High Degree Of Automation Research Articles

A Review of the Multitasking Allocation Model for Vertical Farming System

Vertical Farming System (VFS) can produce food efficiently in times of dramatic climate change, regional conflicts that affect food transportation, or space exploration. The high degree of automation in the system, a large number of sensors and robots work together, so an efficient task allocation model can significantly enhance operational efficiency, reduce energy consumption, and elevate the overall economic performance of VFS.With this background, the purpose of this review is to describe the current available multi-task allocation models applicable to VFS or related contexts. Through a literature analysis over the past 5 years, researchers have developed and analyzed numerous multi-task allocation models in the relevant fields, such as Multi-Objective Knapsack Problem (MOKP) models and Vehicle Routing Problem (VRP) models. Depending on the nature of the problem and the constraints of the environment, diverse types of multi-task allocation models have been proposed. Tasks generated within the VFS must be completed within specified time limits, and certain tasks require collaborative efforts among different robots. Taking into consideration these task requirements and drawing upon existing multi-task allocation models proposed by other researchers, this review propose the VFSRTP (VFS Robot Task Planning) model. It is expected that this work will have a positive impact on the research of VFS multi-task allocation model, and provide some model descriptions and guidelines.

Direct quantification of 1,1-dimethylhydrazine transformation products in sandy soil by thermal desorption gas chromatography – Tandem mass spectrometry

The use of rocket propellants based on unsymmetrical dimethylhydrazine (UDMH) in launch vehicles and boosters poses a threat to the environment due to the release of UDMH into soils and its subsequent transformation into a variety of nitrogen-containing compounds, including toxic and carcinogenic ones. The present proof-of-concept study proposes a novel approach to screening and simultaneous determination of a wide range of nitrogen-containing UDMH transformation products in soils, based on direct thermal desorption (TD) as a solvent-free sample extraction technique coupled with GC–MS/MS analysis.Optimal TD conditions consisting of the incubation of 5 mg sample at 300 °C for 30 min, allowed attaining high recoveries (80–100 %) for the most tested compounds and developing the method for quantification of fifteen acyclic and heterocyclic UDMH transformation products in sandy soil with LODs of 0.3–7.8 µg kg−1. The matrix matched calibration and introduction of the internal standard (pyridine‑d5) ensured high accuracy (75–100 %) and precision (RSD < 15 %) of the analysis. Along with high sensitivity and accuracy, the main advantages of the developed method are exceptional simplicity of sample preparation, low amount of toxic sample, high automation degree, and environmental friendliness.The developed method was successfully tested in the analysis of seven real soil samples taken from the Baikonur cosmodrome operational zone (Kazakhstan). The use of TD-GC–MS analysis in full spectrum scanning mode allowed for tentative identification of 62 UDMH transformation products including several compounds not yet described in the literature.

Evaluation of the Premier Hb9210 instrument for HbA1c determination

Background: Glycated hemoglobin measurements are a valuable tool for long-term blood glucose monitoring and the diagnosis of diabetes. Its widespread use has been made possible due to the development of new analytical methods with improved performances and standardization with reference materials. The aim of the present study was to evaluate the Trinity Biotech Premier Hb9210 analyzer for the measurement of HbA1c. Methods: The precision was assessed using the CLSI EP-15A3 and EP-10A3 protocols. The latter was also used to investigate linearity, carryover, and linear drift. The comparison study was performed between Premier Hb910 and Tosoh HLC-723 G8 through Passing–Bablok regression and the Bland-Altman plot. The Fleiss Kappa index was used to assess the degree of agreement. The interference of Hb variants was investigated using samples with Hb variants S, C, D, E, J, and Seville. Results: Within-run and between-run imprecision fell between 0.37% and 1.16%. No statistically significant nonlinearity, carry-over, and/or drift were observed. The resulting regression line of the Passing–Bablok analysis was y = 0.00 + 1.00x. The Pearson correlation coefficient was 0.997. In the Bland–Altman plot, the relative bias was 0.01%. The overall Fleiss Kappa index was 0.9. No interference from hemoglobin variants was observed. Conclusion: The Premier Hb9210 demonstrated a high degree of automation, reproducibility, good agreement, minimal carry-over effect, and excellent linearity across the wide range of HbA1c levels commonly found in diabetic patients and was not influenced by Hb variants.

