Reduce Labor Costs Research Articles

Оценка экономической эффективности внедрения систем искусственного интеллекта в судопроизводство

In the context of digitalization processes being actively introduced into all spheres of public life, the issues of evaluating the effectiveness of their practical application are becoming relevant. The judicial system is no exception, since the use of modern information technologies in it, including artificial intelligence systems, will help to reduce material costs for the functioning of courts, reduce labor costs and save time, minimize the risks of making erroneous decisions. In this regard, the authors have proposed tools for assessing the economic results associated with the introduction of artificial intelligence systems in judicial activity. A preliminary assessment of the economic efficiency of the introduction of artificial intelligence into the judicial system will allow to predict the possible results of the functioning of the judicial system in the future, and an assessment of the budget effect will ensure savings due to reduced costs due to the introduction of modern intelligent systems.

Combined Implement for Soil Preparation and Sowing Сucurbits

The design of an energy-saving combined implement capable of performing basic and pre-sowing tillage, applying mineral fertilizers, forming irrigation furrows and sowing melons and gourds in one pass is substantiated. The use of a combined tillage unit allows to reduce labor costs by up to 39%, energy consumption for soil preparation – up to 45%, reduce the duration of work, reduce soil compaction by reducing the unit’s passes across the field and retain moisture in the soil layer.

Music sentiment classification based on an optimized CNN-RF-QPSO model

PurposeMusic sentiment analysis helps to promote the diversification of music information retrieval methods. Traditional music emotion classification tasks suffer from high manual workload and low classification accuracy caused by difficulty in feature extraction and inaccurate manual determination of hyperparameter. In this paper, the authors propose an optimized convolution neural network-random forest (CNN-RF) model for music sentiment classification which is capable of optimizing the manually selected hyperparameters to improve the accuracy of music sentiment classification and reduce labor costs and human classification errors.Design/methodology/approachA CNN-RF music sentiment classification model is designed based on quantum particle swarm optimization (QPSO). First, the audio data are transformed into a Mel spectrogram, and feature extraction is conducted by a CNN. Second, the music features extracted are processed by RF algorithm to complete a preliminary emotion classification. Finally, to select the suitable hyperparameters for a CNN, the QPSO algorithm is adopted to extract the best hyperparameters and obtain the final classification results.FindingsThe model has gone through experimental validations and achieved a classification accuracy of 97 per cent for different sentiment categories with shortened training time. The proposed method with QPSO achieved 1.2 and 1.6 per cent higher accuracy than that with particle swarm optimization and genetic algorithm, respectively. The proposed model had great potential for music sentiment classification.Originality/valueThe dual contribution of this work comprises the proposed model which integrated two deep learning models and the introduction of a QPSO into model optimization. With these two innovations, the efficiency and accuracy of music emotion recognition and classification have been significantly improved.

Алгоритмы принятия решений для автоматизированного выполнения и планирования работ по проверке рельсовых цепей на шунтовую чувствительность

The article considers the issues of practical application of the approach on performance automation and scheduling of works on the maintenance of railway automatics and remote controlling devices in the process of planned preventive maintenance realization which includes the analysis of known ways to automate railway circuit inspections on shunt sensitivity and shunt effect performance control in the process of finding a moving unit on a rail circuit. At present, topical problem is the reduction of labor costs on the maintenance of dispersed objects of automatics and remote control which to solve for, we propose to use algorithms providing for technological situation identification and determination of the many of works which will be considered automatically as accomplished as well as the many of ones which will be needed to be accomplished in due time. The essence of the proposed approach to automation of being considered processes is in the following: if in the process of rail circuit functioning in regular mode, the situation, that’s analogous to the performance of work on shunt sensitivity testing (that’s stipulated by work operation technology), is realized, then work is considered to be accomplished at the presence of means of reliable control for the values of rail circuit parameters.

