Repetitive Operation Research Articles

KOALA: A Modular Dual-Arm Robot for Automated Precision Pruning Equipped with Cross-Functionality Sensor Fusion

Landscape maintenance is essential for ensuring agricultural productivity, promoting sustainable land use, and preserving soil and ecosystem health. Pruning is a labor-intensive task among landscaping applications that often involves repetitive pruning operations. To address these limitations, this paper presents the development of a dual-arm holonomic robot (called the KOALA robot) for precision plant pruning. The robot utilizes a cross-functionality sensor fusion approach, combining light detection and ranging (LiDAR) sensor and depth camera data for plant recognition and isolating the data points that require pruning. The You Only Look Once v8 (YOLOv8) object detection model powers the plant detection algorithm, achieving a 98.5% pruning plant detection rate and a 95% pruning accuracy using camera, depth sensor, and LiDAR data. The fused data allows the robot to identify the target boxwood plants, assess the density of the pruning area, and optimize the pruning path. The robot operates at a pruning speed of 10–50 cm/s and has a maximum robot travel speed of 0.5 m/s, with the ability to perform up to 4 h of pruning. The robot’s base can lift 400 kg, ensuring stability and versatility for multiple applications. The findings demonstrate the robot’s potential to significantly enhance efficiency, reduce labor requirements, and improve landscape maintenance precision compared to those of traditional manual methods. This paves the way for further advancements in automating repetitive tasks within landscaping applications.

Ferroelectric BaTiO3 Freestanding Sheets for an Ultra-High-Speed Light-Driven Actuator.

Light-driven actuators convert optical energy into physical motion. Organic materials, commonly used in light-driven actuators thus far, suffer from two limitations: slow repetitive operation and the requirement of two different light sources. Herein, we report a high-speed, light-driven actuator that can be operated by a single light source with low-energy density. We achieved this breakthrough by utilizing a freestanding epitaxial sheet of ferroelectric BaTiO3. One repetitive operation takes only 120 μs, which is 104 times faster than that of organic-based counterparts. The high-speed operation is derived from the light-induced nonthermal deformation provided by the excellent ferroelectricity (remnant polarization of 23 μC/cm2) and piezoelectricity (d33 of 600 pm/V) of the sheet. The displacement-to-length ratio is achieved to be 3.7% with a relatively low laser power density (10-200 mW/cm2) compared to previously reports (150-109 mW/cm2). Furthermore, the actuator was operable even in water, demonstrating its potential in various applications.

Numerical simulation and analysis of fatigue performance for the humeral stem

Background and objectiveFatigue failure of the humeral stem is a severe long-term failure after shoulder arthroplasty, causing harm to patients and resulting in complex revision surgeries. However, there are few studies on humeral stem fatigue testing, and corresponding testing standards have not been established. Therefore, this study aims to investigate the fatigue performance of the humeral stem by establishing an efficient numerical simulation method. MethodsMaterial properties are obtained by uniaxial tensile and fatigue tests. A parameterized static analysis program was written, and an automated fatigue numerical simulation platform was established using Abaqus, Fe-safe, and Isight in combination, enabling the establishment of a numerical simulation method for the fatigue performance of the humeral stem. ResultStandard testing conditions include an 8 mm diameter humeral stem, a 40-21B humeral head, an 8° tilt angle, and a 2 mm fillet radius. Further research found that the fatigue life of the humeral stem decreases with increasing patient weight, and patients should control their weight after surgery. ConclusionsThe established automated fatigue numerical simulation platform avoids repetitive operations and efficiently completes large-scale calculations, guiding preoperative humeral stem selection and testing.

