Automata Research Articles

Diseminasi dan Akselerasi Percepatan Produksi Jamur Tiram Melalui Studi Kasus Mesin Press Baglog Otomatis di UD Zaida Jamur Tiram

The program for implementing this activity aims to accelerate the production of oyster mushrooms at UD Zaida Oyster Mushrooms through dissemination and implementation of an automatic baglog press machine. This activity was carried out in collaboration between a team from the university and UD Zaida, integrating modern technology into traditional production processes. Through a series of training and workshops, implementation partners can operate machines effectively and understand optimal production management. The results of this activity show a significant increase in production capacity, where UD Zaida was able to increase oyster mushroom production output and reduce production waste. This program has also succeeded in increasing the skills and knowledge of workers, while providing a positive impact on the local economy through increasing income and employment opportunities. The dissemination of automatic baglog press machines has proven to be a sustainable solution that drives efficiency and productivity in the mushroom farming sector. This implementation activity recommends expanding the use of this technology to other regions as an effort to support the growth of the oyster mushroom industry at the national level.

Machine vision system for automatic defect detection of ultrasound probes

Industry 4.0 conceptualizes the automation of processes through the introduction of technologies such as artificial intelligence and advanced robotics, resulting in a significant production improvement. Detecting defects in the production process, predicting mechanical malfunctions in the assembly line, and identifying defects of the final product are just a few examples of applications of these technologies. In this context, this work focuses on the detection of ultrasound probes’ surface defects, with a focus on Esaote S.p.A.’s production line probes. To date, this control is performed manually and therefore biased by many factors such as surface morphology, color, size of the defect, and by lighting conditions (which can cause reflections preventing detection). To overcome these shortfalls, this work proposes a fully automatic machine vision system for surface acquisition of ultrasound probes coupled with an automated defect detection system that leverage artificial intelligence. The paper addresses two crucial steps: (i) the development of the acquisition system (i.e., selection of the acquisition device, analysis of the illumination system, and design of the camera handling system); (ii) the analysis of neural network models for defect detection and classification by comparing three possible solutions (i.e., MMSD-Net, ResNet, EfficientNet). The results suggest that the developed system has the potential to be used as a defect detection tool in the production line (full image acquisition cycle takes ~ 200 s), with the best detection accuracy obtained with the EfficientNet model being 98.63% and a classification accuracy of 81.90%.

Smartphone application for improving self-awareness of adherence to edoxaban treatment: a randomised controlled trial (ADHERE-APP)

Abstract Background Adherence to oral anticoagulants is essential for stroke prevention in patients with atrial fibrillation (AF). This study aims to test the feasibility and effectiveness of the smartphone app-based intervention to improve drug adherence in patients with AF. Methods This prospective, randomised, open-label, multicentre trial (ADHERE-APP) enrolled a total of 502 patients at 9 university hospitals in South Korea from November 2021 to January 2023. Patients with AF aged ≥19 years requiring oral anticoagulation,and were treated with edoxaban were enrolled. Patients were randomized in a 1:1 ratio to the conventional treatment group and the app conditional feedback group. Patients in the app conditional feedback group used a smartphone application that pushed alarms for taking medication and measuring blood pressure (BP) and heart rate (HR) at specific times of the day. In addition to medication alarms, the habit of measuring one’s BP and HR could improve drug adherence by increasing self-awareness of the disease. The automatic BP machine was connected to the smartphone via Bluetooth and the measured BP and HR were automatically updated on the smartphone app. The primary endpoint was adherence to edoxaban as measured by pill count at 12 and 24 weeks. In addition, we evaluated the differential effect of the smartphone application by age on the primary endpoint. Results Among the 502 patients, the mean age was 66.0±10.0 years and 67.7% were male. Of the 372 patients who completed 12-weeks trial, edoxaban adherence at 12 weeks was high in both arms, with no difference in the primary outcome (96.5% [92.5%-100.0%] vs. 98.0% [93.2%-99.5%], P = 0.488). or in secondary outcomes, including the proportion of patients achieving adherence ≥ 80% or 95%. In patients aged >65 years, adherence to edoxaban was higher in the app conditional feedback group than in the conventional treatment group (95.8% [93.0%-99.5%] vs. 98.0% [95.8%-99.5%], P = 0.049). Conclusions Overall adherence to edoxaban was much higher than expected in patients with AF. There was no difference in edoxaban adherence between the app conditional feedback group and the conventional treatment group, however, in older patients, app feedback increased medication adherence.

