Reduce Labor Costs Research Articles

Cost effective maintenance strategy for centrifugal pumps using reliability centred maintenance

The aim of this work is to obtain a maintenance strategy that is cost effective for centrifugal pumps using reliability centred maintenance (RCM). The components of the centrifugal pump used for the study are the shaft, impeller, bearing, mechanical seal, coupling, shaft sleeve, wear-ring casing, suction flange and discharge flange. The motor that drives the pump was also considered. Two basic tools of the RCM strategy were applied. These are the failure modes, effects and criticality analysis (FMECA) and logic tree analysis (LTA). Failure data for a period of 10 years about a centrifugal pump was collected and the FMECA was applied to determine the failure modes, the effects of the failure, the criticality of the components, the mean time between failure (MTBF), the failure rate and the mean time to repair (MTTR). The risk priority numbers of all the components were estimated. The LTA was applied to come up with a new maintenance strategy for each of the components of the pump. The maintenance cost consisting of spare part cost and labour cost for the pump components were estimated for the current maintenance practice as well as the new or proposed maintenance practice. The most critical components observed were the motor, shaft, impeller and the mechanical seal. The motor has the highest MTTR value (58 hrs) while the mechanical seal has the highest failure rate (8.06E-04 per hr). The proposed maintenance strategy leads to reduction in spare part cost in a number of components, with the largest reduction of 19.88% occurring in mechanical seal. The proposed strategy also leads to reduction in labour cost in motor, shaft, impeller and the mechanical seal with the values 33%, 25%, 50% and 25% respectively. The methodology could be applied to other devices.

Plucking Flowers using Soft Robot

Abstract: Soft robotics is a subfield of robotics that concerns the design, control, and fabrication of robots composed of complaint materials, instead of rigid links. In contrast to the rigid-bodied robots built from metals, ceramics, and hard plastics, the compliance of soft robots can improve their safety when working in close contact with humans. The main objective of this project is to pluck flowers using a soft robot. The proposed system is designed to provide gentle manipulation of flowers in a horticultural setting. The soft robot is composed of flexible and deformable materials, such as silicone or elastomer, and is designed to mimic the motion and compliance of human fingers. The system is implemented and tested in a real-world scenario, and the results show that it can effectively pluck flowers without causing damage or injury to the plant. The proposed approach has potential applications in the floriculture industry, where the system can improve efficiency and reduce labour costs, while also minimizing damage to the flowers

Automatic Fruit Plucking Machine Using Deep Learning

Abstract: The Automatic Fruit Plucking Machine project aims to develop an innovative automated system to revolutionize fruit harvesting. By integrating computer vision, robotic arm manipulation, and YOLO deep learning algorithms, the machine can accurately identify and pluck ripe fruits. It reduces labor costs, enhances harvest quality, and improves productivity. The machine's core is a trained machine learning algorithm that combines image processing and deep learning models for real-time fruit detection. Through iterative testing and refinement, the machine's performance continually improves. This project offers a sustainable and efficient solution for fruit producers to meet market demands and reduce manual labor reliance

An improved YOLO algorithm for detecting flowers and fruits on strawberry seedlings

Accurately identifying the flowers and fruits of strawberry seedlings in the greenhouse is the key to automated flower and fruit thinning, which can improve efficiency and reduce labour costs in the cultivation. To address the challenges resulting from the small size, similar colour, and overlapping occlusion of strawberry seedling flowers and fruits, this paper proposes a real-time recognition algorithm (Improved YOLO) for accurately identifying them. Firstly, a Swin Transformer prediction head on the high-resolution feature map of YOLO v7 was constructed to better utilise spatial location information to enhance the detection of small target flowers and fruits, and improve the model's spatial interaction and feature extraction ability in scenes with similar colours and overlapping occlusions. Secondly, the GS-ELAN Optimisation Module for neck of network by GSConv was constructed to suppress shallow noise interference from the high-resolution prediction head and mitigate the increase of parameters resulting from high-resolution prediction heads. The experimental results showed that the Precision(P), Recall(R), and mean Average Precision (mAP) of Improved YOLO are 92.6%, 89.6%, and 92.1%. In the meantime, the Improved YOLO algorithm achieves a frame rate of 45 f/s, satisfying the real-time detection requirements. It is 3.2%, 2.7%, and 4.6% higher than the precision, recall, and mAP of YOLOv7, respectively. The accuracy of this model for detecting flowers and fruits was 93.9% and 91.3%, the recall was 93% and 86.3%, and the average precision was 94.7% and 89.5%, respectively. The Improved YOLO algorithm has a high level of robustness and real-time detection performance, allowing it to quickly and accurately identify the flowers and fruits of strawberry seedlings and provides effective support for the automated management of flower and fruit thinning of strawberry seedlings in greenhouse environments.