Eight-Bar Elbow Joint Exoskeleton Mechanism

This paper deals with the design and kinematic analysis of a novel mechanism for the elbow joint of an upper-limb exoskeleton, with the aim of helping operators, in terms of effort and physical resistance, in carrying out heavy operations. In particular, the proposed eight-bar elbow joint exoskeleton mechanism consists of a motorized Watt I six-bar linkage and a suitable RP dyad, which connects mechanically the external parts of the human arm with the corresponding forearm by hook and loop velcro, thus helping their closing relative motion for lifting objects during repetitive and heavy operations. This relative motion is not a pure rotation, and thus the upper part of the exoskeleton is fastened to the arm, while the lower part is not rigidly connected to the forearm but through a prismatic pair that allows both rotation and sliding along the forearm axis. Instead, the human arm is sketched by means of a crossed four-bar linkage, which coupler link is considered as attached to the glyph of the prismatic pair, which is fastened to the forearm. Therefore, the kinematic analysis of the whole ten-bar mechanism, which is obtained by joining the Watt I six-bar linkage and the RP dyad to the crossed four-bar linkage, is formulated to investigate the main kinematic performance and for design purposes. The proposed algorithm has given several numerical and graphical results. Finally, a double-parallelogram linkage, as in the particular case of the Watt I six-bar linkage, was considered in combination with the RP dyad and the crossed four-bar linkage by giving a first mechanical design and a 3D-printed prototype.

An unsupervised embedding method based on streaming videos for process monitoring in repetitive production systems

Advanced production systems, such as multi-step assembly processes, predominantly comprise of repetitive operations. The repetitive manual or human–robot integrated production operations call for new real-time process management technologies such as the increasing use of sensors and the development of system intelligence. Conventional process monitoring and management methods, which are often labor-intensive, fall short in providing immediate and actionable insights. To tackle this limitation, we develop an unsupervised embedding method to automatically delineate the process into different stages and predict real-time progress information. We propose a Contrastive Variational Autoencoder as a feature extractor to adeptly embed repetitive processes into a Gaussian Mixture Model. Based on the extracted features, we propose an adaptive change-point detection and an Iterative Dynamic Time Wrapping algorithm to identify and segment multiple standardized process stages automatically. Theoretically, we establish the asymptotic optimality of the detected change-points associated with the given precision of image and feature extractors, ensuring the high-quality process stage separation and labeling. The proposed method autonomously extracts essential features encapsulating progress information from a limited set of unlabelled process videos. Through four diverse case studies including production of an actual aircraft spoiler, our method exhibits very promising performance. Specifically, it achieves an average of 98.14% accuracy in predicting production progress and 0.9202 area under the curve in predicting progress deviation across three distinct production environments. The proposed process monitoring method in repetitive production systems has the potential to significantly improve productivity, promote standardization of repetitive operations, and predict production deviations.

Horizontal homing laser for high repetitive inertial fusion

For a commercial laser inertial fusion energy reactor, a highly repetitive operation in which many fuel pellets must be illuminated by laser spots. One of the most efficient ways to achieve this is to control the laser pointing by following fluctuations of the target position. The paper shows the precise control of laser pointing in the horizontal direction with a repetition rate of 10 Hz. Free-falling test pellets of 1 mm in diameter have been illuminated by a laser of 1.6 mm in diameter with cancellation of horizontal fluctuation over 4 mm. The difference in centroids between the laser spot and the illuminated test pellets is 86 μm (standard deviation). This corresponds to a 92% engagement within a 0.15 mm difference, which is a condition for successful nuclear fusions at the Hamamatsu facility. This is the proof-of-principle demonstration of the target-supply tracking and homing laser at a repetition rate of 10 Hz for the actualization of a commercial reactor.

Parallel Disassembly Sequence Planning Using a Discrete Whale Optimization Algorithm for Equipment Maintenance in Hydropower Station

