High Level Of Automation Research Articles

UTM Architecture and Flight Demonstration in Korea

Unmanned Aircraft System Traffic Management (UTM) is a traffic management system enabling drones to safely and efficiently fly in low-altitude airspace below 120~150m (400~500ft). UTM provides services such as communication, flight route management, location monitoring, and collision avoidance so that drones completing various missions can fly beyond visual line of sight (BVLOS) safely and increase the usability of airspace. In other words, UTM is a new air traffic management for drones with high levels of automation, advanced decision making and control. Many countries around the world are developing UTM systems that systematically manage the traffic of drones flying at low altitude. In Korea, UTM research has been ongoing as an R&D project since 2017. The purpose of this paper is to introduce the Korean UTM system and to apply it to actual flight demonstration through the developed operational procedures. The approach of this article is to establish Korean UTM architecture through existing references and examples from other countries, devise an operational procedure suitable for the system, and describe the results of using it for flight demonstration. In other words, this paper covers Korea’s UTM architecture, operational procedures, and flight demonstration through a macro approach to UTM. In addition, this paper presents policy and technical challenges that UTM must go through and that need to be solved in the future, which are classified into four categories.

GIS Based Procedural Modeling in 3D Urban Design

Traditional urban design is time-consuming and laborious. We propose a computer-generated architecture (CGA)-based workflow in this work, with the goal of allowing designers to take advantage of a high level of automation. This workflow is based on procedural modeling. A three-step CGA rule was applied to implement 3D urban procedural modeling, (1) parcel subdivision and clustering, (2) building extrusion, and (3) texture mapping. Parcel subdivision and clustering is the key step of layout modeling, giving the modeler flexibility to adjust the placement and size of the inner building lots. Subsequently, a land-use-based combination of eight common building types and layouts was used to generate various urban forms for different urban functional zones. Finally, individual buildings were decorated by creating texture maps of a planar section of the building facade or, alternatively, decomposing facades into sets of repeating elements and texture maps. We employed the proposed workflow in the H-village urban redevelopment program and an air–rail integration zone development program in Guangzhou. Three design proposals were generated for each project. The results demonstrated that this workflow could generate multiple layout proposals and alternative facade textures quickly and, therefore, address most of the collaborative issues with its analysis functions, including a flexible adjustment mechanism and real-time visualization.

ПЕРЕХІД ДО ПРОМИСЛОВОСТІ 4.0 З ПРОГНОЗОВАНИМИ РІШЕННЯМИ ДЛЯ ОБСЛУГОВУВАННЯ

To ensure the high level of automation required in modern industrial applications, equipment must be more efficient, intelligent, context-aware and more connected; it must also be more reliable and provide greater security for the people who interact with them.

PERSONAL THOUGHTS ON AUTOMATION, PROGRAMMING CULTURE, AND MODELBASED SOFTWARE ENGINEERING

When compared to more conventional development techniques, model-based software engineering (MBSE), a methodology for developing software, is characterized in part by much higher levels of automation. Many important components of development involve the use of computer-based tools, such as authoring support (many MBSE languages are primarily visual), automatic or semi-automatic verification, the automated conversion of requirements into corresponding programmers, and many more. There is little question that automation, when correctly designed and achieved, may significantly boost the productivity of software developers and improve the quality of their programme, given the historical examples, such as the advent of compilation technology. Therefore, it becomes sense to predict that MBSE will swiftly overtake other software development methods, much to how computer-aided design methods for hardware were quickly adopted. However, this hasn't happened. This is an opinion piece that draws from the author's extensive knowledge of MBSE and its use in business. In it, we look at the factors that led to this paradoxical situation.

Implementation of the calculation and analytical method for rationing metal consumption in the production of seamless hot-formed pipes at JSC TAGMET

This article discusses the issues of the correlation of theoretical provisions on the sphere of metal use planning and the practical implementation of scientific principles in direct economic practice. Existing rationing methods are analyzed in detail, the most advanced of them, the calculation and analytical method, is identified. It is indicated that this method is implemented only with the help of mathematical modeling. The basis for evaluating the issues of practical implementation is a case study on the development of JSC "Russian Research Institute of the Pipe Industry" tools with a high level of automation and allowing JSC "Taganrog Metallurgical Plant" to solve the problem of scientifically and technically justified calculation of the planned metal consumption in the production of pipe metal products at a pipe rolling mill. Premium Quality Finishing (PQF) continuous mill. The normative and instructive documents regulating this type of activity are given. The process of creating tools for standardizing metal consumption in the form of software products, common to all such works, is characterized. The non-stationary requirements for the process of creating tools, caused by the conditions of TAGMET JSC, are characterized. The emphasis in the material is on describing the features of the accounting policy, its changes, PJSC "Pipe Metallurgical Company" and JSC "Taganrog Metallurgical Plant" in the field of metal consumption rationing, through the chronology of the implementation of these features in software and methodological support. The dynamics of the process of introducing tools into the practice of the plant's specialists and the influence of subjective and objective factors on this process is traced. The results obtained from the implementation, which are an improved process of normalization, are reflected. It also indicates the progressive influence of the optimization of one process on the "adjacent" elements of the management structure.