Receiving ADS-B Signals on Embedded Linux using RTL-SDR: A Practical Guide

This paper presents a solution for ADS-B signal reception with RTL-SDR based on embedded Linux. This system utilizes an inexpensive RTL-SDR radio receiver for real-time reading of aeronautical messages, and it is experimentally demonstrated that this combination provides a significantly cost-effective way to track aircraft. By using an embedded Linux system, this paper demonstrates the high degree of portability and automation that can be achieved with RTL-SDR and its built-in ADS-B signal reception capability. The solution can be used in a wide range of applications and has high practical value.

Plant Operator Support using Industrial Artificial Intelligence

Despite the high degree of automation in industrial control systems, human operators in industrial plants play a critical role in ensuring uptime, production quality, and safety. Plant operators do so by not only monitoring the process but also intervening when the process runs into unusual or exception situations. In this paper, we present to ensure smooth plant operation by automatically identifying and investigating potential upcoming issues as well as providing recommendations to plant operators on how to address them with confidence. This is achieved by applying Artificial Intelligence (AI) techniques including deep learning, process mining, and graph search, on historical industrial process data such as alarm and event data, audit trails, engineering documents, and safety procedures. Our solution has been validated on data from the Draugen oil field operated by the Norwegian oil and gas company, OKEA.

Condition Assessment of Heritage Buildings via Photogrammetry: A Scoping Review from the Perspective of Decision Makers

In recent years, advanced digital technologies have driven an outstanding paradigm shift in the field of architectural heritage, particularly for building modelling, historical documentation and touristic promotion. Nonetheless, they show great potentialities in the field of assessment and control of the state of conservation of heritage buildings. In particular, close-range and aerial photogrammetry have increasingly relied on low-cost and user-friendly tools and procedures, with a high degree of automation that makes them accessible to specialists who are foremost involved in architectural diagnosis and conservation, rather than in remote sensing sciences. In this framework, this paper provides a scoping review of 117 publications, based on the PRISMA protocol, from Scopus and Web of Science databases, related to the employment of photogrammetric models and methods, with specific focus on the targets and purposes of the diagnostic process, including decay mapping, structural monitoring and modelling, non-destructive investigation and multi-source documentation. In detail, the results point out that current studies mainly support robust processing of large amounts of information from direct observation of surface alterations, systematic correlation between materials, construction characteristics, visible anomalies and experimental measurements, as well as multi-disciplinary collaborative workflows through remote inspection and harmonized data management. Further improvements were identified, including standardization of acquisition procedures, automatization of elaboration pipelines, integration of real-time data, validation of diagnosis decision-making support tools and scalability to networks of assets.

Next-generation ice-nucleating particle sampling on board aircraft: characterization of the High-volume flow aERosol particle filter sAmpler (HERA)