In a hydropower station, equipment needs maintenance to ensure safe, stable, and efficient operation. And the essence of equipment maintenance is a disassembly sequence planning problem. However, the complexity arises from the vast number of components in a hydropower station, leading to a significant proliferation of potential combinations, which poses considerable challenges when devising optimal solutions for the maintenance process. Consequently, to improve maintenance efficiency and decrease maintenance time, a discrete whale optimization algorithm (DWOA) is proposed in this paper to achieve excellent parallel disassembly sequence planning (PDSP). To begin, composite nodes are added into the constraint relationship graph based on the characteristics of hydropower equipment, and disassembly time is chosen as the optimization objective. Subsequently, the DWOA is proposed to solve the PDSP problem by integrating the precedence preservative crossover mechanism, heuristic mutation mechanism, and repetitive pairwise exchange operator. Meanwhile, the hierarchical combination method is used to swiftly generate the initial population. To verify the viability of the proposed algorithm, a classic genetic algorithm (GA), simplified teaching–learning-based optimization (STLBO), and self-adaptive simplified swarm optimization (SSO) were employed for comparison in three maintenance projects. The experimental results and comparative analysis revealed that the proposed PDSP with DWOA achieved a reduced disassembly time of only 19.96 min in Experiment 3. Additionally, the values for standard deviation, average disassembly time, and the rate of minimum disassembly time were 0.3282, 20.31, and 71%, respectively, demonstrating its superior performance compared to the other algorithms. Furthermore, the method proposed in this paper addresses the inefficiencies in dismantling processes in hydropower stations and enhances visual representation for maintenance training by integrating Unity3D with intelligent algorithms.

Design and operation of a repetitive compact torus injector for the EAST tokamak

The design, implementation, and initial operation results of a Compact Torus Injector (CTI) capable of repetitive operation are presented. This CTI is designed to reliably and efficiently inject high-density, self-organized plasmoids into the EAST tokamak for central fueling. To enable repetitive operation, a high-voltage, high-power pulse power supply switched by ignitrons has been installed to trigger Compact Torus (CT) discharges. Through careful design and optimization, repetitive operation at a rate of 2 Hz for 10 CTs has been achieved during bench tests. This CTI will be installed on the EAST tokamak in the future. Various diagnostic devices have been developed to monitor the quality of the CT plasma during repetitive operation. The plasma current, plasma line-averaged density, and edge magnetic field exhibit high reproducibility, whereas the reproducibility of plasma radiation intensity is comparatively lower. The maximum plasma line-averaged density, velocity, and mass of the single CT plasma at the injector exit are 3.04×1021 m−3, 180 km/s, and 226.7μg, respectively, suitable for penetrating into a toroidal field up to 0.91 T. The successful operation of the repetitive CTI advances engineering research for the central fueling of future steady-state fusion reactors.

Robust zero-watermarking algorithm for multi-medical images based on FFST-Schur and Tent mapping

Aiming at issues such as the difficulty of centralized protection of multiple medical images and the high storage cost caused by repetitive operations, we proposed a robust zero-watermarking algorithm for multiple medical images based on fast finite Shearlet transform (FFST)-Schur and Tent mapping. First, multiple medical images were normalized, and the processed images were fused using a gray-weighted average fusion method for image fusion. Second, an FFST is applied to the fused image to divide the low-frequency sub-bands into blocks of the same size and perform Schur decomposition, which generates a feature image using the magnitude between the largest eigenvalue of each block and the overall average value. Finally, an image encryption algorithm based on Tent mapping is proposed to encrypt the logo image, and the encrypted image and feature image are then subjected to an exclusive OR operation to produce a zero-watermarking image. Experimental results demonstrate that the proposed approach effectively safeguards against standard image processing and geometric and combinatorial attacks. The average performance of the anti-attack algorithm was improved by approximately 3.2 % compared to similar algorithms, indicating the superior robustness of the proposed algorithm.

OEM: An operation-aware event matching algorithm for content-based Pub/Sub systems

Due to the continuous expansion of the user base and the heightened demands for quality of service (QoS), real-time data distribution has encountered significant challenges in event matching within large-scale content-based publish/subscribe (CPS) systems. Many efficient algorithms have been proposed to enhance the matching performance. However, most existing algorithms scarcely consider operation characteristics, which often leads to performance degradation due to a multitude of repetitive operations, such as comparisons, additions, and assignments. In this paper, we propose an Operation-aware Event Matching algorithm called OEM, which takes into account the efficiency of CPU operations, SIMD instructions, and multi-thread architecture. First, we introduce a bitset-based subscription cache mechanism (SCM), which enables the efficient execution of bitwise OR operations during the matching process. Furthermore, we propose two optimizations, namely an optimal skewness-aware cell grouping (OSCG) strategy and an attribute-based clustering mechanism (ACM), to enhance the efficiency of OEM. These optimizations effectively address the challenges posed by data skewness, low cache efficiency, and high dimensionality. In addition, we establish a performance analysis model that characterizes the trade-off between memory consumption and performance improvement. We conducted extensive experiments to evaluate the performance of OEM. Compared to six state-of-the-art algorithms, namely REIN, Ada-REIN, TAMA, OpIndex, PEM and fgPEM, OEM achieves an average reduction in matching time by 40.4×, 38.5×, 12.0×, 33.1×, 27.8×, and 25.5×, respectively.