Finding the right one: Decision support for selecting cost-efficient order picking solutions

Enabled via recent technological advances coupled with the advent of new systems providers and decreased price points, automated and robotized order-picking solutions (e.g., pick-assisting autonomous mobile robots) have evolved as a surging market. Such innovative picking technologies aim to reduce labor costs, use available space more efficiently, and increase throughput rates. As implementation projects and the variety of solutions rise, managers decide which ones to select for their specific warehouse and products. However, comprehensive decision models for this strategic problem are lacking in the pertinent literature. We propose a mathematical optimization model for the novel problem that selects and sizes order-picking solutions and assigns them products and warehouse spaces. Expert interviews are used to identify the comprehensive decision-relevant costs and constraints. Specifically, we minimize setup, module, labor, and error costs while adhering to characteristics related to the area (e.g., available space), technology (e.g., throughput, handling capabilities of certain products), and product (e.g., physical dimensions). We conduct a case study and complement our findings with numerical experiments. We find significant cost reduction potential of up to 57% by selecting a mix of different order-picking solutions. Further analyses highlight the need to retain human workers and to account for maximum labor capacity.

Life cycle management of the ventilation system of a construction object

Introduction. In the article, the author considers a mechanical ventilation system as a complex technical system that can be effectively controlled based on a model of its life cycle. Like any engineering system (or product, product, project), a ventilation system has its own life cycle, consisting of a set of successive stages. Each stage is characterized by its own types of work and final results that require management decisions.Materials and Methods. The paper uses the method of analyzing the life cycle of complex technical systems, methods of collecting and processing statistical data, as well as methods of systematic and comparative analysis, generalization of scientific and practical results.Results. In the course of the conducted research, the author applied the "life cycle management" approach to ventilation systems and achieved the goal of developing a model of their life cycle, which includes all stages of system development from conception to disposal. Possible ways of managing the life cycle of ventilation systems from the perspective of continuous interconnection of processes are determined.Discussion and Conclusion. Effective life cycle management of a mechanical ventilation system can be implemented through the development of a software product capable of modeling processes and system elements even at the first stages of the life cycle. The software product should eliminate the existing problem of inconsistencies at different stages of work, store information about the object and provide access to it for each participant in the process. Solving the problem of creating a software product will ensure effective management of the entire life cycle of the ventilation system, reduce labor costs and inconsistencies and ensure compliance with modern requirements for operational reliability and energy efficiency of a vital engineering system.

Computing the characteristics of defects in wooden structures using image processing and CNN

Wood, a time-honored construction material prized for its exceptional properties, has been in use for millennia. Its enduring popularity is attributed to its remarkable strength, aesthetic appeal, and favorable environmental footprint. However, wooden structures are susceptible to various defects and imperfections that pose threats to their structural integrity, durability, and safety. These issues encompass knots, cracks, warping, twisting, decay, insect infestations, and more, all of which, if left unaddressed, can culminate in structural failures. Thus, a comprehensive strategy involving inspection, maintenance, and remediation is indispensable for safeguarding wooden structures. Traditional manual inspections, while effective, are characterized by their resource-intensive nature, entailing significant time and cost investments. This study presents a pioneering approach that leverages Convolutional Neural Networks (CNNs) and Image Processing techniques to revolutionize the assessment of damage in wooden structures using digital imagery. Initially, CNNs are employed to categorize images into three fundamental classes: cracks, knots, and undamaged sections. Subsequently, Image Processing techniques are harnessed to compute precise characteristics of these defects, including parameters such as crack length, width, angle, and the extent of the defective area within knots. The Inception-ResNet-V2 pre-trained model is utilized, fine-tuned and validated with a robust dataset comprising 9000 wooden defect images, evenly distributed across the three aforementioned categories. A prudent division allocates 70% of the dataset for model training, with the remaining 30% reserved for validation. Following successful training, the model demonstrates an impressive overall accuracy of 92% when classifying an independent test set comprising 100 new images. To illustrate the model's performance, two images from each damage category are selected and tested to compute the characteristics of the defects. The quantification error for crack angle is only 0.15%, while it is 0.99% for crack length, and 2% for crack width, demonstrating the high performance of the model. The practical implications of this work are profound. By automating defect assessment in wooden structures, our approach offers significant advantages to industry professionals. It expedites inspections, reduces labor costs, and enhances the accuracy of defect quantification.

