Low Level Of Automation Research Articles

Design and Test of a Traction Side-Pull Square Straw Bale Pick-Up-and-Stack Truck

To address the issues of limited one-time loading capacity, single functionality, and low automation level in existing square straw bale pickers, a large automated square straw bale pick-up-and-stack truck that integrates picking, stacking, transporting, and bundling functions has been developed, combining the technical advantages of one-time field removal and storage of bales. We innovatively designed a side-pulling traction mechanism that can realize the rapid transition between the transporting state and working state of the machine; a picking device that can complete the continuous action of forking, lifting, turning positioning, and de-forking; and a bundling device that can realize the adjustment of the attitude of square straw bales. Response surface tests were conducted on the prototype to determine the key structural and operational parameters, using the bundle completion rate and regular bale rate as evaluation indicators. Regression and significance tests were performed on the machine’s forward speed, chassis frame offset, and the ground clearance of the fork tine to determine the influence and priority of these factors on the evaluation indicators. Through multi-objective function optimization of the regression model, the optimal parameter combination was found to be a machine forward speed of 15.5 km/h, a chassis frame offset of 2126 mm, and a fork tine ground clearance of 225 mm, resulting in a bundle completion rate of 98.85% and a regular bale rate of 96.96%. Subsequent field tests with the optimized parameters showed that at a machine forward speed of 15.5 km/h, a chassis frame offset of 2126 mm, and a fork tine ground clearance of 225 mm, the bundle completion rate was 98.37% and the regular bale rate was 95.83%, meeting the relevant design requirements. This study can provide a reference for the design and development of straw collection and storage machinery.

Towards sustainable high-speed cruising: Optimizing energy efficiency of plug-in hybrid electric vehicle via intelligent pulse-and-glide strategy

Conventional pulse-and-glide (PnG) strategies expose issues including poor coordination with energy management systems (EMSs), discomfort and low levels of automation, this paper innovatively addresses these challenges by the comprehensive considerations of energy economy, dynamic performance, and ride comfort, proposing an intelligent pulse-and-glide (I-PnG) strategy tailored for plug-in hybrid electric vehicle (PHEV) configurations. The I-PnG strategy is aimed at optimizing economic cruising in high-speed scenarios; however, it demonstrates applicability across diverse conditions without deterioration in performance. I-PnG is a data-driven autonomous driving solution developed through a deep Q network (DQN), featuring two distinct modes: ECO and SPORT, the former prioritizes energy economy, while the latter emphasizes dynamic performance enhancement. The results indicate that our proposed strategy significantly optimizes energy management compared to the baseline methods. Specifically, during variable-speed cruising, I-PnG ECO achieves a maximum energy efficiency improvement of 16.13 % and a cost reduction of 7.74 %, while I-PnG SPORT exhibits a maximum energy efficiency improvement of 13.37 % and a cost reduction of 3.92 %. Under the Highway Fuel Economy Test (HWFET), I-PnG ECO and I-PnG SPORT further improve energy efficiency by up to 22.94 % and 19.04 %, respectively, with corresponding cost savings of 18.47 % and 13.35 %.

Measures to increase the reliability and efficiency of water supply and wastewater disposal systems

The reliability of water supply and wastewater disposal systems has a significant impact on the environment: therefore, it is essential to implement new methods and approaches that reduce the risk of emergency situations. Based on the experience in automation, maintenance and modernization of wastewater disposal and water supply systems, the study suggests introducing additional programmable modules into automated control systems that provide control and regulation directly on the actuator. For this purpose, the study involves OWEN Logic programming environment, which manages pumping stations through programmable relays. The paper presents functional diagrams that have been introduced into standard macros to increase the reliability of pumping units. In addition, the paper demonstrates that the implementation of multi-level management facilitates the improvement of existing water supply and wastewater disposal systems, thereby increasing their functional capabilities and ensuring the efficient operation of technological equipment. The conducted analysis shows that the adoption of this method for managing technological processes will enhance the reliability and durability of the equipment, ensuring uninterrupted operation of pumping stations. With a change in the performance of water supply and wastewater disposal systems, the installed devices will allow for modernization with minimal economic costs, and reprogramming the operation of the low-level automation without affecting the performance of other levels.