Integrate computation intelligence with Bayes theorem into complex construction installation: a heuristic two-stage resource scheduling optimisation approach

The cost control challenge in construction and installation projects has always been a critical concern for construction entities. The complexity of task collaboration among various equipment and nodes during the installation process leads to extended construction duration, resulting in increased construction costs. To address this issue, this paper proposes a heuristic two-stage optimal deployment approach called MERD. The MERD approach incorporates intelligent computing principles from computer science into the resource scheduling of the construction process, modelling the installation scheduling problem into a combinatorial optimisation problem. Designing the probability method based on Bayes theorem, the MERD approach carries out an installation provisioning mechanism to optimise personnel and device allocation in the selected area. As a result, the MERD approach minimises construction hours and reduces labour costs in the construction process. Experimental results demonstrate the effectiveness and efficiency of the MERD approach in reducing work time and cost in engineering projects.

OPTIMIZATION OF WORKERS QUANTITY USING MATHEMATICAL MODEL

Production and maintenance processes are inherent in the life cycle of every product. Despite great efforts to automate these processes, a great deal of human resources are still required, which represent a significant part of the financial costs. Each process is composed of sub-tasks that require certain specifics in terms of the number of staff, their expertise, qualifications and experience. It is assumed that the staff are divided according to specifics into different groups with differing wages. Workers' wages are reflected in the final financial cost of the product, its life cycle and its return. Reducing labour costs in a production or maintenance process can be achieved by reducing the total number of staff deployed in the process and by appropriately composing groups of workers. Reducing labour costs leads to increased competitiveness in the market. The main tools of competitiveness are price, speed and range of services offered. This paper examines a strategy that uses price as the main tool for competitiveness in the market. One way to reduce the final price of the product for the customer is to optimise the costs of human resources. This can be achieved through appropriate planning of staff shifts. The specifics of the deployment of staff in a production or maintenance process depend on the requirements of the process sub-tasks. This means that each group of workers can only handle a certain group of tasks according to their qualifications. A Binary Programming Problem with Linear Bonds will be used to plan the deployment of staff, aiming to minimize the number of workers needed in a production or maintenance process within a predefined timeframe.

Social Service Robot using Gesture recognition technique

A robot is a machine that can automatically do a task or a series of tasks based on its programming and environment. They are artificially built machines or devices that can perform activities with utmost accuracy and precision minimizing time constraints. Service robots are technologically advanced machines deployed to service and maintain certain activities. Research findings convey the essential fact that serving robots are now being deployed worldwide. Social robotics is one such field that heavily involves an interaction between humans and an artificially built machine. These man-built machines interact with humans and can also understand social terms and words. Modernization has bought changes in design and mechanisms due to this ever-lasting growth in technology and innovation. Therefore, food industries are also dynamically adapting to the new changes in the field of automation to reduce human workload and increase the quality of service. Deployment of a robot in the food industries which help to aid deaf and mute people who face social constraints is an ever-growing challenge faced by engineers for the last few decades. Moreover, a contactless form of speedy service system which accomplishes its task with at most precision and reduced complexity is a feat yet to be perfected. Preservation of personal hygiene, a better quality of service, and reduced labour costs is achieved.

The choice of degrees of freedom of automatic refueling manipulator

Traditional artificial gas station come on all the required 5 to 6 minutes, peak even 10 minutes or so, along with our country automobile holdings continued to increase, how to improve fuel efficiency become an important problem of gas station, according to the above problem, non-inductive refueling “AI” is put forward to cooperate with mechanical arm instead of artificial gas, this scheme can make the refueling time reduced to 3 minutes, Greatly improve refueling efficiency, and reduce labor costs. The manipulator adopts a tandem joint robot with a 2-DOF BB wrist at the wrist and a fixed base, which is driven by the servo motor of each joint as a whole. The end-effector is equipped with a cap opening device, a clamping device and an oil feeding device to open the oil tank cover, unscrew the seal plug and oil feeding tasks respectively.

