATT. In the paper, a comparison in terms of loss measurement accuracy on shunt reactor is given for two measurement systems – a newly integrated system for shunt reactors and standard system used for transformer measurements.

Automated Detection of MT Indications with CRACKVIEW AI

Magnetic particle testing (MT) has been used in non-destructive testing for over 80 years. Although there were attempts to automate it in the last century, it is still largely done manually today. Many forges and foundries already have a very high degree of automation in production, so that most of the personnel costs are spent on the final inspection. Especially in high-wage countries, these companies are under great cost pressure and the exhibit the problem of finding enough qualified personnel and keeping costs low at the same time. As a result of this growing pressure, there has been an increasing number of enquiries from test system manufacturers for fully automated MT systems in recent years. The advances in artificial intelligence (AI) in the last decade, show a promising way to realize the automated evaluation of indications in MT systems. The AI method of deep learning uses neural networks, that simulate the working principle of a human brain, is trained by a large amount of data. In case annotated images of crack indications. After the training the network is capable of detecting crack indications independent of the part geometry or crack position on the part itself. This presentation shows the CRACKVIEW AI system developed by KARL DEUTSCH for the automated evaluation in magnetic particle testing and presents results from its application.

Quantum computing for several AGV scheduling models

Due to the high degree of automation, automated guided vehicles (AGVs) have been widely used in many scenarios for transportation, and traditional computing power is stretched in large-scale AGV scheduling. In recent years, quantum computing has shown incomparable performance advantages in solving specific problems, especially Combinatorial optimization problem. In this paper, quantum computing technology is introduced into the study of the AGV scheduling problem. Additionally two types of quadratic unconstrained binary optimisation (QUBO) models suitable for different scheduling objectives are constructed, and the scheduling scheme is coded into the ground state of Hamiltonian operator, and the problem is solved by using optical coherent Ising machine (CIM). The experimental results show that compared with the traditional calculation method, the optical quantum computer can save 92% computation time on average. It has great application potential.

Automated feeder routing for underground electricity distribution networks based on aerial images

The process towards a carbon-neutral society requires a substantial amount of investment into electricity distribution networks for integrating more sustainable technologies such as photovoltaic systems, electric vehicles, heat pumps, and others. In order to execute the large number of network expansion measures associated with this effort, strategic and operational planning of distribution networks benefit from higher degrees of automation. A key component in these planning processes is the routing of feeder segments, i.e., the task of finding feasible and cost-efficient paths for underground power lines between two geographic locations, such as residential buildings and transformer stations. In this paper, we present FEEDAIR, a novel and innovative approach to automate feeder routing based exclusively on aerial images. FEEDAIR integrates deep neural networks, which encode geoinformation from aerial images, and A* search, which identifies effective feeder routes based on this encoding. We train and evaluate FEEDAIR using a large data set of more than 7000 real-world electricity distribution network feeder segments. Thereby, we compare FEEDAIR to state-of-the-art approaches from related work and find that the feeder segments generated by FEEDAIR are on par or superior with regard to several characteristics and metrics. FEEDAIR achieves this performance by relying exclusively on aerial images, whereas competing approaches require data on geocoded land cover and estimated construction cost, which is not as widely available and is frequently based on potentially subjective expert opinion.

Intelligent optimal layout of drainage pipe network monitoring points based on information entropy theory

The rapid expansion of urban drainage pipe networks, driven by economic development, poses significant challenges for efficient monitoring and management. The complexity and scale of these networks make it difficult to effectively monitor and manage the discharge of urban domestic sewage, rainwater, and industrial effluents, leading to illegal discharges, leakage, environmental pollution, and economic losses. Efficient management relies on a rational layout of drainage pipe network monitoring points. However, existing research on optimal monitoring point layout is limited, primarily relying on manual analysis and fuzzy clustering methods, which are prone to human bias and ineffective monitoring data. To address these limitations, this study proposes a coupled model approach for the automatic optimization of monitoring point placement in drainage pipe networks. The proposed model integrates the information entropy index, Bayesian reasoning, the Monte Carlo method, and the stormwater management model (SWMM) to optimize monitoring point placement objectively and measurably. The information entropy algorithm is utilized to quantify the uncertainty and complexity of the drainage pipe network, facilitating the identification of optimal monitoring point locations. Bayesian reasoning is employed to update probabilities based on observed data, while the Monte Carlo method generates probabilistic distributions for uncertain parameters. The SWMM is utilized to simulate stormwater runoff and pollutant transport within the drainage pipe network. Results indicate that (1) the relative mean error of the parameter inversion simulation results of the pollution source tracking model is linearly fitted with the information entropy. The calculation shows that there is a good positive linear correlation between them, which verifies the feasibility of the information entropy algorithm in the field of monitoring node optimization; (2) the information entropy algorithm can be well applied to the optimal layout of a single monitoring node and multiple monitoring nodes, and it can correspond well to the inversion results of the tracking model parameters; (3) the constructed monitoring point optimization model can well realize the optimal layout of monitoring points of a drainage pipe network. Finally, the pollution source tracking model is used to verify the effectiveness of the optimal layout of monitoring points, and the whole process has less human participation and a high degree of automation. The automated monitoring point optimization layout model proposed in this study has been successfully applied in practical cases, significantly improving the efficiency of urban drainage network monitoring and reducing the degree of manual participation, which has important practical significance for improving the level of urban water environment management.