A high-throughput system for drug screening based on the movement analysis of zebrafish

Zebrafish is a highly advantageous model animal for drug screening and toxicity evaluation thanks to its amenability to optical imaging (i.e., transparency), possession of organ structures similar to humans, and the ease with which disease models can be established. However, current zebrafish drug screening technologies and devices suffer from limitations such as low level of automation and throughput, and low accuracy caused by the heterogeneity among individual zebrafish specimens. To address these issues, we herein develop a high-throughput zebrafish drug screening system. This system is capable of maintaining optimal culturing conditions and simultaneously monitoring and analyzing the movement of 288 zebrafish larvae under various external conditions, such as drug combinations. Moreover, to eliminate the effect of heterogeneity, locomotion of participating zebrafish is assessed and grouped before experiments. It is demonstrated that in contrast to the experimental results without pre-selection, which shows ∼20 % damaged motor function (i.e., degree of attenuation), the drug-induced variations among zebrafish with equivalent mobility reaches ∼80 %. Overall, our high-throughput zebrafish drug screening system overcomes current limitations by improving automation, throughput, and accuracy, resulting in enhanced detection of drug-induced variations in zebrafish motor function.

Remote Sensing Thematic Product Generation for Sustainable Development of the Geological Environment

Remote sensing thematic data products are critical for assessing and analyzing geological environments, while efficient generation of thematic products is also highly significant for achieving corresponding sustainable development goals (SDGs). Currently, remote sensing thematic product generation has problems like low levels of automation and efficiency. Addressing these challenges is imperative for advancing sustainable development within the geological environment. This paper aims to address issues related to the generation of geological environment remote sensing thematic products, sorting through the overall process of remote sensing thematic product generation, exploring algorithm encapsulation, combination, and execution under technical methods for container and workflow, and relies on the Spark distributed processing architecture to achieve efficient thematic product generation supported by multiple geological environment data processing models. Finally, taking the three SDGs of SDG6, SDG11, and SDG15 as examples, we achieved the generation of a variety of thematic products such as the interpretation of water body distribution, extraction of urban informal settlements and distribution of water and soil erosion. Meanwhile, we comparatively analyzed the efficiency of thematic product generation on different processing architectures, and the experimental results further verified the feasibility and effectiveness of our proposed solution. This research provides a programme for the automated and intelligent generation of geological environment remote sensing thematic products and effectively assists the construction of sustainable development in the geological environment.

A Measurement Method for Body Parameters of Mongolian Horses Based on Deep Learning and Machine Vision

The traditional manual methods for measuring Mongolian horse body parameters are not very safe, have low levels of automation, and cannot effectively ensure animal welfare. This research proposes a method for extracting target Mongolian horse body parameters based on deep learning and machine vision technology. Firstly, Swin Transformer is used as the backbone feature extraction network of Mask R-CNN model, and the CNN-based differentiated feature clustering model is added to minimize the loss of similarity and spatial continuity between pixels, thereby improving the robustness of the model while reducing error pixels and optimizing the rough mask boundary output. Secondly, an improved Harris algorithm and a polynomial fitting method based on contour curves are applied to determine the positions of various measurement points on the horse mask and calculate various body parameters. The accuracy of the proposed method was tested using 20 Mongolian horses. The experimental results show that compared with the original Mask R-CNN network, the PA (pixel accuracy) and MIoU (mean intersection over union) of the optimized model results increased from 91.46% and 84.72% to 98.72% and 95.36%, respectively. The average relative errors of shoulder height, withers height, chest depth, body length, croup height, shoulder angle, and croup angle were 4.01%, 2.98%, 4.86%, 2.97%, 3.06%, 4.91%, and 5.21%, respectively. The research results can provide technical support for assessing body parameters related to the performance of horses under natural conditions, which is of great significance for improving the refinement and welfare of Mongolian horse breeding techniques.