Effect of labour costs on wood harvesting costs and timber provision

Efficient forest operations are essential for forest enterprises, who provide wood and numerous ecosystem services for the society. Important factors influencing the efficiency of forest operations, and thus the harvesting costs, are the level of mechanization, the harvesting method applied, the forest road network, and the training of the labourers. The cost of labour, i.e. salaries, is another important driver of harvesting costs. However, its effect on and relative importance for overall harvesting costs is poorly described in the scientific literature. Thus, this study aims to analyse the influence of labour costs in more detail, especially on the country-wide wood harvesting potentials. In addition, we aimed to cross-compare the effect of labour costs with the effect of switching to a more efficient harvesting method. For this purpose, we calculated timber harvesting costs with varying salaries for all sample plots of the Swiss National Forest Inventory (NFI) for both, the currently applied harvesting method recorded in the NFI, and the potential best suitable harvesting method. A 1% change in labour costs affects harvesting costs by 0.33–0.77%, depending on the harvesting method applied. The influence is larger for systems that involve a large share of motor-manual work and for cable-based methods. Changing labour costs by ± 30% affects the number of plots for which timber harvesting is economically feasible, by 5 to 15 percent points. The effect of switching from the current to the best suitable harvesting method is comparable to that of reducing labour costs by 15–30%. These results indicate that the efficiency of wood harvesting can be increased with further mechanization and does not require cutting salaries of forestry personnel. In that, our results may inform forestry planning and policy making at regional to national level.

Visual Servo Kinematic Control of Delta Robot using YOLOv5 Algorithm

Industrial delta robots with motion-and vision-base controllers, providing high-precision and fast/flexible motion, normally come with high cost and complex software. Low-cost delta robots with vision solution could be beneficial to vast industries to increase productivity and to reduce labor cost. A delta robot using a low-cost motion controller and an open-source vision system is developed to accomplish real-time visual servoing with high motion accuracy. In the low-cost motion controller, three parallel links’ upper-arm angles, computed from inverse kinematics for a given desired target position by a high-level computer, are used as reference position commands for three AC-motor drives. A low-level Arduino microcontroller is employed to convert these links’ angles to high-frequency pulses and on-off signals for synchronously controlling three motor angles and direction. Experimental results of a point-to-point motion tracking exhibit high-precision repeatability. Synchronous pulse generation from Arduino microcontroller and structural misalignments of parallel links are major challenges for achieving high motion accuracy. For the vision-based system, the YOLOv5 algorithm is implemented along with a Python GUI Application. Then, the visual-servo performance is evaluated on localization accuracy and recognition rate of 3-color objects. However, a partial object occlusion can reduce the visual classification rate. A sorting task of 4-category medicine boxes demonstrate a high-speed pick-and-place operation using the low-cost visual-servo system of this delta robot. Therefore, integration of low-cost visual servoing with this delta robot can revolutionize various industries, like automobile, pharmaceutical, and food sectors, in separating, sorting and packing applications.

Benefit evaluation analysis of new energy power consumption information collection with basic steady-state harmonic characteristics

Abstract In this paper, firstly, the load monitoring characteristics of new energy power consumption are studied and analyzed in terms of both steady-state and transient characteristics, and suitable characteristic values are selected for different types as the basis of load decomposition. A comparative analysis of various types of load imprints is conducted to highlight the advantages of steady-state harmonic features as load monitoring feature data. Then, an evaluation model of the demand response potential of electricity information collection is established based on a multi-objective optimization algorithm, and technical and economic analysis of electricity information collection is carried out to evaluate the response potential of power companies collecting customer data. Finally, the situation and benefits of collecting electricity consumption information for new energy power are analyzed. The daily maximum load moments of typical users are all cut by more than 10%, the peak load portion cut by more than 97% is also more obvious, and the maximum moments of valley filling response are all more than 11%. D Power saved a total of 3.759 billion yuan in labor costs by reducing labor costs by 400 million yuan after adopting electricity consumption information collection. The indices of excellent and qualified management benefit and social benefit evaluation of electricity consumption information collection are above 0.8. Overall, the benefits of electricity consumption information collection for new energy power with basic steady-state harmonic characteristics are significant and effective.