Development of a flexible tooling system for sheet metal bending

This article presents the design and development of a flexible tooling system for sheet metal bending. The flexible tooling system aims to reduce manufacturing disturbances and increase the efficiency of the forming process. First and foremost, the structural behaviour of the sheet metal is investigated using the finite element method for the numerical simulation of the three-point bending process. The analysis’ findings enabled the prediction of component reaction to loads, which are essential for the further optimization and enhancement of the tooling system’s flexibility. At the initial stage of the development phase, SolidWorks, the computer-aided design software, is utilized to visualise the flexible tooling system and improve the tooling connectivity design. Furthermore, the prototype is developed by integrating mechanical and electrical components, such as the Arduino Mega microcontroller, stepper motors, and digital stepper drivers. Automation is achieved by programming the Arduino microcontroller board and controlling the stepper motors’ movement to ensure precise displacement and speed control of the forming tools. The tooling system’s major qualities are its high flexibility, achieved through the implementation of two moveable support cylinders and the possibility of being further upgraded to a closed-loop forming system. The higher level of automation and optimization of the sheet metal bending process can lead to improved processing efficiency and help achieve the desired formed products with higher quality and the required geometric tolerance. It is expected that the development of a flexible tooling system will find widespread application in sheet metal bending processes, resulting in reduced material costs, rapid equipment set-up and higher processing repeatability.

An architecture and performance evaluation framework for artificial intelligence solutions in beyond 5G radio access networks

The evolution of mobile communications towards beyond 5th-generation (B5G) networks is envisaged to incorporate high levels of network automation. Network automation requires the development of a network architecture that accommodates multiple solutions based on artificial intelligence (AI) and machine learning (ML). Consequently, integrating AI into the 5th-generation (5G) systems such that we could leverage the advantages of ML techniques to optimize and improve the networks is one challenging topic for B5G networks. Based on a review of 5G system architecture, the state-of-the-art candidate AI/ML techniques, and the progress of the state of the art, and the on AI/ML for 5G in standards we define an AI architecture and performance evaluation framework for the deployment of the AI/ML solution in B5G networks. The suggested framework proposes three AI architectures alternatives, a centralized, a completely decentralized and an hybrid AI architecture. More specifically, the framework identifies the logical AI functions, determines their mapping to the B5G radio access network architecture and analyses the associated deployment cost factors in terms of compute, communicate and store costs. The framework is evaluated based on a use case scenario for heterogeneous networks where it is shown that the deployment cost profiling is different for the different AI architecture alternatives, and that this cost should be considered for the deployment and selection of the AI/ML solution.

Evaluating levels of automation with different feedback modes in an assistive robotic table clearing task for eldercare

This paper focuses on how the autonomy level of an assistive robot that offers support for older adults in a daily task and its feedback affect the interaction. Identifying the level of automation (LOA) that prioritizes older adults’ preferences while avoiding passiveness and sedentariness is challenging. The feedback mode should match the cognitive and perceptual capabilities of older adults and the LOA. We characterized three LOAs and paired them with two modes of feedback in a human-robot collaborative task. Twenty-seven older adults participated in evaluating the LOA-feedback variations in a mixed experimental design, utilizing an experimental setup of an assistive robot in a table clearing task. The quality of the interaction was evaluated with objective and subjective measures. The combination of high LOA with voice feedback improved the overall interaction when compared to other LOA and feedback combinations. This study emphasizes the importance of appropriate coupling of LOA and feedback for successful interaction of the older adults with an assistive robot.