Growth and nitrogen productivity of drip-irrigated winter wheat under different nitrogen fertigation strategies in the North China Plain

Excessive application of nitrogen (N) fertilizer is the main cause of N loss and poor use efficiency in winter wheat ( Triticum aestivum L.) production in the North China Plain (NCP). Drip fertigation is considered to be an effective method for improving N use efficiency and reducing losses, while the performance of drip fertigation in winter wheat is limited by poor N scheduling. A two-year field experiment was conducted to evaluate the growth, development and yield of drip-fertigated winter wheat under different split urea (46% N, 240 kg ha −1 ) applications. The six treatments consisted of five fertigation N application scheduling programs and one slow-release fertilizer (SRF) application. The five N scheduling treatments were N0-100 (0% at sowing and 100% at jointing/booting), N25-75 (25% at sowing and 75% at jointing/booting), N50-50 (50% at sowing and 50% at jointing/booting), N75-25 (75% at sowing and 25 at jointing/booting), and N100-0 (100% at sowing and 0% at jointing/booting). The SRF (43% N, 240 kg ha −1 ) was only used as fertilizer at sowing. Split N application significantly ( P <0.05) affected wheat grain yield, yield components, aboveground biomass (ABM), water use efficiency (WUE) and nitrogen partial factor productivity (NPFP). The N50-50 and SRF treatments respectively had the highest yield (8.84 and 8.85 tons ha −1 ), ABM (20.67 and 20.83 tons ha −1 ), WUE (2.28 and 2.17 kg m −3 ) and NPFP (36.82 and 36.88 kg kg −1 ). This work provided substantial evidence that urea-N applied in equal splits between basal and topdressing doses compete economically with the highly expensive SRF for fertilization of winter wheat crops. Although the single-dose SRF could reduce labor costs involved with the traditional method of manual spreading, the drip fertigation system used in this study with the N50-50 treatment provides an option for farmers to maintain wheat production in the NCP.

МОДЕЛЮВАННЯ ТА ПЕРСПЕКТИВНІ НАПРЯМИ КОНСТРУКТИВНО-ТЕХНОЛОГІЧНИХ СХЕМ ГРУДКОРУЙНУЮЧИХ РОБОЧИХ ОРГАНІВ КОРЕНЕБУЛЬБОЗБИРАЛЬНИХ МАШИН

Для успішного виходу України на західні ринки необхідно забезпечити конкурентоспроможність власної продукції, яка досягається завдяки комплексній механізації технологічних процесів, зниженню витрат праці, збільшенню врожайності та якості одержуваної продукції. Відповідно, метою дослідження було проведення порівняльного аналізу технологій і машин для збирання картоплі, розроблення моделі вдосконалення грудкоруйнуючих робочих органів й окреслення перспективних напрямів удосконалення коренебульбозбиральних машин. Дослідження проводилися шляхом технологічно-конструкційного аналізу технологій і машин для збирання картоплі. У процесі досліджень використовувались методи порівняння та математичного моделювання конструктивно-технологічних схем грудкоруйнуючих робочих органів. Інформаційною базою досліджень слугували праці українських і зарубіжних науковців із технологій і машин для збирання картоплі. На основі проведеного порівняльного аналізу технологій і машин для збирання картоплі виявлено основні конструктивно-технологічні схеми, що впливають на агротехнічні показники збиральної техніки, розроблено модель грудкоруйнуючих робочих органів (збиральних машин). Дана модель включає руйнування ґрунтових грудок: по-перше, катками безпосередньо перед підкопом бульбоносного пласта; по-друге, грудкоподрібнюючими пристроями (балонами, бітерами або інтенсификаторами сепарації) різних конструкцій у процесі виконання технологічних операцій картоплезбиральної машиною. Попереднє руйнування бульбоносного пласта проводиться безпосередньо перед його підкопуванням і в момент підкопування опорними катками різних типів та інших конструктивних і кінематичних параметрів робочих органів машин.

Poultry Monitoring and Controlling System using Arduino Uno

This work is used to save the time and dependence on the work. By combining wireless system networks to manage and monitor environmental parameters such as light intensity, fan, water and temperature are automatically monitored and controlled. This system also helps farmers to monitor the poultry farm. It is a combination of Arduino Uno, gas sensor, LDR sensor, water sensor and temperature sensor which makes the work easier. The changes in this system are observed with the help of LCD display. The production of automatic feeding machines for poultry farming may be necessary to expand this industry in rural India. In the present system, a poultry farm requires labour. The chickens are fed manually by humans. This automatic feeding system solves the labour shortage while reducing labour costs. This automatic feeding system can be used in both large and small poultry farms and agriculture. In this method, the feed is put into a feeder or a feeder. This device also uses an automatic misting system to maintain a constant temperature in the farm. This automatic misting device can also be used to keep the room at a constant temperature. This automatic misting device can also be used to keep the air in the livestock farm at a comfortable temperature. This device is easy to use and affordable. Small poultry farms in India can use it. It can be controlled by android phone.