Size matters: Microherbivores make a big impact in coral aquaculture

Reef restoration activities and the ornamental trade are increasing the demand for sexually propagated corals. One challenge faced in scaling up the aquaculture production of corals is high mortality as a result of fouling organisms overgrowing coral spat, with manual removal of algae and other fouling organisms being costly and time consuming. Here we test the use of microherbivore grazers as a potential biocontrol method for fouling in coral aquaculture and compare their effectiveness to manual cleaning by an aquarist. Recruits of six coral species (Acropora millepora, Acropora kenti, Goniastrea retiformis, Porites lobata, Dipsastraea speciosa and Lobophyllia corymbosa) were reared for 112 days with aquarist cleaning, or co-cultured with gastropods (Calthalotia strigata or Turbo haynesi), sea urchins (Tripneustes gratilla or Echinomentra mathaei), the hermit crab Clibanarius cf. taeniatus or under a control treatment with no grazers. Corals grown in the aquarist cleaning treatment displayed high survival and growth, though similar responses were observed for most coral species grown with C. strigata or T. gratilla, likely due to minimal damage via overgrazing and the promotion of relatively short turf algal communities in these treatments. However, effort required, measured as average cleaning time, was 2–3 times greater in the aquarist treatment compared to C. strigata or T. gratilla treatments. Survival of coral recruits housed with C. cf. taeniatus, E. mathaei or T. haynesi were variable, likely due to the dominance of long, filamentous turf algae in tanks with E. mathaei, and physical disturbance to recruits by C. cf. taeniatus and T. haynesi. Our results demonstrate microherbivores have potential for application in aquaculture to promote production, while also reducing labour costs.

Magpie cleaning device for cleaning the pre-chamber of the pumping station from sediment

The purpose of the work is to develop, test and evaluate the efficiency of the water treatment device at the water intake of the pumping station. The article provides an analysis of the existing designs of water intakes at pumping stations of irrigation systems of the Saratov region. A brief description of the device and operation of the developed conveyor-type water treatment device mounted on the water intake of the pumping station No. 4 of the Engels OS is given. Studies have established the species composition and mass of sediment entering the grid during the irrigation period. When filling the channel (May 20), a large mass of sediment in the form of branches, stems and leaves of plants was observed in the ante-chamber. At the first watering, the mass of the removed dry sediment is small ? 2-3 kg. With the second watering, the weight of the sediment increases to 14 kg. The maximum mass of sediment was removed from the pre-chamber during the third and fourth watering (July, August), and reached 17, 21 and 24 kg. In addition to branches, stems and leaves, a large amount of algae, mud, duckweed and a small amount of household garbage were observed in it. The cleaning time of the ante-chamber in front of the conveyor varied from 10 to 60 minutes. The developed water treatment device showed reliable operation, reduced labor costs for cleaning the ante-chamber and ensured the uninterrupted operation of the pumping station during the irrigation period. Proposals for further improvement of the water purification device have been developed.

The Effect of Express Delivery Robot Autonomy on Customer Responsibility Attribution in a Service Failure Scenario

In order to improve the efficiency of e-commerce logistics services and reduce labor costs, many cities have introduced express delivery robots to provide express delivery services. Robotic service failures occur from time to time due to the complexity of the service environment, immature technology, and other constraints. This study investigates the effect of service robot autonomy on customer responsibility attribution using a 2 (robot autonomy: high vs. low) x 2 (customer participation level: high vs. low) between-groups experimental approach to investigate the mediating role of controllability and the moderating role of customer participation using the scenario of service failure of a express delivery robot. The experimental results show that robot autonomy increases customers' attribution of responsibility to the robot in service failure scenarios, and controllability partially mediates this effect, but the moderating effect of customer participation is not demonstrated. The experimental results provide implications for the design and use of service robots.