Development of a dedicated process simulator for the digital twin in apparel manufacturing: a case study

PurposeThe purpose of this study is to introduce a dedicated simulator to automatically generate and simulate a balanced apparel assembly line, which is critical to the digital twin concept in apparel manufacturing. Given the low automation level in apparel manufacturing, this is a first step toward the implementation of a smart factory based on cyber-physical systems.Design/methodology/approachThe mixed task assignment algorithm was implemented to automatically generate a module-based apparel assembly line in the developed simulator. To validate the developed simulator, a case study was conducted using process analysis data of technical jackets obtained from an apparel manufacturer. The case study included three scenarios: calculating the number of workers, selecting orders based on factory capacity and managing unexpected worker absences.FindingsThe developed simulator is approximately 97.2% accurate in assigning appropriate tasks to workstations using the mixed task assignment algorithm. The simulator was also found to be effective in supporting decision-making for production planning, order selection and apparel assembly line management. In addition, the module-based line generation algorithm made it easy to modify the assembly line.Originality/valueThis study contributes a novel approach to address the challenge of low automation levels in apparel manufacturing by introducing a dedicated simulator. This dedicated simulator improves the efficiency of virtual apparel assembly line generation and simulation, which distinguishes it from existing commercial simulation software.

Reversible data hiding with automatic contrast enhancement for color images

Automatic contrast enhancement (ACE) is a technique that can automatically enhance the image contrast. Reversible data hiding (RDH) with ACE (ACERDH) can achieve ACE while hiding data. However, some methods with good performance for color images suffer from insufficient enhancement. Therefore, an ACERDH method based on the R, G, B, and V channels enhancement is proposed. First, histogram shifting with contrast control is proposed to enhance the R, G, and B channels. It can prevent contrast degradation and histogram shifting from stopping prematurely. Then, the V channel is enhanced. Since some RDH methods with non-ACE that can well enhance the V channel have a low automation level, histogram shifting with brightness control that can realize ACE very well is proposed. It can effectively avoid over-enhancement by controlling the brightness. Experimental results verify that the proposed method improves the image quality and embedding capability better than some state-of-the-art methods.

Pairing in-vehicle intelligent agents with different levels of automation: implications from driver attitudes, cognition, and behaviors in automated vehicles

ABSTRACT In-vehicle intelligent agents (IVIAs) have been developed to improve user experience in autonomous vehicles. Yet, the impact of the automation system on driver behavior and perception toward IVIAs is unclear. In this study, we conducted three experiments with 73 participants in a driving simulator to examine how automation system parameters (the level of automation system and IVIA features) influence driver attitudes, cognition, and behaviors when driving or riding in a simulated vehicle. We focused on subjective evaluations of driver-agent interaction and driver trust toward IVIAs to assess driver attitudes, driver situation awareness, and visual distraction to capture their cognition, and their driving performance to understand their behaviors. Our results show that the level of automation system affects drivers’ attitudes toward agent capabilities (e.g. perceived intelligence). Embodiment benefits are more pronounced with Level 5 systems, while speech style, in general, is more influential in determining affective aspects of user attitudes (e.g. Warmth, Likability). As the level of automation increases, drivers engage in more visual distractions. In addition, conversational speech style in general encouraged safer driving behaviors indicated by more stable lateral control under lower levels of automation. Our findings uncover the path of how system parameters affect driver behaviors through system evaluation and trust in agents. These findings have important implications for the development of cohesive user experiences in future transportation systems.

Puttybot: A sensorized robot for autonomous putty plastering

AbstractPlastering is dominated manually, exhibiting low levels of automation and inconsistent finished quality. A comprehensive review of literature indicates that extant plastering robots demonstrate a subpar performance when tasked with rectifying defects in the transition area. The limitations encompass a lack of capacity to independently evaluate the quality of work or perform remedial plastering procedures. To address this issue, this research describes the system design of the Puttybot and a paradigm of plastering to solve the stated problems. The Puttybot consists of a mobile chassis, a lift platform, and a macro/micromanipulator. The force‐controlled scraper parameters have been calibrated to dynamically modify their rigidity in response to the applied putty. This strategy utilizes convolutional neural networks to identify plastering defects and executes the plastering operation with force feedback. This paradigm's effectiveness was validated during an autonomous plastering trial wherein a large‐scale wall was processed without human involvement.