Abstract. Atmospheric ice-nucleating particle (INP) concentration data from the free troposphere are sparse but urgently needed to understand vertical transport processes of INPs and their influence on cloud formation and properties. Here, we introduce the new High-volume flow aERosol particle filter sAmpler (HERA) which was specially developed for installation on research aircraft and subsequent offline INP analysis. HERA is a modular system consisting of a sampling unit and a powerful pump unit, and it has several features which were integrated specifically for INP sampling. Firstly, the pump unit enables sampling at flow rates exceeding 100 L min−1, which is well above typical flow rates of aircraft INP sampling systems described in the literature (∼ 10 L min−1). Consequently, required sampling times to capture rare, high-temperature INPs (≥ −15 ∘C) are reduced in comparison to other systems, and potential source regions of INPs can be confined more precisely. Secondly, the sampling unit is designed as a seven-way valve, enabling switching between six filter holders and a bypass with one filter being sampled at a time. In contrast to other aircraft INP sampling systems, the valve position is remote-controlled via software so that manual filter changes during flight are eliminated and the potential for sample contamination is decreased. This design is compatible with a high degree of automation, i.e., triggering filter changes depending on parameters like flight altitude, geographical location, temperature, or time. In addition to presenting the design and principle of operation of HERA, this paper describes laboratory characterization experiments with size-selected test substances, i.e., SNOMAX® and Arizona Test Dust. The particles were sampled on filters with HERA, varying either particle diameter (300 to 800 nm) or flow rate (10 to 100 L min−1) between experiments. The subsequent offline INP analysis showed good agreement with literature data and comparable sampling efficiencies for all investigated particle sizes and flow rates. Furthermore, the collection efficiency of atmospheric INPs in HERA was compared to a straightforward filter sampler and good agreement was found. Finally, results from the first campaign of HERA on the High Altitude and LOng range research aircraft (HALO) demonstrate the functionality of the new system in the context of aircraft application.

Review on positional significance of LSTM and CNN in the multilayer deep neural architecture for efficient sentiment classification

The deep learning revolution in the current decade has transformed the artificial intelligence industry. Eventually, deep learning techniques have become essential for many computational modeling tasks. Nevertheless, deep neural models provide a high degree of automation for natural language processing (NLP) applications. Deep neural models are extensively used to decode public reviews subjective to specific products, services, and other social activities. Further, to improve sentiment classification accuracy, several neural architectures have been developed. Convolutional neural networks (CNN) and Long-short term memory (LSTM) are the popular deep models employed in ensemble architectures for sentiment classification tasks. This review article extensively compares the competence of CNN and LSTM-based ensemble models to improve the sentiment accuracy for online review datasets. Further, this article also provides an empirical study on various ensemble models concerning the position of LSTM and CNN for efficient sentiment classification. This empirical study provides deep learning researchers with insights into building effective multilayer LSTM and CNN models for many sentiment analysis tasks.

The shift toward autonomous European inland waterway shipping: Identifying the gaps in the emerging socio-technical system

The introduction of automated solutions for inland waterways shipping supports the European vision for a sustainable and greener shift within maritime management and logistics. Despite increasing investments, impressive technological advancements, and active discussion to solve the regulatory and business model barriers, socio-technical aspects are still less studied in the preparation for the future of autonomous shipping. This study examines stakeholder and technology roles to map the gaps and challenges created in this emerging space using a system theory approach, specifically a recently developed work design system analysis tool named Change Agent Infrastructure (CHAI) analysis [1]. This framework was chosen to better understand the likely changes of the system with the introduction of automated technology and higher degrees of automation and digitalization in the European inland waterways. The analysis identifies that there are multiple receivers of the change and regulatory bodies will also be actors with considerable responsibilities to direct these changes.

An Automated Machine Learning Approach towards Energy Saving Estimates in Public Buildings

Reducing the energy consumption of buildings in the public sector is an important component in our efforts towards reaching our sustainability goals. In this context, a decisive prerequisite for administrations and policy makers is a tool for estimating the effectiveness of measures to reduce energy consumption. Estimating the impact of planned investments in building technology at scale, however, remains challenging, mainly for two reasons. For one, accurate physical modeling requires detailed building data, which can be difficult to obtain. Second, adapting established building models to novel measures aiming at energy consumption reduction is difficult. Hence, modeling building consumption patterns after retrofitting is a non-trivial task, and more research is needed to improve modeling techniques as well as to assess their effectiveness across a wide range of application scenarios. Modeling tools need to be generic enough to enable modeling of a variety of building types, they should ideally require as few input features as possible and they should allow for a high degree of automation in the selection and calibration of building modeling tools. Here, we propose a novel machine learning approach that does not require detailed building data and can automatically adapt to retrofitting measures. We evaluate our method on a data set of 113 public buildings in 4 building categories in Berlin, Germany. The data set contains energy consumption data in the initial state and after implementation of a weather-predictive heating control system. Despite being fully automated and requiring only minimal information about the building, our model can reliably predict the energy consumption of large public buildings better than established methods. All code and data are publicly released.