Repetitive high-power discharge operation of a micro-laser triggered gas switch.

A low-energy, diode-pumped, solid-state passively Q-switched laser operating at a wavelength of 1535nm has been demonstrated to trigger gas switches. This technology has the potential to bring significant improvements to the operation and reliability of compact pulsed power drivers. Compact pulsed power drivers are often required to operate for hundreds to thousands of shots without maintenance; thus, the reliable operation of the laser triggered switch is significant for the adoption of this technology. A primary limiter of spark gap lifetime is electrode erosion. In the case of a laser triggered switch where the laser is focused on one electrode, an additional source of erosion is the direct ablation of the electrode material by the laser. The micro-laser used in the previous experiments is very low energy, but over thousands of shots, it does damage directly to the target electrode. This damage could potentially interact with the erosion due to the current discharge and impact the operation of the switch. The focus of this paper is to describe experiments undertaken to assess this damage and quantify its effect on switch operation over many discharges at current and energy levels relevant to compact pulsed power systems.

Visual Servo Algorithm Of Robot Arm For Pick And Place Application

The robot arm is a device consisting of a moving chain of links connected by joints. Electrical motors are frequently used to move each robot arm joint. An end-effector that can move freely in space is usually attached to one end of the robot platform, which is fixed. Robot arms can do repetitive operations at rates and precision far exceeding human operators. Nowadays, robot arm systems are widely used worldwide to increase the quality and efficiency of the manufacturing process in the industry. Typical applications of the robot arm system are assembly, painting, welding, pick and place operation, and others. Besides, many industries employ robot arms for various jobs such as selecting and putting, painting, and material handling. However, one of the most challenging issues in completing these jobs is determining the target location of the robot arm's end-effector. There are two different methods for analyzing the robot arm's movement: forward and inverse kinematic analysis. Based on the visual servo algorithm, this study uses inverse kinematics to execute the pick and place operation. First, an object recognition algorithm is implemented to identify the object to be grasped. Then, an algorithm to avoid any obstacles is done. The study's findings show that good system performance has been obtained in all three algorithms: first, object recognition algorithm, second, obstacle avoidance algorithm, and lastly, visual servobased pick and place operation. Thus, it can be concluded that the robot arm's visual servo algorithm is suitable for pick-and-place applications.

Optimal Energy Management Strategy for Repeat Path Operating Fuel Cell Hybrid Tram

This study focuses on minimizing fuel consumption of a fuel cell hybrid tram, operated with electric power from both the fuel cell stack and the energy storage system, by optimizing energy distribution between distinct energy sources. In the field of fuel cell hybrid system application, dealing with real-world optimal control implementation becomes more important. Some ‘online control’ strategies optimize energy management by measuring the current battery’s state and planning for future cycles. However, its dependence on stochastic processes remains a limitation for adapting ‘online control’ even when driving in the same way. In order to optimize energy distribution robustly during the tram’s repetitive cycle operation, we develop a practical control map with a fuel cell hybrid tram simulation model and conduct energy distribution. The control map is based on a mathematical equivalent consumption minimization strategy (ECMS) equation reflecting the characteristics of the fuel cell stack and electric cells. The comparison of fuel consumption with another practical control strategy optimized for a specific railway cycle shows that the suggested map-based optimal control achieves a reduction in fuel consumption while satisfying a boundary condition.

