Abstract The Lean Production paradigm has become the major approach to create highly efficient processes in industry since the early 1990s. After the sudden end of the Computer Integrated Manufacturing (CIM) era, which finally was doomed to fail due to its unrulable complexity of the required automation technology, the Lean approach was successful because of its high effectiveness by reducing complexity and avoiding non-value-creating process steps. Today, the term Industry 4.0 describes a vision of future production. Many people are at least skeptical or even hostile towards this new approach. This position paper gives an overview over existing combinations of Lean Production and automation technology, also called Lean Automation. Furthermore, it discusses major Industry 4.0 corner stones and links them to the well-proven Lean approach. Examples of combining both are smart watches for supporting the Andon principle or Cyber Physical Systems (CPS) for a flexible Kanban production scheduling.
The automatic identification of P-wave arrival times in microseismic signals is crucial for the intelligent monitoring and early warning of dynamic hazards in coal mines. Traditional methods suffer from low accuracy and poor stability due to complex underground geological conditions and substantial noise interference. This paper proposes a microseismic P-wave arrival time automatic picking model that integrates physical constraints with a deep learning architecture. This study trained and optimized the model using a high-quality, manually labeled dataset. A systematic comparison with the AR picker algorithm and the short-term–long-term average ratio method revealed that this model achieved a precision of 96.60%, a recall of 90.59%, and an F1 score of 93.50% on the test set, with a P-wave arrival time-picking error of less than 20 ms. The average arrival time error was only 5.49 ms, significantly outperforming traditional methods. In cross-mining area generalization tests, the model performed excellently in two mining areas with consistent sampling frequencies (1000 Hz) and high signal-to-noise ratios, demonstrating good engineering transferability. However, its performance decreased in a mining area with a higher sampling rate and stronger noise, indicating its sensitivity to data acquisition parameters. This study developed a high-precision, robust, and potentially cross-domain adaptive model for automatically picking microseismic P-wave arrival times. This model provides support for the automation, precision, and intelligence of coal mine microseismic monitoring systems and has significant practical value in promoting real-time early warning and risk prevention for mine dynamic hazards.
Collision avoidance between vehicles is a great challenge, especially in the context of mixed driving of connected and autonomous vehicles (CAVs) and human-driven vehicles (HVs). Advances in automation and connectivity technologies provide opportunities for CAVs to drive cooperatively. This paper proposes a proactive collision avoidance model, aiming to avoid collisions by controlling the speed and lane-changing behavior of CAVs. In the model, the subject vehicle first collects information about surrounding lanes and judges the traffic conditions; it then chooses to decelerate or change lanes to avoid collisions. The subject vehicle also searches for the optimal vehicle in the surrounding lanes for cooperation. The effectiveness of the proposed collision avoidance model is verified through the Python-SUMO platform. The experimental results show that the performance of the collision avoidance model is better than that of the cooperative adaptive cruise control (CACC) model in terms of average speed, lost time and the number of vehicle conflicts, proving the advantages of the proposed model in safety and efficiency.
Feudjio Ghislain, Saha Tchinda Beaudelaire, Romain Atangana
et al.
Context: Early detection of ophthalmic diseases, such as drusen and glaucoma, can be facilitated by analyzing changes in the retinal microvascular structure. The implementation of algorithms based on convolutional neural networks (CNNs) has seen significant growth in the automation of disease identification. However, the complexity of these algorithms increases with the diversity of pathologies to be classified. In this study, we introduce a new lightweight algorithm based on CNNs for the classification of multiple categories of eye diseases, using discrete wavelet transforms to enhance feature extraction. Methods: The proposed approach integrates a simple CNN architecture optimized for multi-class and multi-label classification, with an emphasis on maintaining a compact model size. We improved the feature extraction phase by implementing multi-scale decomposition techniques, such as biorthogonal wavelet transforms, allowing us to capture both fine and coarse features. The developed model was evaluated using a dataset of retinal images categorized into four classes, including a composite class for less common pathologies. Results: The feature extraction based on biorthogonal wavelets enabled our model to achieve perfect values of precision, recall, and F1-score for half of the targeted classes. The overall average accuracy of the model reached 0.9621. Conclusion: The integration of biorthogonal wavelet transforms into our CNN model has proven effective, surpassing the performance of several similar algorithms reported in the literature. This advancement not only enhances the accuracy of real-time diagnoses but also supports the development of sophisticated tools for the detection of a wide range of retinal pathologies, thereby improving clinical decision-making processes.