Conveyor-Less Urban-Car Assembly Factory with VaaC and Matrix System

The advent of autonomous electric vehicles (AEVs) will give drivers time and space instead of focusing on driving. Because of this, some drivers may want to personalize their car for their work, while others may want to customize their vehicle space to be more suitable for relaxation, which will accelerate the megatrend of mass individualization. However, the production of individualized cars faces several challenges. For example, since high-level automation during individualized car production is difficult, a stable skilled labor supply is essential, low-volume/high-variety production is required, and customer proximity or involvement is also important. These conditions can be satisfied by building a car assembly plant in an urban area. The problem is that urban areas are often spatially and environmentally constrained. However, it is be possible to overcome these urban limitations by implementing a conveyor-less micro factory. The objective of this study is to propose a new iterative matrix-system layout design method that can realize a conveyor-less urban car assembly factory with two technologies—VaaC (vehicle as a conveyor) and matrix assembly system. VaaC consists of three novel ideas: sensor skid, safety-sensor guidance system, and vehicle-powered devices, and this paper views each of them in detail. The proposed iterative matrix-system layout design method consists of four steps: (1) layout refinement, (2) simulation, (3) cost analysis, and (4) optimization check, and will examine how each step is performed through simple examples. The authors hope that this paper will arouse interest and provide elements to spur future research on the conveyor-less urban car assembly system.

Explanations in Autonomous Driving: A Survey

The automotive industry has witnessed an increasing level of development in the past decades; from manufacturing manually operated vehicles to manufacturing vehicles with a high level of automation. With the recent developments in Artificial Intelligence (AI), automotive companies now employ blackbox AI models to enable vehicles to perceive their environments and make driving decisions with little or no input from a human. With the hope to deploy autonomous vehicles (AV) on a commercial scale, the acceptance of AV by society becomes paramount and may largely depend on their degree of transparency, trustworthiness, and compliance with regulations. The assessment of the compliance of AVs to these acceptance requirements can be facilitated through the provision of explanations for AVs' behaviour. Explainability is therefore seen as an important requirement for AVs. AVs should be able to explain what they have 'seen', done, and might do in environments in which they operate. In this paper, we provide a comprehensive survey of the existing body of work around explainable autonomous driving. First, we open with a motivation for explanations by highlighting and emphasising the importance of transparency, accountability, and trust in AVs; and examining existing regulations and standards related to AVs. Second, we identify and categorise the different stakeholders involved in the development, use, and regulation of AVs and elicit their explanation requirements for AV. Third, we provide a rigorous review of previous work on explanations for the different AV operations (i.e., perception, localisation, planning, control, and system management). Finally, we identify pertinent challenges and provide recommendations, such as a conceptual framework for AV explainability. This survey aims to provide the fundamental knowledge required of researchers who are interested in explainability in AVs.

Development of Steering Control System for Autonomous Vehicle

Automation can help us to reduce the number of crashes on our roads. Through research it is identified that 94 percent of the accidents that occur are because of driver behavior or error as a factor and self-driving vehicles can help reduce driver error. High levels of automation have the potential to reduce risky and dangerous driver behavior and prevent accidents. The main aim is to convert the manual operated steering of the vehicle into fully autonomous steering. The objective of Steering Control System is to control the vehicle’s steering while the vehicle is in motion and also to take accurate decisions while making a turn from the given inputs. The main purpose to develop a steering system for the autonomous vehicle is to replace the manual steering of the vehicle into driverless steering. The steering control is responsible for the vehicle’s steering i.e., at what desired angle the vehicle need to turn. A PID (Proportional Integral Derivative) controller and an encoder is basically used to control the system based on the necessary conditions and requirement’s. For the vehicle’s steering the encoder is used to generate pulses when the steering wheel is turned so that those pulse values can be sent to the DC Motor which is attached to the front axle which is responsible for the vehicle to turn. This autonomous vehicle is a Level-4 automation system and the benefit of this automation is that the vehicle can be even operated in manual mode whenever it’s necessary.

Integrating NLP and context-free grammar for complex rule interpretation towards automated compliance checking

Automated rule checking (ARC) is expected to significantly promote the efficiency and compliance of design in the construction industry. The most vital and complex stage of ARC is interpreting the regulatory text into a computer-processable format. However, existing systems and studies of rule interpretation either require considerable time-consuming manual effort or are based on exhaustive enumerations of matching patterns with a limited scope of application. To address this problem, this research integrates natural language processing (NLP) and context-free grammar (CFG) to propose a novel generalized rule interpretation framework, which can parse regulatory text like a domain-specific language. First, a syntax tree structure with several semantic elements is proposed to represent the roles and relations of concepts in regulatory text. Second, a deep learning model with the transfer learning technique is utilized to label the semantic elements in a sentence. Finally, a set of CFGs is built to parse a labeled sentence into the language-independent tree structure, from which computable checking rules can be generated. Experimental results demonstrate that our method outperforms the state-of-the-art methods: it achieves 99.6% and 91.0% accuracies for parsing single- and multi-requirement sentences, respectively, where the multi-requirement sentences are more complex and common but difficult for existing methods to deal with. This research contributes a method and framework for rule interpretation with both a high level of automation and wide scope of application, which can create computable rules from various textual regulatory documents. This research also publishes the first regulation dataset for future exploration, validation, and benchmarking in the ARC area.