Design and Experiment of Automatic Transport System for Planting Plate in Plant Factory

In the plant factories using stereoscopic cultivation systems, the cultivation plate transport equipment is an essential component of production. However, there are problems, such as high labor intensity, low levels of automation, and poor versatility of existing solutions, that can affect the efficiency of cultivation plate transport processes. To address these issues, this study designed a cultivation plate transport system that can automatically input and output cultivation plates, and can flexibly adjust its structure to accommodate different cultivation frame heights. We elucidated the working principles of the transport system and carried out structural design and parameter calculation for the lift cart, input actuator, and output actuator. In the input process, we used dynamic simulation technology to obtain an optimum propulsion speed of 0.3 m·s−1. In the output process, we used finite element numerical simulation technology to verify that the deformation of the cultivation plate and the maximum stress suffered by it could meet the operational requirements. Finally, operation and performance experiments showed that, under the condition of satisfying the allowable amount of positioning error in the horizontal and vertical directions, the horizontal operation speed was 0.2 m·s−1, the maximum positioning error was 2.87 mm, the vertical operation speed was 0.3 m·s−1, and the maximum positioning error was 1.34 mm. Accordingly, the success rate of the transport system was 92.5–96.0%, and the operational efficiency was 176–317 plates/h. These results proved that the transport system could meet the operational requirements and provide feasible solutions for the automation of plant factory transport equipment.

Multi-Vehicle Unmanned Following Operation Driving System Based on OpenMV

In response to the low level of agricultural production automation and labor shortage, to improve the efficiency of agricultural machinery utilization and the reliability of automatic navigation, this paper designs an intelligent agricultural machinery system based on OpenMV for multi-vehicle unmanned following operations. Firstly, the kinematics of the agricultural machinery vehicle is analyzed, the path recognition and location line extraction are carried out using the OpenMV vision module, and the agricultural machinery steering control system is designed using the cascade PID closed-loop control technology, which achieves accurate steering control and path tracking on straight lines and curves. Subsequently, using embedded microcontrollers to manage the workflow, utilizing OpenMV visual modules to receive, store, and process positioning information, and sending control instructions to execution units such as the TB6612FNG motor drive module. Moreover, in response to the communication issues involving multiple HC-05 Bluetooth modules in the system's multi-vehicle communication, corresponding protocol specifications have been developed, and combined with the use of the HC-SR04 ultrasonic module to maintain dual vehicle distance and obstacle detection, achieving multi-vehicle collaborative following operations. The experimental results show that the system achieves a path recognition navigation accuracy of 93.4% and a communication success rate of 97.5% in simulating the process of farm harvesting and transportation, effectively proving the multi-vehicle collaboration of the system in the paper

Legal bases of the Water Code of the Republic of Kazakhstan

The relevance of the research topic is due to the fact that water security in recent years has been included in the list of global risks of modernity, which requires effective water resources management. In particular, in the Republic of Kazakhstan, this issue has been one of the most acute recently. To date, there are a number of unresolved issues at the inter-national level of water resources management in the republic, as almost all freshwater resources of the country are transboundary in nature, and almost half of them are formed on the territory of neighbouring states. The purpose of this study is to analyse the existing problems in transnational water resources management in the Republic of Kazakhstan and to develop directions for their solution. The research methods include normative-legal method, expert, factual method, content analysis, comparison, generalisation, classification, synthesis and analysis. The article presents the state of water resources of the Republic of Kazakhstan at the present stage of development, including those of transboundary nature. The management of water resources of the republic, including at the inter-national level, is characterised, and problems in this area are revealed. Thus, the main problems to date have been attributed to: dependence on transboundary flows from the territories of neighbouring countries, primarily China and Russia, in recent years increasingly polluting rivers and increasing the volume of water withdrawal, significant wear and tear of infrastructure, low level of automation and digitalisation of management, low efficiency of control over the implementation of measures for the rational use of water resources. To solve the problems, recommendations have been proposed and promising directions for the development of transnational water resources management have been considered, which will contribute to improving the efficiency of transboundary water resources use.