Advancing the marketing‐operations interface in omnichannel retail

Advancing the marketing‐operations interface in omnichannel retail

Assessing the Economic Performance of Multipurpose Collaborative Robots toward Skillful and Sustainable Viticultural Practices

The increased cost of labor in modern viticulture stemming from the nature of operations that require physical strength and precision, coupled with labor shortages, poses a significant constraint in facilitating and scheduling seasonal activities. Therefore, autonomous collaborative robots present a potential solution for achieving sustainable development objectives and decreasing operational expenditures in agricultural operations. The current paper presents an economic assessment of collaborative robots (or cobots for short) in comparison to conventional labor for four different cultivars in Greece in a lifecycle costing methodological framework. The selected cultivars are Asyrtiko, Cabernet Sauvignon, Merlot and Tempranillo, which are cultivated by two private wineries in the area of interest. All the relevant expenses of their annual production were distributed to agricultural operations, and eight scenarios were developed to compare conventional and cobot practices. The results indicate the great potential of cobots regarding specific viticultural operations such as weed control, pruning, herbiciding and topping. The adoption of cobots in these operations has the potential to contribute to sustainable agriculture by reducing labor costs and addressing labor shortages, while also increasing the efficiency and precision of these tasks. Nevertheless, the defoliation and tying operations appeared to be inefficient in most cases in comparison to conventional labor practices. Overall, the annual equivalent costs could be reduced by up to 11.53% using cobots, even though the projected lifetime of the cobots plays a significant role in the cost-effectiveness of autonomous robotic labor in viticulture. In conclusion, cobots could be instrumental in the Greek viticulture, integrating innovation and high-quality products toward sustainable agricultural development.

Attention-Based Convolutional Neural Network for Ingredients Identification

In recent years, with the development of artificial intelligence, smart catering has become one of the most popular research fields, where ingredients identification is a necessary and significant link. The automatic identification of ingredients can effectively reduce labor costs in the acceptance stage of the catering process. Although there have been a few methods for ingredients classification, most of them are of low recognition accuracy and poor flexibility. In order to solve these problems, in this paper, we construct a large-scale fresh ingredients database and design an end-to-end multi-attention-based convolutional neural network model for ingredients identification. Our method achieves an accuracy of 95.90% in the classification task, which contains 170 kinds of ingredients. The experiment results indicate that it is the state-of-the-art method for the automatic identification of ingredients. In addition, considering the sudden addition of some new categories beyond our training list in actual applications, we introduce an open-set recognition module to predict the samples outside the training set as the unknown ones. The accuracy of open-set recognition reaches 74.6%. Our algorithm has been deployed successfully in smart catering systems. It achieves an average accuracy of 92% in actual use and saves 60% of the time compared to manual operation, according to the statistics of actual application scenarios.

Genomic-based genetic parameters for milkability traits derived from automatic milking systems in North American Holstein cattle