Research on Path Planning of Handling Robot in Chinese Medicine Factory

The path planning of handling robot in Chinese medicine factory is the key problem to improve production efficiency and reduce labor cost. In view of the complex layout characteristics in the handling process of Chinese medicine plant, this paper studies and improves the existing path planning algorithm. Firstly, through the analysis of the production environment of the Chinese medicine factory, a suitable map model was established. Then, combined with the operation environment of the handling robot in the Chinese medicine factory, the handling robot has A large number of nodes and many kinds of paths. The improved A* algorithm is used to plan the moving path of the robot. The effectiveness and practicability of the proposed path planning algorithm are verified by Matlab simulation test. Finally, the path conflict problem is solved.

Research on the Cleaning Method of Unmanned Sweeper Based on Target Distribution Situation Analysis

Replacing traditional manual sweeping with unmanned sweepers in closed parks can not only greatly reduce labor costs, but also improve sweeping efficiency. Efficient path planning is the key technology for unmanned sweepers to complete the sweeping task. Existing unmanned sweepers are often based on fixed path sweeping or completely traversing the sweeping mode, this mode does not have the environmental adaptability, in the actual sweeping is often high energy cost, and sweeping is not complete. In this paper, an environment-adaptive sweeping path planning method is proposed to improve the sweeping intelligence and environmental adaptability of unmanned sweepers, reduce the energy consumption of sweeping and improve the efficiency of sweeping. Specifically, in this paper, we first use YOLOv5 to complete the accurate identification of individual garbage and obstacles in the road, and then work with LIDAR and Gaussian Mixture Model(GMM) to remove redundant targets. We also propose a Permutation Entropy(PE) value-based discrimination method to complete the target distribution posture analysis of each complex garbage pile. Finally, the traditional path planning problem is transformed into a combinatorial optimization problem of garbage areas, and a sweeping path accurate method based on Simulated Annealing(SA) algorithm is proposed. Through comprehensive theoretical analysis and simulation study, the optimality and effectiveness of the proposed method are proved by comparing A star and Coverage Path Planning(CPP) algorithms in a variety of experimental scenarios.

Preference for young workers in mid-career recruiting using online ads for sales jobs: Evidence from Japan

This study uses an original dataset of online mid-career job ads for full-time sales jobs collected from July 2018 to December 2019 to examine the use of explicit and implied age limits on job applicants and the characteristics of firms that set them. Although Japanese law prohibits age discrimination in employment, several exemptions, such as hiring young workers without prior work experience on regular contracts, are allowed. Firms can set an age limit, require job-related experience, or search broadly; however, they can also express their age preference in other ways. In the sample, 24 % of ads included explicit age limits generally capped at 35 years, 26 % set experience requirements, and nearly all contained some form of implied age preference. Consistent with theoretical predictions, the results show that firms with higher capital, those with fewer employees, older firms and those located in urban centers tended to set requirements on applicants. Further, domestic firms, firms with fewer employees, in urban centers and firms using probation periods for new hires were more likely to set age limits. Moreover, firms setting either requirement did not seem to be sensitive to local labor market conditions. Firms searching broadly responded to population age-related increased wage expectations while reducing labor costs by increasing the number of working hours covered by a baseline wage.

Deep insights into biodegradability mechanism and growth cycle adaptability of polylactic acid/hyperbranched cellulose nanocrystal composite mulch

The widespread use of petroleum-based plastic mulch in agriculture has accelerated white and microplastic pollution while posing a severe agroecological challenge due to its difficulty in decomposing in the natural environment. However, endowing mulch film with degradability and growth cycle adaptation remains elusive due to the inherent non-degradability of petroleum-based plastics severely hindering its applications. This work reports polylactic acids hyperbranched composite mulch (PCP) and measured biodegradation behavior under burial soil, seawater, and ultraviolet (UV) aging to understand the biodegradation kinetics and to increase their sustainability in the agriculture field. Due to high interfacial interactions between polymer and nanofiler, the resultant PCP mulch significantly enhances crystallization ability, hydrophilicity, and mechanical properties. PCP mulch can be scalable-manufactured to exhibit modulated degradation performance under varying degradation conditions and periods while concurrently enhancing crop growth (wheat). Thus, such mulch with excellent performance can reduce labor costs and the environmental impact of waste mulch disposal to replace traditional mulch for sustainable agricultural production.