Технология интеллектуального анализа бизнес-процессов для повышения конкурентоспособности предприятий

A high level of competitiveness of a company is one of the key factors that guarantees stable long-term economic development and a stable position of the enterprise in the market. Competition is a trigger for activating innovative processes and introducing modern information technologies into the enterprise infrastructure. The lack of control and regulation system, the low level of automation of processes, their inconsistency with strategic goals and duplication significantly reduce the overall level of competitiveness of the enterprise and negatively affects economic profit. As a tool that can increase a company’s competitive position in the market, the work explores the digital technology of in-depth analysis, that is Process Mining. This article aims to explore how businesses gain a competitive edge by using business process mining technology. It also examines the current trends in implementing process analytics across different industries. Process Mining technology is the focus of the study. The subject is the impact of business process mining technology on the competitiveness of a company. The study used theoretical and empirical scientific approaches, methods of statistical analysis, and systems analysis techniques. By analyzing competitive advantages, a correlation between the implementation of process analytics and the company’s increased efficiency was established. The most popular areas of application of Process Mining are procurement, customer service and information technology. The leaders in the number of successful implementations of business process analysis technology are the financial services, telecommunications, and oil and gas industries.

High-precision FBG-based sensor for soil settlement monitoring: A comparative study with magnetic settlement gauges and PIV technique

The primary objective of layered settlement monitoring is to detect subsidence issues in soil or foundational structures, in order to implement appropriate measures for ensuring the safety and stability of engineering projects. Conventional monitoring techniques have problems such as errors in manual readings, high costs, susceptibility to interference, and low levels of automation. A layered soil settlement sensor based on fiber Bragg grating (FBG) technology is proposed in this study. This sensor utilizes an equal-strength beam as an "intermediate bridge" to make the FBG sensor respond. The sensor is prepared by the laboratory's 3D fused deposition modelling (FDM) and calibrated by applying displacement loads through a fine-tuning platform. The sensitivity coefficient is 0.068 nm/mm, and the correlation coefficient exceeds 0.99. Finally, the sensor exhibited high precision when compared to measurements obtained from magnetic settlement gauges and particle image velocimetry (PIV). The discrepancies between the measurement outcomes of the sensor and the actual values of soil settlement does not exceed 3.33%.

Research on Belt Deviation Fault Detection Technology of Belt Conveyors Based on Machine Vision

Traditional belt deflection detection devices for underground belt conveyors in coal mines have problems, such as their single function, poor fault location and analysis accuracy, low automation level, and low reliability. In order to solve the defects of traditional detection devices, the belt deviation faults of the underground belt conveyor transport process require to be detected effectively and reliably. This paper proposes a belt deviation detection method based on machine vision. This method makes use of a global adaptive high dynamic range imaging method to complete the brightness enhancement processing of the underground image. Then the straight-line features of the conveyor belt edges are extracted using Canny edge detection and the Hough transform algorithm. In addition, a dual-baseline localization judgment method is proposed to realize the identification of band bias faults. Finally, a test bench for belt conveyor deviation was built. Testing experiments for different deviations were conducted. The accuracy of the tape deviation detection reached 99.45%. The method proposed in this study improves the reliability of belt deviation fault detection of underground belt conveyors in coal mines and has wide application prospects in the field of coal mining.

Intelligent dustbin based on convolution neural network, spectroscopy and internet of things

Aiming at the problems of low level of intelligent automation of garbage classification, low flexibility, low efficiency, high cost and difficulty in large-scale production, this paper proposes a multimodal data fusion intelligent garbage bin system based on neural network, spectral technology and ultrasonic technology, which can effectively classify and recover different types of garbage. This paper mainly includes the following aspects: (1) The accuracy of MobileNetV2 was improved to 76.3% by using an experimental research method; (2) The near-infrared spectrum was studied by literature study and simulation method, and the characteristic wavelength reflectance ratio was used to establish the model; (3) The literature research method and quantitative analysis method were used to judge the types of solid and liquid waste according to the cavitation effect; (4) Integrate the above three technologies and design the corresponding intelligent trash can model, so that the system can perform multimodal data fusion according to the items put by users, jointly determine their categories and assign them to the corresponding categories of trash cans.