Advanced Analysis of Scalability and Mobility in Cloud Servers using Logistic Regression Models.

With the rapid expansion of cloud computing, efficiently managing scalability and mobility has become paramount. This paper presents an in-depth examination of how logistic regression models can predict and optimize these two critical aspects of cloud infrastructure. By analyzing various cloud configurations, workload patterns, and system performance data, the logistic regression framework forecasts resource needs and identifies potential bottlenecks. The study incorporates real-world applications and case studies to demonstrate the practical benefits of logistic regression in enhancing cloud performance. Additionally, the paper compares logistic regression with other predictive models and explores future advancements in cloud optimization through AI-driven automation and real-time machine learning. The findings underscore the importance of predictive analytics in maintaining seamless cloud operations and provide actionable insights for cloud architects aiming to design more resilient and efficient cloud systems.

Mitigating Microfiber Pollution in Laundry Wastewater: Insights from a Filtration System Case Study in Galle, Sri Lanka

Synthetic fibers are widely used in daily life due to their durability, elasticity, low cost, and ease of use. The textile industry is the primary source of synthetic microfibers, as these materials are mostly used in production processes. Globally, plastic pollution has been identified as a major environmental threat in this era, since plastics are not degradable but break down into smaller particles such as mesoplastics, microplastics, and microfibers. Synthetic microfiber pollution is a significant issue in aquatic ecosystems, including oceans and rivers, with laundry wastewater being a major source. This problem is particularly pressing in cities like Galle, Sri Lanka, where numerous tourist hotels are located. Despite the urgency, there has been a lack of scientific and systematic analysis to fully understand the extent of the issue. This study addresses this gap by analyzing the generation of microfibers from laundry activities at a selected hotel and evaluating the efficiency of a laundry wastewater filtration system. This study focused on a fully automatic front-loading washing machine (23 kg capacity) with a load of 12 kg of polyester–cotton blend serviettes (black and red). Samples (1 L each) were taken from both treated and untreated wastewater during four wash cycles, with a total of 100 L of water used for the process. The samples were filtered through a 100 μm sieve and catalytic wet oxidation along with density separation were employed to extract the microfibers, which were then collected on a membrane filter paper (0.45 μm). Microfibers were observed and analyzed for shapes, colors and sizes under a stereo microscope. Results revealed that untreated laundry wastewater contained 10,028.7 ± 1420.8 microfibers per liter (n = 4), while treated wastewater samples recorded 191.5 ± 109.4 microfibers per liter (n = 4). Most of the microfibers observed were black and white/transparent colors. Further analysis revealed that 1 kg of polyester–cotton blend fabric can generate 336,833 microfibers per wash, which was reduced to 6367 microfibers after treatment. The filtration unit recorded an impressive efficiency of 98.09%, indicating a remarkably high capacity for removing microfibers from wastewater. These findings highlight the potential of such filtration techniques to significantly reduce microfiber emissions from laundry wastewater, presenting a promising approach to mitigating environmental pollution from microfibers.

Design and Testing of a Seedling Pick-Up Device for a Facility Tomato Automatic Transplanting Machine.

At present, tomato transplanting in facility agriculture is mainly manual operation. In an attempt to resolve the problems of high labor intensity and low efficiency of manual operation, this paper designs a clip stem automatic transplanting and seedling picking device based on the yolov5 algorithm. First of all, through the study of the characteristics of tomato seedlings of different seedling ages, the age of tomato seedlings suitable for transplanting was obtained. Secondly, the improved yolov5 algorithm was used to determine the position and shape of tomato seedlings. By adding a lightweight upsampling operator (CARAFE) and an improved loss function, the feature extraction ability and detection speed of tomato seedling stems were improved. The accuracy of the improved yolov5 algorithm reached 92.6%, and mAP_0.5 reached 95.4%. Finally, the seedling verification test was carried out with tomato seedlings of about 40 days old. The test results show that the damage rate of the device is 7.2%, and the success rate is not less than 90.3%. This study can provide a reference for research into automatic transplanting machines.