Transformation in substation automation: Cyber-Resilient Digital Substations (CyReDS) in power grids

Abstract The digitalization of substations leads to a high degree of automation, which is referred to as Digital Substations with the use of the IEC 61850. An increase in cybersecurity is necessary, and technically enabled by detection and incident response systems – security-by-design is currently often secondary. The next transformation step of substations is introduced and outlined in this paper as a cyber-resilient digital substation. Based on a state-of-the-art description, this paper presents a definition of cyber resilience for digital substations as a basis for the introduced cyber resilience monitor. The monitor acts as a central instance for recording, assessing and responding to security threats and incidents. Furthermore, the requirements for the system structure of the cyber-resilient digital substation are shown and underpinned with current research approaches.

Automatic Piston-Type Flow Standard Device Calibration System

Measurement of flow is crucial for assuring product quality, increasing manufacturing effectiveness, and promoting the development of science and technology. With the advancement of calibration and automation, standard devices using the mass method, volumetric method, and master meter method have limitations, such as low calibration efficiency and automation, large size, and complex operation. Innovations in this area are desperately needed. To realize the automation of calibrating ultrasonic water meters, a piston-type flow standard device calibration system with a high degree of automation, high calibration efficiency, small size, and easy operation was designed. A piston-type flow standard device was designed, the standard device was modeled, the selection of the main hardware and the design of the automated control of the hardware parts were completed; an automation control system adapted to the flow standard device was developed; and, furthermore, a water meter flow point calibration algorithm integrating the start–stop method and the dual-time method, as well as a water meter flow correction algorithm, was devised to improve the efficiency of ultrasonic water meter calibration. An uncertainty assessment of the designed system was completed; the standard uncertainty and expanded uncertainty of the device were 0.013% and 0.026%. Meanwhile, flow calibration tests were conducted, validating the rationality of the automated calibration algorithm for ultrasonic water meters. The results show that ultrasonic water meters calibrated with flow correction have a flow error within ±3% in the “low flow range” and within ±2% in the “high flow range”, with a repeatability of less than 0.05%. This indicates that a piston-type flow standard device, coupled with an automation calibration control system, can efficiently, accurately, and conveniently perform water meter calibration, and the system has good practical value.

Mining on Students’ Execution Logs and Repairing Compilation Errors Based on Deep Learning

Automatic program repair techniques based on deep neural networks have attracted widespread attention from researchers due to the high degree of automation and generality. However, there is a scarcity of high-quality labeled datasets available for training program repair models. This study proposes a method of mining reasonable program repair examples from student program execution logs. Additionally, we introduce the Rookie Simulator (RS), which simulates the error patterns commonly made by novice programmers and generates a large number of program repair sample pairs. To address the issue of low repair rates for infrequent and complex error patterns in compilation errors, the study proposes the attention-enhanced capsule network for program repair (ACNPR), a program repair model that integrates compiler feedback information and utilizes capsule networks to capture complex semantic features. Experimental evaluations were conducted using publicly available datasets, including the DeepFix, TEGCER, and a real course dataset named SUES-COJ mined in this study. The results indicate that our method consistently outperforms current state-of-the-art models in terms of full repair rates.

A Scalable Method to Improve Large-Scale Lidar Topographic Differencing Results

Differencing digital terrain models (DTMs) generated from multitemporal airborne light detection and ranging (lidar) data provide accurate and detailed information about three-dimensional (3D) changes on the Earth. However, noticeable spurious errors along flight paths are often included in the differencing results, hindering the accurate analysis of the topographic changes. This paper proposes a new scalable method to alleviate the problematic systematic errors with a high degree of automation in consideration of the practical limitations raised when processing the rapidly increasing amount of large-scale lidar datasets. The proposed method focused on estimating the displacements caused by vertical positioning errors, which are the most critical error source, and adjusting the DTMs already produced as basic lidar products without access to the point cloud and raw data from the laser scanner. The feasibility and effectiveness of the proposed method were evaluated with experiments with county-level multitemporal airborne lidar datasets in Indiana, USA. The experimental results demonstrated that the proposed method could estimate the vertical displacement reasonably along the flight paths and improve the county-level lidar differencing results by reducing the problematic errors and increasing consistency across the flight paths. The improved differencing results presented in this paper are expected to provide more consistent information about topographic changes in Indiana. In addition, the proposed method can be a feasible solution to upcoming problems induced by rapidly increasing large-scale multitemporal lidar given recent active government-driven lidar data acquisition programs, such as the U.S. Geological Survey (USGS) 3D Elevation Program (3DEP).