The effect of seating recline on sleep quality, comfort and pressure distribution in moving autonomous vehicles

The revolution of technologically advanced vehicles with a high level of automation involves a profound transformation. The focus of most research in this area has been on the use of travel time for different use cases. Sleeping is one of the most time-consuming activities in everyone's life; therefore, this has been described as one of the most desired use cases for fully automated vehicles. In order to identify the best conditions to allow sleep and improve sleep quality while travelling in such vehicles, two studies were performed: a sleep study and a pressure distribution study, the results of which are included in this document. The focus of both studies was on two seat positions: reclined (60° backrest recline) and flat (87° backrest recline). In the sleep study, forty participants had the opportunity to sleep during a 90-min drive in order to evaluate long-term comfort and subjective sleep quantity and quality. Although both positions resulted in generally similar results in terms of sleep and comfort, some significant differences were identified. Karolinska Sleepiness Scale results showed that sleepiness increased in the reclined position, whereas it decreased in the flat position. Moreover, the self-reported parameter Wake After Sleep Onset was higher in the reclined position. In the pressure distribution study, it was possible to identify specific seat prototype limitations indicating inadequate support, which was related to discomfort detected during the sleep study. As a conclusion, the comparison between the reclined and flat positions showed indications that, in moving fully automated vehicles, the flat seat position is the most comfortable and effective for sleeping.

A hierarchical deep convolutional regression framework with sensor network fail-safe adaptation for acoustic-emission-based structural health monitoring

Lamb wave-based signals from sparse-distributed sensors are complicated and difficult to process for structural health monitoring (SHM), not only due to their dispersive and multi-mode nature, but also due to the increasing complexity of materials and structures. Deep learning (DL) has attracted huge attention to help solve physical problems with a high level of automation and accuracy. However, its reliability and robustness are still questioned when performing the case-by-case model trained by inadequate datasets for practical scenarios, where many variables exist. In this study, a hierarchical deep convolutional regression framework is proposed to solve the impact source localization problem by acoustic emission signals. One-dimensional (1D) network is used due to its capability to process fast with raw time-series data. The window length of input data and the target of output results are discussed to improve the over-fitting issue. The sensor network fail-safe mechanism is designed via generalizing the model to handle abnormal situations with random faulty channels. Data augmentation and transfer learning techniques are utilized to train the fail-safe model without the need for additional experimental data. Pristine case and multiple random-faulty-channel cases are used to test and validate the adaptation performance of the fail-safe model. The whole framework combines both pristine and fail-safe models to achieve high accuracy of impact localization results of both a simple homogeneous plate and a complex inhomogeneous plate with geometric features. The proposed DL framework of greatly improved reliability and robustness, also short processing time, is well suitable for real-time and in-situ SHM applications.

Hierarchical bead materials multi-property design for wire-feed laser additive manufacturing

Wire-feed laser additive manufacturing has gained attention for years with its promises of high-level automation, reducing materials waste, overall costs, and large-scale volume production. However, printing material with multiple desired bead properties is a great challenge due to the high cost of experimental data and trial and error methods and the interdependent correlations among the properties. This study proposes a comprehensive multi-property integrated design framework based on learning from experiments data to enable quality control of part (bead) surface finish, geometry and microstructural properties. The printed bead process – microstructure – geometry relationships and process features' importance scores are investigated. The overall bead quality (smooth, ripple, and failed), geometry (e.g. bead height, width, fusion zone depth, fusion zone area), and microstructures characteristics are simultaneously optimized to obtain the optimal processing window. Specifically, the desirable property thresholds are defined experimentally, and the microstructures and geometries of printed beads under various process combinations have been predicted by machine learning models and visualized in a 3D contour space. With these predictions, the process parameters are optimized by passing through a set of filters to filter out undesired process candidates from a process searching space. The validation results highlight that the process parameterizations optimized by this framework can speed up the manufacturing of materials with the desired bead multi-performance.