Design and Test of Seedling-Picking Mechanism of Fully Automatic Transplanting Machine

The seedling retrieval mechanism is a crucial component of fully automatic transplanting machines, significantly influencing the quality, reliability, and efficiency of the transplanting process. Nonetheless, the existing seedling retrieval mechanisms in current transplanting machines exhibit several deficiencies, including substantial damage to seedlings and inadequate retrieval accuracy. To overcome these challenges, we propose an integrated approach combining pneumatic and mechanical techniques to further improve performance. By employing a lower thimble elevation and clamping mechanism, alongside a mathematical model based on the seedling removal process, this method ensures precise seedling extraction and minimizes damage to the root system and substrate. The novelty of this study lies in its ability to reduce the adhesion between seedlings and the holes of the plug plate, thereby minimizing non-destructive extraction of the seedlings and preserving the integrity of the matrix, which is essential for ensuring healthy seedling growth. Moreover, the optimization of the seedling retrieval trajectory enhances the accuracy of the seedling retrieval mechanism while also meeting the requisite speed requirements. Experimental results indicate that at a rate of 72 seedlings per minute, the extraction success rate reached 94.90%, and the casting success rate was 98.53%. The seedling injury rate was only 1.95%, resulting in an overall success rate of 91.69%. These findings confirm that the device meets operational efficiency requirements and delivers effective performance.

Comparison of Pre-Defined Automatic Machine Learning (AutoML) for MBTI Personality Prediction of Twitter Users using Binary Classification Approach

The Myers-Briggs Type Indicator (MBTI) is a personality test that is globally accepted and used as a method for identifying personality. MBTI uses a four-factor linear model to characterize a person's behaviour patterns. This feature is often used to pursue career opportunities, make decisions, manage leadership, and deal with stress. In particular, MBTI personality prediction has been widely conducted and well performed using Recurrent Neural Network (RNN) based on Twitter data because it indirectly reveals most of a person's personality through their tweets. However, deep understanding is needed in building RNN-based solutions. Hence it will take a lot of time and resources to produce an excellent model architecture and the parameters used. Therefore, this study proposed the Auto Machine Learning (AutoML) method with a pre-defined search space to determine the correct model architecture and hyperparameters based on the results of data analysis. Thus, the search algorithm can exploit environments with suitable configurations in general. There are two pre-defined search spaces employed in this study, i.e. (i) two RNN algorithms, including Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), and (ii) pre-trained Word2Ves as word embedding. In addition, this study compares the model's performance that employs preprocessing and raw data (without preprocessing). The first result shows that the preprocessing increases the F1-Score values for LSTM and GRU by 2.35% and 2.02%, respectively. Subsequently, the LSTM outperformed GRU by the values of F1-Score at 0.35% and accuracy at 0.76%. The implementation of LSTM with pre-processed data in pre-defined AutoML with Word2Vec as a word embedding technique can provide good performance on long and complex data sequences such as Twitter data for predicting its user personality.

Visual Anomaly Detection via CNN-BiLSTM Network with Knit Feature Sequence for Floating-Yarn Stacking during the High-Speed Sweater Knitting Process

In order to meet the current expanding market demand for knitwear, high-speed automatic knitting machines with “one-line knit to shape” capability are widely used. However, the frequent emergence of floating-yarn stacking anomalies during the high-speed knitting process will seriously hinder the normal reciprocating motion of the needles and cause a catastrophic fracture of the whole machine needle plate, greatly affecting the efficiency of the knitting machines. To overcome the limitations of the existing physical-probe detection method, in this work, we propose a visual floating-yarn anomaly recognition framework based on a CNN-BiLSTM network with the knit feature sequence (CNN-BiLSTM-KFS), which is a unique sequence of knitting yarn positions depending on the knitting status. The sequence of knitting characteristics contains the head speed, the number of rows, and the head movements of the automatic knitting machine, enabling the model to achieve more accurate and efficient floating-yarn identification in complex knitting structures by utilizing contextual information from knitting programs. Compared to the traditional probe inspection method, the framework is highly versatile as it does not need to be adjusted to the specifics of the automatic knitting machine during the production process. The recognition model is trained at the design and sampling stages, and the resulting model can be applied to different automatic knitting machines to recognize floating yarns occurring in various knitting structures. The experimental results show that the improved network spends 75% less time than the probe-based detection, has a higher overall average detection accuracy of 93% compared to the original network, and responds faster to floating yarn anomalies. The as-proposed CNN-BiLSTM-KFS floating-yarn visual detection method not only enhances the reliability of floating-yarn anomaly detection, but also reduces the time and cost required for production adjustments. The results of this study will bring significant improvements in the field of automatic floating-yarn detection and have the potential to promote the application of smart technologies in the knitting industry.