ObjectiveThis paper develops a multi-stage path planning algorithm for the auto docking operation of an underactuated surface vehicle (USV). As one of the most difficult tasks, docking requires advanced maneuvering skills. Therefore, it is necessary to generate a smooth trajectory in the berthing environment while maintaining control accuracy. MethodThe proposed method introduces a hybrid A* search algorithm in the pre-docking stage in which the well-known A* search algorithm is combined with the kinematic state space of the USV using the Reeds–Shepp curves, enabling it to produce sub-optimal paths that guarantee kinematic feasibility and low collision risk. For the docking stage, a cubic Bezier curve is introduced to represent the target path. The generated trajectories are then tested in numerical simulations wherein a mathematical model of a ship with two fixed propellers is established and verified. ResultsThe simulation and experimental results show that the path planning and tracking performance of the method are satisfactory.ConclusionThe proposed path planning method can contribute to automatic ship docking and the development of smart ships.
Yu. A. Tsoy, V. V. Kirsanov, R. A. Mamedova
et al.
The paper shows the development of technologies and tools of mechanization and automation in livestock farming for the period from 1930 to the present. (Research purpose) Given the cyclical nature of processes and the nuanced interpretations of scientific heritage, it is necessary to investigate the historical experience and contributions of scientists to the development and practical implementation of mechanization and automation tools in livestock farming. (Methods and materials) The paper examines the primary stages of mechanization and automation in cow milking, starting with the development of the first Soviet three-stroke milking machine DA-3. It considers the contributions made by specialized institutes of the All-Russian Academy of Agricultural Sciences and individual scientists in formulating testing methodologies for milking machines. Additionally, it explores the development and adoption of novel electromechanical tools for dairy farms. (Results and discussion) Considered in this paper are the pioneering efforts in the creation of the first milking parlors. These include the stationary herringbone type machines with a parallel-pass design, spearheaded by V.S. Krasnov, V.F. Korolev, V.P. Larin, V.P. Pokhvalensky, and A.N. Dormidontov, and the development of mobile carousel parlors of the «rotating herringbone» type, engineered by the design bureau of the Siberian Research Institute of Agriculture under the supervision of I.I. Teslenko, N.V. Krasnoshchekov, K.S. Shapovalov, N.K. Vazenmiller, A.V. Goldenfang. (Conclusions) The paper notes the significance of adopting a systematic approach to the developing of machinery for livestock farming mechanization. This approach was actively pursued by N.M. Morozov, the member of the Russian Academy of Sciences. A significant milestone in the advancement of the theory and practice of animal husbandry processes was the pioneering work in developing and implementing technology for flow-conveyor animal service, headed by Member of the Russian Academy of Sciences L.P. Kormanovsky. The paper reveals the necessity of conducting a historical practices from previous generations. This approach helps to develop modern models of machinery and equipment and avoid mere replication of foreign designs. It also facilitates independent research and development efforts, incorporating insights from the accumulated experience of utilizing equipment and technologies in Russia.
The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations
Originality/value: To the best of the authors’ knowledge, this chapter presents the first systematic and in-depth review of RAISA technologies in the restaurant industry.
MultiCal is an affordable, high-precision measuring device designed for the on-site calibration of industrial robots. Its design features a long measuring rod with a spherical tip that is attached to the robot. By restricting the rod’s tip to multiple fixed points under different rod orientations, the relative positions of these points are accurately measured beforehand. A common issue with MultiCal is the gravitational deformation of the long measuring rod, which introduces measurement errors into the system. This problem becomes especially serious when calibrating large robots, as the length of the measuring rod needs to be increased to enable the robot to move in a sufficient space. To address this issue, we propose two improvements in this paper. Firstly, we suggest the use of a new design of the measuring rod that is lightweight yet has high rigidity. Secondly, we propose a deformation compensation algorithm. Experimental results have shown that the new measuring rod improves calibration accuracy from 20% to 39%, while using the deformation compensation algorithm, the accuracy increases from 6% to 16%. In the best configuration, the calibration accuracy is similar to that of a measuring arm with a laser scanner, producing an average positioning error of 0.274 mm and a maximum positioning error of 0.838 mm. The improved design is cost-affordable, robust, and has sufficient accuracy, making MultiCal a more reliable tool for industrial robot calibration.
Zhiyuan Jiang, Xianzhen Huang, Pengfei Ding
et al.