The number of dairy farms adopting automatic milking systems (AMS) has considerably increased around the world aiming to reduce labor costs, improve cow welfare, increase overall performance, and generate a large amount of daily data, including production, behavior, health, and milk quality records. In this context, this study aimed to (1) estimate genomic-based variance components for milkability traits derived from AMS in North American Holstein cattle based on random regression models; and (2) derive and estimate genetic parameters for novel behavioral indicators based on AMS-derived data. A total of 1,752,713 daily records collected using 36 milking robot stations and 70,958 test-day records from 4,118 genotyped Holstein cows were used in this study. A total of 57,600 SNP remained after quality control. The daily-measured traits evaluated were milk yield (MY, kg), somatic cell score (SCS, score unit), milk electrical conductivity (EC, mS), milking efficiency (ME, kg/min), average milk flow rate (FR, kg/min), maximum milk flow rate (FRM, kg/min), milking time (MT, min), milking failures (MFAIL), and milking refusals (MREF). Variance components and genetic parameters for MY, SCS, ME, FR, FRM, MT, and EC were estimated using the AIREMLF90 software under a random regression model fitting a third-order Legendre orthogonal polynomial. A threshold Bayesian model using the THRGIBBS1F90 software was used for genetically evaluating MFAIL and MREF. The daily heritability estimates across days in milk (DIM) ranged from 0.07 to 0.28 for MY, 0.02 to 0.08 for SCS, 0.38 to 0.49 for EC, 0.45 to 0.56 for ME, 0.43 to 0.52 for FR, 0.47 to 0.58 for FRM, and 0.22 to 0.28 for MT. The estimates of heritability (± SD) for MFAIL and MREF were 0.02 ± 0.01 and 0.09 ± 0.01, respectively. Slight differences in the genetic correlations were observed across DIM for each trait. Strong and positive genetic correlations were observed among ME, FR, and FRM, with estimates ranging from 0.94 to 0.99. Also, moderate to high and negative genetic correlations (ranging from -0.48 to -0.86) were observed between MT and other traits such as SCS, ME, FR, and FRM. The genetic correlation (± SD) between MFAIL and MREF was 0.25 ± 0.02, indicating that both traits are influenced by different sets of genes. High and negative genetic correlations were observed between MFAIL and FR (-0.58 ± 0.02) and MFAIL and FRM (-0.56 ± 0.02), indicating that cows with more MFAIL are those with lower FR. The use of random regression models is a useful alternative for genetically evaluating AMS-derived traits measured throughout the lactation. All the milkability traits evaluated in this study are heritable and have demonstrated selective potential, suggesting that their use in dairy cattle breeding programs can improve dairy production efficiency in AMS.

Intelligent Alignment Monitoring Method for Tortilla Processing Based on Machine Vision

As people pay more and more attention to a healthy diet, it has become a consensus to eat more coarse grains. The development of its edible value is of great significance for a healthy human diet and has attracted the attention of many scholars and food processing companies. However, due to the differences in protein composition and structure between corn flour and wheat protein, it is difficult to form a network structure during processing, and the viscoelasticity and flexibility are poor. Based on this, this paper proposes a machine vision-based noodle positioning monitoring method to achieve noodle alignment monitoring in the noodle processing process. First, the images are captured by binocular cameras and preprocessed. Further, feature detection and matching algorithms are used to recover the pose information between binocular cameras, and then the recognition targets are matched. Finally, noodle alignment monitoring during noodle processing is achieved. Experiments show that the detection accuracy of the method proposed in this paper is much higher than the traditional manual detection, which can improve the noodle quality and reduce labor costs.

In Situ Sea Cucumber Detection across Multiple Underwater Scenes Based on Convolutional Neural Networks and Image Enhancements.

Recently, rapidly developing artificial intelligence and computer vision techniques have provided technical solutions to promote production efficiency and reduce labor costs in aquaculture and marine resource surveys. Traditional manual surveys are being replaced by advanced intelligent technologies. However, underwater object detection and recognition are suffering from the image distortion and degradation issues. In this work, automatic monitoring of sea cucumber in natural conditions is implemented based on a state-of-the-art object detector, YOLOv7. To depress the image distortion and degradation issues, image enhancement methods are adopted to improve the accuracy and stability of sea cucumber detection across multiple underwater scenes. Five well-known image enhancement methods are employed to improve the detection performance of sea cucumber by YOLOv7 and YOLOv5. The effectiveness of these image enhancement methods is evaluated by experiments. Non-local image dehazing (NLD) was the most effective in sea cucumber detection from multiple underwater scenes for both YOLOv7 and YOLOv5. The best average precision (AP) of sea cucumber detection was 0.940, achieved by YOLOv7 with NLD. With NLD enhancement, the APs of YOLOv7 and YOLOv5 were increased by 1.1% and 1.6%, respectively. The best AP was 2.8% higher than YOLOv5 without image enhancement. Moreover, the real-time ability of YOLOv7 was examined and its average prediction time was 4.3 ms. Experimental results demonstrated that the proposed method can be applied to marine organism surveying by underwater mobile platforms or automatic analysis of underwater videos.