Forecasting severe respiratory disease hospitalizations using machine learning algorithms

BackgroundForecasting models predicting trends in hospitalization rates have the potential to inform hospital management during seasonal epidemics of respiratory diseases and the associated surges caused by acute hospital admissions. Hospital bed requirements for elective surgery could be better planned if it were possible to foresee upcoming peaks in severe respiratory illness admissions. Forecasting models can also guide the use of intervention strategies to decrease the spread of respiratory pathogens and thus prevent local health system overload. In this study, we explore the capability of forecasting models to predict the number of hospital admissions in Auckland, New Zealand, within a three-week time horizon. Furthermore, we evaluate probabilistic forecasts and the impact on model performance when integrating laboratory data describing the circulation of respiratory viruses.MethodsThe dataset used for this exploration results from active hospital surveillance, in which the World Health Organization Severe Acute Respiratory Infection (SARI) case definition was consistently used. This research nurse-led surveillance has been implemented in two public hospitals in Auckland and provides a systematic laboratory testing of SARI patients for nine respiratory viruses, including influenza, respiratory syncytial virus, and rhinovirus. The forecasting strategies used comprise automatic machine learning, one of the most recent generative pre-trained transformers, and established artificial neural network algorithms capable of univariate and multivariate forecasting.ResultsWe found that machine learning models compute more accurate forecasts in comparison to naïve seasonal models. Furthermore, we analyzed the impact of reducing the temporal resolution of forecasts, which decreased the model error of point forecasts and made probabilistic forecasting more reliable. An additional analysis that used the laboratory data revealed strong season-to-season variations in the incidence of respiratory viruses and how this correlates with total hospitalization cases. These variations could explain why it was not possible to improve forecasts by integrating this data.ConclusionsActive SARI surveillance and consistent data collection over time enable these data to be used to predict hospital bed utilization. These findings show the potential of machine learning as support for informing systems for proactive hospital management.

AUTOMATIC MACHINE TOOL ALIGNMENT ON ITS FOUNDATION

This article deals with the issue of the automatic setting of machines on their base. The focus is on machines with a self-supporting frame, freestanding, on more than three supports. The alignment uses the previously presented method of precise and repeatable machine placement on the foundation. As a result, the machine has the necessary parameters in terms of its geometry, and at the same time a defined weight distribution on the individual building elements is guaranteed. The paper presents a new method for automatic machine alignment using so-called “smart feet”. Due to this, the process of machine alignment is clearly defined, repeatable and does not depend on the experience of the personnel performing the alignment. The presented algorithm makes it possible to automate the entire machine-setting process. The article includes the presentation of a machine placement on its base, which is used for testing and verification of the functionality of the alignment algorithm.

Method for weaving basket using eco-friendly materials in industrial production

The trend of using eco-friendly items as substitutes for products made from plastic and synthetic leather is becoming increasingly popular with the desire to contribute to reducing environmental pollution. There are many biodegradable materials applied to make baskets as alternatives to plastic or leather products such as bamboo, lemongrass, pandan leaves, etc. However, the basket weaving process has been mainly done by manual or semi-automation. As a result, its productivity is significantly low, and the quality of the product is inconsistent. Thus, the need for an automatic basket-weaving machine is essential, this paper presents a prototype of a basket-weaving machine. The dimensions of the baskets are 150-200 mm in diameter and 100-150 mm in height, respectively. The proposed method produces 30-50 products per day which is three times as many as the manual, and simultaneously, enhances the working conditions of the laborers.•A novel machine is developed to automatically weave baskets using eco-friendly materials.•The proposed method is to improve the productivity of the basket manufacturing process.•The proposed machine would promote eco-friendly baskets and reduce plastic waste.