IMPROVEMENT OF INTELLIGENT INTERNAL COMBUSTION ENGINES

Research investigations of the solutions of internal combustion engine control problems based on artificial intelligence systems are quite relevant. The power of ship devices and mechanisms is increasing, the requirements for accuracy, reliability, speed and other indicators of the quality of control processes are increasing, increased operational requirements related to economy and efficiency of ship systems and equipment. Currently, there are a lot of studies which are successfully performed to create the installations with a higher degree of automation – adaptive (intelligent) internal combustion engines, including a class of marine diesel installations. This term means the Engine controlled by adaptive automatic control systems with elements of artificial intelligence that can apprehend and analyses quite complicated and changeable ambient and take a decision. However, the problematic issues of adapting a piston engine in operating conditions are still under study and theoretical conceptualization. In this article, a study of the scientific and technical problem of improving the operational qualities of intelligent marine diesel installations is carried out. The problem has a great technical importance and requires scientific substantiation of the directions of both improving the operational qualities and reliability of existing models, and identifying ways to modernize existing structures of marine diesel installations. Кeywords: internal combustion engines, marine diesel, intelligent engine, adaptive systems, adaptive motors, microprocessor controls, controlling algorithm, neural networks.

Automatic Relay Protection Calibration Device and System Design for New Intelligent Power Grid

Maintaining the protection device and eliminating the abnormal and fault defects of the device are important tasks for the maintenance of the power system. In general, relay protection equipment managers need to regularly check the relay protection device. According to the usual practice, relay protection professionals use the relay protection tester to manually connect. The traditional relay protection verification method has complicated operation, high professional requirements for testers. In this paper, a set of intelligent relay protection verification device with high degree of automation and harmonious human-computer interaction is developed to realize the communication between the host computer and the protection device and the tester. The host computer software and database were developed. The thermal overload protection in the SPAM150C motor protection device of ABB company is verified, and the reliability of the device is verified. The device can improve the efficiency of relay protection equipment inspection, reduce the technical threshold of operators, and reduce the probability of human error. It realizes the process and standardized control of the operation process of relay protection verification which has strong practical application value.

Air-coupled Ultrasonic Inspection of Thermoplastic Composite Structures for Aerospace Vehicles

Composites have become the most important type of material used in the manufacturing of aerospace vehicle components. One focus of the German Aerospace Center is to develop cost-efficient manufacturing processes for large-scale composite components such as liquid hydrogen tanks, rocket booster pressure vessels, and aircraft fuselages. To enable a high degree of automation in the manufacturing, robot-assisted tape-laying processes with flash lamp consolidation of thermoplastic prepregs are preferred. Nondestructive inspection is crucial to monitor manufacturing quality, and various forms of ultrasonic inspection have become standard. Air-coupled ultrasonic inspection is applied at the German Aerospace Center, and since access to the component is usually available only from one side, Lamb waves are excited and detected in focused slanted reflection mode. Like the manufacturing process, air-coupled ultrasonic inspection has been automated as well to avoid a significant pause during or after manufacturing. The paper demonstrates the robot-assisted air-coupled ultrasonic inspection of a liquid hydrogen tank. The robotic program is generated using offline programming software, based on the topography of a CAD model of the hydrogen tank. The inspection is carried out by scanning the component while ultrasonic pulses are triggered on a regular grid. The ultrasonic A-scans together with the measurement coordinates are monitored simultaneously. The data are used to generate and evaluate a three-dimensional C-scan.