PSXII-13 The Design of Equipment That Combines a High-Pressure Jet and a Translational Scraper to Improve the Manure Treatment Efficiency of Pig Barn Cleaning Machine

Abstract Livestock farming generates a significant amount of manure, which must be effectively managed to prevent environmental pollution and ensure the health and well-being of animals. The cleaning machine is a commonly used tool for removing manure from pig barns: however, it often leaves behind residual waste that is difficult to remove. To address this issue, a new equipment design that combines a high-pressure jet and a translational scraper has been proposed. This design aims to improve the efficiency of manure treatment by weakening the adhesion and removing stubborn waste particles, making it easier for the cleaning machine to collect the manure. The high-pressure jet provides a powerful stream of water that can dislodge even the most stubborn waste particles. For high-pressure washing, a DC 10V/10A standard water pump and a 70L water tank are included. The translational scraper, on the other hand, can move left and right across the surface of the slatted floor, effectively scraping away any remaining waste. For this work, an actuator was installed to move the blade part and the hook type work part. These two components work together to improve the efficiency of manure treatment and reduce the amount of waste left behind. By improving the efficiency of manure treatment, this new equipment design can help to reduce environmental pollution and improve the overall health and well-being of pigs. In addition, it can also reduce labor costs and improve the profitability of pig farming operations. In conclusion, the design of equipment that combines a high-pressure jet and a translational scraper has the potential to significantly improve the manure treatment efficiency of a pig barn cleaning machine. This new design can help to address the challenges associated with manure management in pig farming and promote a more sustainable and efficient approach to livestock farming. This achievement was made by the support of the Ministry of Agriculture, Food and Rural Affairs, R&D project(421044043HD030)

An adaptive crack inspection method for building surface based on BIM, UAV and edge computing

Unmanned aerial vehicle (UAV) and building information modeling (BIM) have been applied to inspect building surfaces to improve efficiency and reduce labor costs. However, most of the current research focus on the inspection mode of “first collect and then identify” with limited intelligence. Therefore, this study proposes a “global-local” adaptive inspection method for building surfaces by integrating BIM, UAV (with multifunction camera), and edge computing, enabling real-time synchronization of crack data collection and analysis. First, the building surface area to be inspected is extracted from BIM to generate global inspection points using the selected camera's field of view (FOV) and considering coordinates transformation between BIM and reality. Then, A star algorithm integrating with genetic algorithm (GA) is used to optimize the global flight path based on generated inspection points. Finally, the coordinates of the inspection points based on BIM are converted into real-world coordinates, and the flight parameters such as the flight yaw angle (FYA) and the gimbal yaw angle (GYA) are calculated to generate the inspection mission. During global inspection, a local adaptive inspection is proposed for each FOV through real-time local crack identification and flight replanning using edge computing and zoom camera. In addition, multiple battery supply points are automatically added considering the endurance of the UAV. A real building has been used to validate the proposed “global-local” adaptive inspection method. The experiment results show that the inspection time has been significantly reduced compared with the traditional method, and the crack identification of building surfaces can be implemented with high efficiency, precision, quality, and low cost.

A design of a smart farm system for cattle monitoring

<span>The integration of technology in agriculture has led to the adoption of smart farming systems, which are becoming increasingly popular for optimizing resources, reducing labor costs, and improving efficiency. This article presents a design of a smart cattle monitoring farm system, which focuses on monitoring individual animal behavior and health, improving resource management, and optimizing overall farm efficiency. The proposed system integrates various internet of things (IoT) sensors, communication technologies, and cloud computing to provide a real-time monitoring solution <span>for cattle farms. The system uses machine learning (ML) algorithms to analyze data on cattle behavior, health, and performance, which can be accessed through web and mobile applications by farmers to proactively monitor their herd. The adoption of the system promises good results for intelligently transforming traditional cattle farming by reducing manual monitoring time and achieving a good accuracy in early detection of diseases, automated tracking of estrus cycles, and location mapping of cattle across the farm fields.</span></span>