Design and Experimental Testing of an Overhead Rail Automatic Variable-Distance Targeted Spray System for Solar Greenhouses

Crop cultivation in solar greenhouses is affected by issues such as low levels of automation in spraying machinery, inefficient spraying, and a lack of suitable spraying equipment for vertically cultivated crops, all of which are in urgent need of resolution. To address these problems, this paper proposes a suspended-rail automatic variable-distance targeted spray system. The rated working speed of the spray system is 0.3 m/s, and the rated working pressure is 0.3 MPa. This system achieves precise spraying of crops at varying heights by dynamically adjusting the position of the spray nozzle. To ensure accuracy in spraying, the system employs a laser ranging sensor for real-time measurement of crop positions and spraying distances. Combined with a parameter processing scheme, the system generates control signals to adjust the operation of the electric push rod and electromagnetic valve, thereby dynamically adjusting the spraying distance and timing. Using vertically cultivated potted peppers as experimental subjects, this study compares the performance of fixed- and variable-distance spraying modes. The results indicate that, compared to the fixed-distance mode, the automatic variable-distance mode increases pesticide adherence by 16.65% and reduces pesticide usage by 29.58%. The proposed suspended-rail automatic variable-distance targeted spray system offers an effective technical solution for the precise spraying of vertically cultivated crops in solar greenhouses and thus contributes to improved pesticide utilization efficiency, reduced pesticide residue, and lower environmental pollution.

FBANet: Transfer Learning for Depression Recognition Using a Feature-Enhanced Bi-Level Attention Network.

The House-Tree-Person (HTP) sketch test is a psychological analysis technique designed to assess the mental health status of test subjects. Nowadays, there are mature methods for the recognition of depression using the HTP sketch test. However, existing works primarily rely on manual analysis of drawing features, which has the drawbacks of strong subjectivity and low automation. Only a small number of works automatically recognize depression using machine learning and deep learning methods, but their complex data preprocessing pipelines and multi-stage computational processes indicate a relatively low level of automation. To overcome the above issues, we present a novel deep learning-based one-stage approach for depression recognition in HTP sketches, which has a simple data preprocessing pipeline and calculation process with a high accuracy rate. In terms of data, we use a hand-drawn HTP sketch dataset, which contains drawings of normal people and patients with depression. In the model aspect, we design a novel network called Feature-Enhanced Bi-Level Attention Network (FBANet), which contains feature enhancement and bi-level attention modules. Due to the limited size of the collected data, transfer learning is employed, where the model is pre-trained on a large-scale sketch dataset and fine-tuned on the HTP sketch dataset. On the HTP sketch dataset, utilizing cross-validation, FBANet achieves a maximum accuracy of 99.07% on the validation dataset, with an average accuracy of 97.71%, outperforming traditional classification models and previous works. In summary, the proposed FBANet, after pre-training, demonstrates superior performance on the HTP sketch dataset and is expected to be a method for the auxiliary diagnosis of depression.

Comprehensive dynamic numerical simulation of cooling process for a 1.3GHz 9-cell superconducting cavity based on different structures

As an indispensable component of X-ray free electron lasers and future ring electron-positron colliders, a 1.3 GHz 9-cell superconducting cavity provides higher acceleration voltage and higher RF power per unit length, and saves equipment space. During testing and operation, superconducting cavities often need to be cooled gradually from the ambient temperature (300 K) to the superconducting temperature (4.5 K or less). To avoid plastic deformation of materials and seal leaking caused by high thermal stress, the cooling rates in each stage and the temperature differences on the cavity must be properly controlled. Currently, a less effective manual control strategy is typically used, whose main limitations include a low level of automation, a high reliance on the experience of the operating personnel, and a fairly lengthy time due to low efficiency. In this paper, a 1.3 GHz 9-cell superconducting cavity and its helium vessel were taken as the research subjects, six different mechanical structures of the helium vessel were designed, and six different three-dimensional thermal-flow coupling models were established. For each structural model, the temperature distribution and cooling strategy were analyzed using fluid numerical simulation software. The influence of inlet parameters on the cooling process was analyzed, and the appropriate mechanical structure model was recommended. The results showed that: During the cooling process, the closer the superconducting cavity is to the inlet, the lower the temperature is. The cooling rate is faster in the early stage in which the temperature difference can be reduced by 50 % within the first 30 min. After experimental test verification, the total cooling time is about 650 min (10.8 h or so), and the deviation between the simulation results and the experimental test results is less than 7 %, which shows the simulation results can well predict the actual cooling process for 1.3 GHz 9-cell superconducting cavity. The research work in this paper can lay a good foundation for the further R&D status of the superconducting cavity.