Artificial-Intelligence-Based Triple Phase Shift Modulation for Dual Active Bridge Converter With Minimized Current Stress

The dual active bridge (DAB) converter has been popular in many applications for its outstanding power density and bidirectional power transfer capacity. Up to now, triple phase shift (TPS) can be considered as one of the most advanced modulation techniques for DAB converter. It can widen zero voltage switching range and improve power efficiency significantly. Currently, current stress of the DAB converter has been an important performance indicator when TPS modulation is applied for smaller size and higher efficiency. However, to minimize the current stress when the DAB converter is under TPS modulation, two difficulties exist in analysis process and realization process, respectively. Firstly, three degrees of modulation variables in TPS modulation bring challenges to the analysis of current stress in different operating modes. This analysis and deduction process leads to heavy computational burden and also suffers from low accuracy. Secondly, to realize TPS modulation, if a lookup table is adopted after the optimization of modulation variables, modulation performance will be unsatisfactory because of the discrete nature of lookup table. Therefore, an AI-based TPS modulation (AI-TPSM) strategy is proposed in this paper. Neural network (NN) and fuzzy inference system (FIS) are utilized to deal with the two difficulties mentioned above. With the proposed AI-TPSM, the optimization of TPS modulation for minimized current stress will enjoy high degree of automation which can relieve engineers’ working burden and improve accuracy. In the end of this paper, the effectiveness of the proposed AI-TPSM has been experimentally verified with a 1 kW prototype.

Reducing the Costs of Automation Failure by Providing Voluntary Automation Checking Tools.

We investigated the extent to which a voluntary-use range and bearing line (RBL) tool improves return-to-manual performance when supervising high-degree conflict detection automation in simulated air traffic control. High-degree automation typically benefits routine performance and reduces workload, but can degrade return-to-manual performance if automation fails. We reasoned that providing a voluntary checking tool (RBL) would support automation failure detection, but also that automation induced complacency could extend to nonoptimal use of such tools. Participants were assigned to one of three conditions, where conflict detection was either performed: manually, with RBLs available to use (Manual + RBL), automatically with RBLs (Auto + RBL), or automatically without RBLs (Auto). Voluntary-use RBLs allowed participants to reliably check aircraft conflict status. Automation failed once. RBLs improved automation failure detection - with participants intervening faster and making fewer false alarms when provided RBLs compared to not (Auto + RBL vs Auto). However, a cost of high-degree automation remained, with participants slower to intervene to the automation failure than to an identical manual conflict event (Auto + RBL vs Manual + RBL). There was no difference in RBL engagement time between Auto + RBL and Manual + RBL conditions, suggesting participants noticed the conflict event at the same time. The cost of automation may have arisen from participants' reconciling which information to trust: the automation (which indicated no conflict and had been perfectly reliable prior to failing) or the RBL (which indicated a conflict). Providing a mechanism for checking the validity of high-degree automation may facilitate human supervision of automation.

Inferring material properties from FRP processes via sim-to-real learning

Fiber reinforced polymers (FRP) provide favorable properties such as weight-specific strength and stiffness that are central for certain industries, such as aerospace or automotive manufacturing. Liquid composite molding (LCM) is a family of often employed, inexpensive, out-of-autoclave manufacturing techniques. Among them, resin transfer molding (RTM), offers a high degree of automation. Herein, textile preforms are saturated by a fluid polymer matrix in a closed mold.Both impregnation quality and level of fiber volume content are of crucial importance for the final part quality. We propose to simultaneously learn three major textile properties (fiber volume content and permeability in X and Y direction) presented as a three-dimensional map based on a sequence of camera images acquired in flow experiments and compare CNNs, ConvLSTMs, and Transformers. Moreover, we show how simulation-to-real transfer learning can improve a digital twin in FRP manufacturing, compared to simulation-only models and models based on sparse real data. The overall best metrics are: IOU 0.5031 and Accuracy 95.929 %, obtained by pretrained transformer models.