Development of automatic CNC machine with versatile applications in art, design, and engineering

The area of computer numerical control (CNC) machines has grown fast, and their use has risen significantly in recent years. This article presents the design and development of a CNC writing machine that uses an Arduino, a motor driver, a stepper motor, and a servo motor. The machine is meant to create 2D designs and write in numerous input languages using 3-axis simultaneous interpolated operations. The suggested machine is low-cost, simple to build, and can be operated with merely G codes. The performance of the CNC writing machine was assessed by testing it on a range of solid surfaces, including paper, cardboard, and wood. The results reveal that the machine can generate high-quality text and images with great accuracy and consistency. The proposed machine's ability to write in several input languages makes it appropriate for various applications, including art, design, and engineering.

Spatio-temporal change and driving mechanisms of land use/cover in Qarhan Salt Lake area during from 2000 to 2020, based on machine learning

The significance of land use classification has garnered attention due to its implications for climate and ecosystems. This paper establishes a connection by introducing and applying automatic machine learning (Auto ML) techniques to salt lake landscape, with a specific focus on the Qarhan Salt Lake area. Utilizing Landsat-5 Thematic Mappe (TM) and Landsat-8 Operational Land Imager (OLI) imagery, six machine learning algorithms were employed to classify eight land use types from 2000 to 2020. Results show that XGBLD performed optimally with 77% accuracy. Over two decades, salt fields, construction land, and water areas increased due to transformations in saline land and salt flats. The exposed lakes area exhibited a rise followed by a decline, mainly transforming into salt flats. Agricultural land areas slightly increased, influenced by both human activities and climate. Our analysis reveals a strong correlation between salt fields and precipitation, while exposed lakes demonstrate a significant negative correlation with evaporation and temperature, highlighting their vulnerability to climate change. Additionally, human water usage was identified as a significant factor impacting land use change, emphasizing the dual influence of anthropogenic activities and natural factors. This paper addresses the void in the application of Auto ML in salt lake environments and provides valuable insights into the dynamic evolution of land use types in the Qarhan Salt Lake region.

Analysis of Machine Performance with Calculations Overall Equipment Effetiveness on Packing Line Automation Machine

Analysis of Machine Performance with Calculations Overall Equipment Effetiveness on Packing Line Automation Machine

Quantitative assessment of brain structural abnormalities in children with autism spectrum disorder based on artificial intelligence automatic brain segmentation technology and machine learning methods

Rationale and objectivesTo explore the characteristics of brain structure in Chinese children with autism spectrum disorder (ASD) using artificial intelligence automatic brain segmentation technique, and to diagnose children with ASD using machine learning (ML) methods in combination with structural magnetic resonance imaging (sMRI) features. MethodsA total of 60 ASD children and 48 age- and sex-matched typically developing (TD) children were prospectively enrolled from January 2023 to April 2024. All subjects were scanned using 3D-T1 sequences. Automated brain segmentation techniques were utilized to obtain the standardized volume of each brain structure (the ratio of the absolute volume of brain structure to the whole brain volume). The standardized volumes of each brain structure in the two groups were statistically compared, and the volume data of brain areas with significant differences were combined with ML methods to diagnose and predict ASD patients. ResultsCompared with the TD group, the volumes of the right lateral orbitofrontal cortex, right medial orbitofrontal cortex, right pars opercularis, right pars triangularis, left hippocampus, bilateral parahippocampal gyrus, left fusiform gyrus, right superior temporal gyrus, bilateral insula, bilateral inferior parietal cortex, right precuneus cortex, bilateral putamen, left pallidum, and right thalamus were significantly increased in the ASD group (P< 0.05). Among six ML algorithms, support vector machine (SVM) and adaboost (AB) had better performance in differentiating subjects with ASD from those TD children, with their average area under curve (AUC) reaching 0.91 and 0.92, respectively. ConclusionAutomatic brain segmentation technology based on artificial intelligence can rapidly and directly measure and display the volume of brain structures in children with autism spectrum disorder and typically developing children. Children with ASD show abnormalities in multiple brain structures, and when paired with sMRI features, ML algorithms perform well in the diagnosis of ASD.