A High-Power Relativistic Ka-Band Millimeter-Wave Coaxial Transit Time Oscillator With Stable Repetitive Operation at Low Guiding Magnetic Field

A High-Power Relativistic <i>Ka</i>-Band Millimeter-Wave Coaxial Transit Time Oscillator With Stable Repetitive Operation at Low Guiding Magnetic Field

Artificial Intelligence at Work: Transforming Industries and Redefining the Workforce Landscape

Global industry transformation is occurring due to the adoption of Artificial Intelligence (AI) in the workplace, bringing a new era of productivity, creativity, and revolutionary change. AI technologies are evolving into essential tools that improve workflows, automate repetitive operations, and enhance human capabilities in various industries, including finance, human resources, and manufacturing. AI-driven robotic systems in manufacturing improve speed and precision, boosting output and economy of scale. Artificial intelligence (AI) algorithms that analyze large datasets and provide insights into market patterns, risk, and investing strategies benefit the financial sector. AI-powered chatbots are revolutionizing customer care by offering prompt, customized support. This article explores the multifaceted impact of Artificial Intelligence (AI) on the contemporary workforce, briefly examining its transformative influence on various industries.

Smart pH Sensing: A Self-Sensitivity Programmable Platform with Multi-Functional Charge-Trap-Flash ISFET Technology

This study presents a novel pH sensor platform utilizing charge-trap-flash-type metal oxide semiconductor field-effect transistors (CTF-type MOSFETs) for enhanced sensitivity and self-amplification. Traditional ion-sensitive field-effect transistors (ISFETs) face challenges in commercialization due to low sensitivity at room temperature, known as the Nernst limit. To overcome this limitation, we explore resistive coupling effects and CTF-type MOSFETs, allowing for flexible adjustment of the amplification ratio. The platform adopts a unique approach, employing CTF-type MOSFETs as both transducers and resistors, ensuring efficient sensitivity control. An extended-gate (EG) structure is implemented to enhance cost-effectiveness and increase the overall lifespan of the sensor platform by preventing direct contact between analytes and the transducer. The proposed pH sensor platform demonstrates effective sensitivity control at various amplification ratios. Stability and reliability are validated by investigating non-ideal effects, including hysteresis and drift. The CTF-type MOSFETs’ electrical characteristics, energy band diagrams, and programmable resistance modulation are thoroughly characterized. The results showcase remarkable stability, even under prolonged and repetitive operations, indicating the platform’s potential for accurate pH detection in diverse environments. This study contributes a robust and stable alternative for detecting micro-potential analytes, with promising applications in health management and point-of-care settings.

A Parallel Privacy-Preserving k-Means Clustering Algorithm for Encrypted Databases in Cloud Computing

With the development of cloud computing, interest in database outsourcing has recently increased. However, when the database is outsourced, there is a problem in that the information of the data owner is exposed to internal and external attackers. Therefore, in this paper, we propose decimal-based encryption operation protocols that support privacy preservation. The proposed protocols improve the operational efficiency compared with binary-based encryption operation protocols by eliminating the need for repetitive operations based on bit length. In addition, we propose a privacy-preserving k-means clustering algorithm using decimal-based encryption operation protocols. The proposed k-means clustering algorithm utilizes efficient decimal-based protocols that enhance the efficiency of the encryption operations. To provide high query processing performance, we also propose a parallel k-means clustering algorithm that supports thread-based parallel processing by using a random value pool. Meanwhile, a security analysis of both the proposed k-means clustering algorithm and the proposed parallel algorithm was performed to prove their data protection, query protection, and access pattern protection capabilities. Through our performance analysis, the proposed k-means clustering algorithm shows about 10~13 times better performance compared with the existing algorithms.