Spiral water jackets are essential refrigeration units in motorized spindles. The design of spiral water jackets directly affects the machining accuracy and reliability of motorized spindles. In this regard, a reliability-based design optimization (RBDO) method is presented to obtain the optimal configuration of the spiral water jacket. The proposed RBDO algorithm aims to minimize the volume of the spiral water jacket and power loss of the cooling equipment while ensuring machining reliability. The geometric correlation and machining reliability are selected as constraints. The Kriging surrogate model is established to approximate the time-consuming finite element model for improving computational efficiency. An efficient metaheuristic algorithm is introduced to perform optimal exploration for the proposed RBDO problem. The risk speed of the motorized spindle is investigated by the experiment and finite element analysis. Subsequently, the effects of various cooling configurations on thermal error are investigated thoroughly. Eventually, the proposed RBDO is conducted on a motorized spindle. Through probabilistic evaluation, machining reliability is guaranteed by the proposed RBDO compared to deterministic design optimization. This paper provides a new design paradigm for cooling configurations in motorized spindles from the perspective of machining reliability.
Systematic reviews (SR) are vital to health care, but have become complicated and time-consuming, due to the rapid expansion of evidence to be synthesised. Fortunately, many tasks of systematic reviews have the potential to be automated or may be assisted by automation. Recent advances in natural language processing, text mining and machine learning have produced new algorithms that can accurately mimic human endeavour in systematic review activity, faster and more cheaply. Automation tools need to be able to work together, to exchange data and results. Therefore, we initiated the International Collaboration for the Automation of Systematic Reviews (ICASR), to successfully put all the parts of automation of systematic review production together. The first meeting was held in Vienna in October 2015. We established a set of principles to enable tools to be developed and integrated into toolkits.This paper sets out the principles devised at that meeting, which cover the need for improvement in efficiency of SR tasks, automation across the spectrum of SR tasks, continuous improvement, adherence to high quality standards, flexibility of use and combining components, the need for a collaboration and varied skills, the desire for open source, shared code and evaluation, and a requirement for replicability through rigorous and open evaluation.Automation has a great potential to improve the speed of systematic reviews. Considerable work is already being done on many of the steps involved in a review. The ‘Vienna Principles’ set out in this paper aim to guide a more coordinated effort which will allow the integration of work by separate teams and build on the experience, code and evaluations done by the many teams working across the globe.
This review examines several recent books that deal with the impact of automation and robotics on the future of jobs. Most books in this genre predict that the current phase of digital technology will create massive job loss in an unprecedented way, that is, that this wave of automation is different from previous waves. Uniquely digital technology is said to automate professional occupations for the first time. This review critically examines these claims, puncturing some of the hyperbole about automation, robotics and Artificial Intelligence. The review argues for a more nuanced analysis of the politics of technology and provides some critical distance on Silicon Valley's futurist discourse. Only by insisting that futures are always social can public bodies, rather than autonomous markets and endogenous technologies, become central to disentangling, debating and delivering those futures.
Studying firm-level adjustments is important for understanding the economic effects of workplace automation. So far, emerging firm-level evidence is focused on robotics and the manufacturing sector. In this paper, we document that the adoption of automation technologies extends beyond manufacturing firms. We identify firm-level automation events and show that automating firms experience faster employment and revenue growth than do nonautomating firms. However, around automation events themselves, employment growth slows markedly. Notably, we find that these effects are similar for manufacturing and nonmanufacturing firms, suggesting that an increasing diffusion of automation technology has important consequences for firms and their workers.
Professional service jobs exist at the high end of the skill ladder; thus, some have assumed that highly trained professional workers are relatively immune to being replaced by automation. However, this assumption is a bit dubious because automation does not occur at the job level but rather at the task level, and some tasks within a professional job might be highly susceptible to automation disruption. This research builds on prior research by (1) empirically testing a model for automation of professional services and (2) developing a professional task-automation framework that shows how individual tasks within a given job can be enhanced or disrupted by automation in very different ways. Some tasks are augmented by automation and remain in the purview of professionally trained workers. Other tasks are deskilled by automation, allowing the tasks to be transferred to lower cost workers (who are aided by automation). Other tasks are moved directly to customers through self-service technologies, reducing or eliminating the need to interact with professionals or other workers. Finally, some professional tasks are centralized, which leverages professional workers’ distinctive expertise. Our framework shows precipitating conditions for each task-automation strategy and outlines logic for reconfiguring tasks within professional service systems.