Battery Analysis in Off Grid Solar Power Plant as A Power Source for AC Motor Automatic Coffee Roaster Machine Capacity 20 Kg

Solar power plants that utilize solar energy to be converted into electrical energy have several advantages compared to other power plants, namely that they do not produce air pollution, are available continuously and are available everywhere. Coffee is a commodity in the world that is cultivated in more than 50 countries. Two species of coffee trees that are generally known are Robusta Coffee and Arabica Coffee. Processing coffee before consumption goes through a long process, namely from harvesting coffee beans that are ready to harvest. Then proceed with drying before roasting. Coffee roasting machine that uses an AC motor. The motor gets a DC power source from a solar power plant then the inverter changes the DC voltage to AC. This coffee roasting machine is equipped with IoT to control the motor rotation remotely and monitor the temperature. The battery has the function of storing electrical energy produced by solar panels in the form of direct current energy. The energy stored in the battery functions as a backup, which is usually used when the solar panels do not produce electrical energy. The greater the capacity of the battery used, the longer the battery can back up the load used.

Precise Servo-Control System of a Dual-Axis Positioning Tray Conveying Device for Automatic Transplanting Machine

Precise Servo-Control System of a Dual-Axis Positioning Tray Conveying Device for Automatic Transplanting Machine

Prediction of spatial heterogeneity in nutrient-limited sub-tropical maize yield: Implications for precision management in the eastern Indo-Gangetic Plains

Knowledge of the factors influencing nutrient-limited subtropical maize yield and subsequent prediction is crucial for effective nutrient management, maximizing profitability, ensuring food security, and promoting environmental sustainability. We analyzed data from nutrient omission plot trials (NOPTs) conducted in 324 farmers' fields across ten agroecological zones (AEZs) in the Eastern Indo-Gangetic Plains (EIGP) of Bangladesh to explain maize yield variability and identify variables controlling nutrient-limited yields. An additive main effect and multiplicative interaction (AMMI) model was used to explain maize yield variability with nutrient addition. Interpretable machine learning (ML) algorithms in automatic machine learning (AutoML) frameworks were subsequently used to predict attainable yield relative nutrient-limited yield (RY) and to rank variables that control RY. The stack-ensemble model was identified as the best-performing model for predicting RYs of N, P, and Zn. In contrast, deep learning outperformed all base learners for predicting RYK. The best model's square errors (RMSEs) were 0.122, 0.105, 0.123, and 0.104 for RYN, RYP, RYK, and RYZn, respectively. The permutation-based feature importance technique identified soil pH as the most critical variable controlling RYN and RYP. The RYK showed lower in the eastern longitudinal direction. Soil N and Zn were associated with RYZn. The predicted median RY of N, P, K, and Zn, representing average soil fertility, was 0.51, 0.84, 0.87, and 0.97, accounting for 44, 54, 54, and 48% upland dry season crop area of Bangladesh, respectively. Efforts are needed to update databases cataloging variability in land type inundation classes, soil characteristics, and INS and combine them with farmers' crop management information to develop more precise nutrient guidelines for maize in the EIGP.

Analisis Manajemen Risiko Kecelakaan Kerja Pada Mesin Automatic Bar Bending dengan Metode Hazard Identification, Risk Assessment And Determining Control (HIRADC) Pada PT Faza Jaya Pratama

Work accidents in the construction industry are often related to the operation of machinery, including the complex Automatic Bar Bending machine. This study aims to analyze the risk management of work accidents on Automatic Bar Bending machines using the Hazard Identification, Risk Assessment and Determining Control (HIRADC) approach at PT Faza Jaya Pratama. The research method includes hazard identification, risk assessment, and determination of effective controls to reduce the risk of accidents. The results showed that the Automatic Bar Bending machine has several potential hazards that can result in serious accidents if not handled appropriately. The risk evaluation provided a deeper understanding of the level of risk associated with the machine operation, while the implementation of appropriate controls was able to significantly reduce the risk. The implication of these findings is the need for the implementation of a comprehensive risk management strategy to improve occupational safety and health in the construction industry work environment.