Within-Plant and Within-Field Distribution Patterns of Asian Bean Thrips and Melon Thrips in Snap Bean

Asian bean thrips, Megalurothrips usitatus Bagnall, are a serious pest of vegetable crops, especially leguminous crops, across the Asian continent. In Florida, it is a new invasive pest of snap beans. In 2019, it was recorded for the first time in the United States in snap bean (Phaseolus vulgaris) fields. Another thrips species, melon thrips, Thrips palmi Karny, is also a serious pest that affects several vegetable crops. Within-plant and within-field distribution patterns of M. usitatus and T. palmi were determined in snap bean fields in southern Florida. The highest number of both thrips species (Asian bean thrips and melon thrips) in snap beans were in flowers, followed by leaves and pods. Both adults and immatures of these thrips exhibited regular to clumped distribution patterns in bean fields. Several statistical indices showed agreement in the distribution patterns of Asian bean thrips, melon thrips, and larvae, irrespective of sampling units and plot size, in three years of study. In most instances, the distribution of Asian bean thrips and melon thrips was aggregated. This study assessed the optimum sample size to accurately determine the population density of these thrips for management purposes. The results from this study will be useful for implementing targeted management programs against thrips pests, thereby reducing labor costs and time. This information will also help reduce agrochemical use.

A latent survival model integrated computer simulation-based evaluation for nursing home staffing

Nursing homes (NHs) are critical facilities for caring frail older adults with around-the-clock formal care and personal assistance. To ensure quality of care for NH residents, an adequate staffing level is of great importance. Current NH staffing practice is mainly based on experience and regulation. The objective of this paper is to investigate the viability of experience-based and regulation-based strategies, as well as alternative staffing strategies to meet the heterogeneous service demand of NH residents at reduced labor cost under various scenarios of census compositions. We propose a predictive analytics integrated computer simulation model to characterize the heterogeneous service demand of NH residents, and further evaluate and identify promising staffing strategies at the facility level. Specifically, we propose a predictive model based on latent survival analysis to characterize diverse length-of-stay (LOS) with multiple discharge dispositions among NH residents. Further, we develop a simulation model with the incorporation of predictive analytics and domain knowledge to characterize the heterogeneous service demand of NH residents on different types of caregivers over time. Based on the simulation model, we develop a graphical user interface for the simulator to evaluate different staffing strategies at the facility level and inform NH administrators about promising strategies. We use real NH data to validate the proposed model and demonstrate its effectiveness. The proposed predictive LOS model considering multiple discharge dispositions exhibits superior prediction performance and offers better staffing decisions at reduced costs than those without the consideration. With the improved modeling fidelity via integrating predictive analytics with computer simulation, the proposed model is flexible to evaluate various staffing strategies using total labor cost as a performance metric, and can identify promising staffing strategies to meet the service demand of NH residents. Promising staffing strategies with the suggested staff-to-resident (SR) ratio can significantly reduce the total labor cost of multiple types of caregivers, as compared to the benchmark strategies, such as the SR ratios based on industrial practice or minimum requirement of state regulation. Moreover, we construct multiple scenarios of different census compositions of NH residents to demonstrate the capability of the proposed model. Our proposed model can facilitate NH staffing decision making to meet the heterogeneous service demand of NH residents at reduced labor costs.

Implementation of Computer-Vision-Based Farrowing Prediction in Pens with Temporary Sow Confinement.

The adoption of temporary sow confinement could improve animal welfare during farrowing for both the sow and the piglets. An important challenge related to the implementation of temporary sow confinement is the optimal timing of confinement in crates, considering sow welfare and piglet survival. The objective of this study was to predict farrowing with computer vision techniques to optimize the timing of sow confinement. In total, 71 Austrian Large White and Landrace × Large White crossbred sows and four types of farrowing pens were included in the observational study. We applied computer vision model You Only Look Once X to detect sow locations, the calculated activity level of sows based on detected locations and detected changes in sow activity trends with Kalman filtering and the fixed interval smoothing algorithm. The results indicated the beginning of nest-building behavior with a median of 12 h 51 min and ending with a median of 2 h 38 min before the beginning of farrowing with the YOLOX-large object detection model. It was possible to predict farrowing for 29 out of 44 sows. The developed method could reduce labor costs otherwise required for the regular control of sows in farrowing compartments.