Use of algorithms in BIM-based audit processes

Abstract Building information modeling is a complex and structure-based methodology. It applies predefined steps and frameworks; however, an audit procedure can be complicated and time-consuming. The steps of the evaluations are based on logical connections that also form algorithms in a manual workflow. Algorithms can be interpreted by computers with the help of software languages. A higher level of automation, more efficient workflows, and more economical and accurate results can be developed by using algorithms.

EXTRACTION OF ORTHOGONAL BUILDING BOUNDARY FROM AIRBORNE LIDAR DATA BASED ON FEATURE DIMENSION REDUCTION

Abstract. Building boundary extraction is an active research topic in the field of feature extraction from airborne LiDAR point cloud data. Owing to the high complexity of most building extraction algorithms based on point clouds, multiple feature parameters must often be combined with iterative operations, particularly in the process of mitigating the sawtooth phenomenon using the sleeve algorithm and its improved versions. To improve the degree of automation and ensure accuracy, this study proposes a fast corner point detection method based on a dimensionality reduction technique, which utilizes reduced data mapping from 3D to 2D. We converted the boundaries extracted by the alpha shape algorithm to a 2D image and applied recursive Gaussian filtering with a relatively high level of automation to smoothen the image edges and mitigate the sawtooth phenomenon, thereby improving upon the sleeve algorithm, which requires a large number of iterations. Subsequently, the Douglas Peucker algorithm is used to retrieve the contour key points after extracting the contour lines and obtaining the regularized building contours using the grouped orthogonal regularization method. To verify the accuracy of the algorithm, it was compared with a cluster and adjustment (CAA)method based on the sleeve algorithm using three major evaluation metrics with respect to four representative building instances in two experimental datasets of urban areas. The value of the RMSE was reduced by an average of 43.79%. In addition, the time complexity decreased from O(n2) to O(n). These results demonstrate that the proposed method improves not only the accuracy of boundary extraction, but also the efficiency of data processing.

Robots and protest: does increased protest among Chinese workers result in more automation?

Abstract The rising level of automation has increasingly attracted scholars' attention. On the other hand, there are many studies of the consequences of social movements, but relatively fewer studies focus on their economic consequences, and even fewer studies have examined their consequences on automation. This article bridges the gap between the two literatures by hypothesizing that a rising number of labor protests will lead to a higher level of automation. We argue that political economy factors influence the adoption of more automation. Protests anticipate higher wages and labor costs; contest for social power with employers and the state, and, in extreme cases, pose a public relations challenge to employers, which will likely push employers to replace human workers with robots. We empirically test the relationship by using two protest event datasets in China, the China Labor Bulletin and Collective Action from Social Media and robot data from the International Federation of Robotics. Statistical analysis shows that provinces and industries that have more protests also tend to concentrate more robots, and the results are robust to most specifications and placebo tests. The findings have implications for both understanding the mechanism explaining rising automation and the consequences of social protests.

Digitalization and automation in construction project’s life-cycle: a review

The fourth construction industry revolution (i.e., Construction 4.0), driven by the fourth industrial revolution, introduces technological novelties to the construction industry in the direction of utilizing automation and digitalization potential. Various levels of maturity and adoption of these technologies have been identified separately in previous studies. In this study, a state-of-the art literature review is presented with the aim of determining the genesis and current levels of digitalization and automation, as well as their interoperability, among the main construction projects’ life-cycle phases. The results revealed that the construction project life-cycle phases are indeed at significantly different digitalization and automation levels. The initiation phase was found to be at a low level of digitalization and automation, the design and planning phase at a high level of digitalization with a low level of automation, and the execution phase at low-level digitalization with a higher level of automation. Since the topic is continuously developing, this research could be conducted in the near future to determine the advancements in comparison to the current conclusions.

Automatic data‐driven stoichiometry identification and kinetic modeling framework for homogeneous organic reactions

AbstractData‐driven and knowledge‐driven methods are two approaches used in studying reaction kinetics. This article proposes a hybrid‐modeling framework for homogeneous synthesis reactions, which combines the advantages of high level of automation in the data‐driven approach and improved accuracy in the knowledge‐driven approach. A constrained enumeration method is proposed to generate possible candidate stoichiometries, and dynamic response surface methodology, target factor analysis, and mass balance are used together for identifying stoichiometries one‐by‐one, without the necessity of an expert‐generated candidate list. Then, the previously screened stoichiometries are formed into different groups that represent candidate reaction systems, and the group (or groups) with the greatest likelihood will be identified, based on kinetic fitting and reaction dynamic criteria. This framework has been demonstrated by several examples of different reaction systems. The true reaction stoichiometries are all correctly identified, and the accurate kinetic models are obtained, showing satisfactory performance of the proposed method.