A real‐time energy management control strategy of hybrid energy system for a pure electric loader

SummaryTo enhance the efficiency and prolong the battery life of hybrid energy systems equipped with battery and supercapacitor for a pure electric loader, this paper proposes a real‐time energy management strategy based on model predictive control (MPC). First, the working characteristics of the loader and the topology structure of the hybrid energy are analyzed. Second, considering the repetitive operation model of electric loaders, a neural network algorithm based on long short‐term memory is used to predict power and a dynamic programming algorithm is used as the rolling optimization solution for MPC strategy. Finally, the MPC is verified using the simulation in MATLAB/Simulink. The results indicate that compared with other strategies, the MPC strategy can improve hybrid energy efficiency and reduce the maximum battery current fluctuations. The proposed method demonstrates the feasibility and effectiveness of the hybrid energy system.

Risk and related factors of elbow musculoskeletal diseases: A nationwide cross-sectional survey in China.

Although studies have shown that Work-related Musculoskeletal Disorders (WMSDs) are common and continue to be a main source of disability and work time loss, there are few reports on elbow WMSDs. The aim of this study was to explore the prevalence and associated factors of elbow WMSDs. The valid questionnaires of 57501 workers from 15 different industries nationwide were collected and the Chi-square test and logistic-regression-analysis were applied to reveal the prevalence and risk factors of elbow. The findings indicated that prevalence of elbow WMSDs among workers was 7.3%. The prevalence of elbow WMSDs in toy manufacturing was 21.3%, which significantly higher than that in other industries (P<0.05). Logistic regression analysis showed that aged 40 and above, married, very poor health, left-handed, lifting weights (more than 20 kg each time) , work requiring upper limb or hand force, work in an uncomfortable position, repetitive operations within one minute, using vibrating tools, work involves cold, cool winds or temperature changes, work being completed in the same workshop, work being done outdoors, frequent deal with customers , two shifts, often work overtime, staff shortage, often work for colleagues were the risk factors of elbow WMSDs.The higer education level and monthly income, and enough rest time were the protective factors of elbow WMSDs. The toy manufacturing is a high-risk industry for elbow WMSDs. The publicity and education of ergonomics knowledge should be strengthened, and the workers' ergonomics awareness should be improved to reduce the impact of WMSDs.

CVRanalysis: a free software for analyzing cardiac, vascular and respiratory interactions.

Introduction: Simultaneous beat-to-beat R-R intervals, blood pressure and respiration signals are routinely analyzed for the evaluation of autonomic cardiovascular and cardiorespiratory regulations for research or clinical purposes. The more recognized analyses are i) heart rate variability and cardiac coherence, which provides an evaluation of autonomic nervous system activity and more particularly parasympathetic and sympathetic autonomic arms; ii) blood pressure variability which is mainly linked to sympathetic modulation and myogenic vascular function; iii) baroreflex sensitivity; iv) time-frequency analyses to identify fast modifications of autonomic activity; and more recently, v) time and frequency domain Granger causality analyses were introduced for assessing bidirectional causal links between each considered signal, thus allowing the scrutiny of many physiological regulatory mechanisms. Methods: These analyses are commonly applied in various populations and conditions, including mortality and morbidity predictions, cardiac and respiratory rehabilitation, training and overtraining, diabetes, autonomic status of newborns, anesthesia, or neurophysiological studies. Results: We developed CVRanalysis, a free software to analyze cardiac, vascular and respiratory interactions, with a friendly graphical interface designed to meet laboratory requirements. The main strength of CVRanalysis resides in its wide scope of applications: recordings can arise from beat-to-beat preprocessed data (R-R, systolic, diastolic and mean blood pressure, respiration) or raw data (ECG, continuous blood pressure and respiratory waveforms). It has several tools for beat detection and correction, as well as setting of specific areas or events. In addition to the wide possibility of analyses cited above, the interface is also designed for easy study of large cohorts, including batch mode signal processing to avoid running repetitive operations. Results are displayed as figures or saved in text files that are easily employable in statistical softwares. Conclusion: CVRanalysis is freely available at this website: anslabtools.univ-st-etienne.fr. It has been developed using MATLAB® and works on Windows 64-bit operating systems. The software is a standalone application avoiding to have programming skills and to install MATLAB. The aims of this paper area are to describe the physiological, research and clinical contexts of CVRanalysis, to introduce the methodological approach of the different techniques used, and to show an overview of the software with the aid of screenshots.