Automatic System Research Articles

Survey of farm, parlour and milking management, parlour technologies, SCC control strategies and farmer demographics on Irish dairy farms

BackgroundThis cross-sectional study describes a survey designed to fill knowledge gaps regarding farm management practices, parlour management practices and implemented technologies, milking management practices, somatic cell count (SCC) control strategies, farmer demographics and attitudes around SCC management on a sample of Irish dairy farms.ResultsWe categorized 376 complete responses by herd size quartile and calving pattern. The average respondent herd was 131 cows with most (82.2%) operating a seasonal calving system. The median monthly bulk tank somatic cell count for seasonal calving systems was 137,000 cells/ml (range 20,000 – 1,269,000 cells/ml), 170,000 cells/ml for split-calving systems (range 46,000 – 644,000 cells/ml) and 186,000 cells/ml for ‘other’ herds (range 20,000 – 664,000 cells/ml). The most common parlour types were swing-over herringbones (59.1%) and herringbones with recording jars (22.2%). The average number of units across herringbone parlours was 15, 49 in rotary parlours and two boxes on automatic milking system (AMS) farms. The most common parlour technologies were in-parlour feeding systems (84.5%), automatic washers on the bulk tank (72.8%), automatic cluster removers (57.9%), and entrance or exit gates controlled from the parlour pit (52.2%). Veterinary professionals, farming colleagues and processor milk quality advisors were the most commonly utilised sources of advice for SCC management (by 76.9%, 50.0% and 39.2% of respondents respectively).ConclusionsIn this study, we successfully utilised a national survey to quantify farm management practices, parlour management practices and technology adoption levels, milking management practices, SCC control strategies and farmer demographics on 376 dairy farms in the Republic of Ireland. Rotary and AMS parlours had the most parlour technologies of any parlour type. Technology add-ons were generally less prevalent on farms with smaller herds. Despite finding areas for improvement with regard to frequency of liner changes, glove-wearing practices and engagement with bacteriology of milk samples, we also found evidence of high levels of documentation of mastitis treatments and high use of post-milking teat disinfection. We discovered that Irish dairy farmers are relatively content in their careers but face pressures regarding changes to the legislation around prudent antimicrobial use in their herds.

IOT based Smart Plant Monitoring System and Control System

The world is now fully dependent on the internet and the Internet of Things (IoT), which is the inter-networking of physical devices embedded with electronics, sensors, software and network connectivity. IoT enables physical devices to exchange data with the manufacturer and achieve greater value and services. Agriculture is the backbone of many countries, but water scarcity is a major issue in this field. To overcome this problem, the irrigation process can be automated using IoT. By using sensors, the temperature, humidity, and light can be measured, and appropriate processing can be performed based on the outcome. This helps in reducing water wastage and improving accuracy, economic benefits, and efficiency while reducing human intervention. This paper highlights the importance of IoT in day-to-day life, its scope in the future, and its potential applications in farming methods. To demonstrate the use of IoT in agriculture, we present the Automatic Watering System, which monitors and maintains the moisture content in the soil using temperature, humidity, and moisture sensors. The Arduino UNO microcontroller is used as the control unit, regulating the appropriate amount of water to avoid over/under irrigation. Keywords – sensors, temperature, humidity, soil moisture, Arduino uno

FABRICATION AND ANALYSIS OF AUTOMATIC LINEAR DISTANCE ADJUSTER

The Automatic linear distance adjustment system tailored for robotics arms, capitalizing on ultrasonic sensor technology. The system's design, implementation, and evaluation are comprehensively explored, highlighting its ability to facilitate precise and autonomous adjustments of linear positions within the robotic arm's operational envelope. Real-time feedback mechanisms are integrated, enabling dynamic regulation of the distance between the end-effector and target objects or surfaces. This ensures optimal positioning and operational efficiency during various tasks such as pick-and-place operations, assembly processes, and object manipulation. By reducing reliance on manual adjustments, the system enhances automation and augments robotics arms' capabilities across diverse industrial and research applications. The seamless integration of the proposed system empowers robotics arms with heightened flexibility, accuracy, and adaptability, thereby fostering advancements in robotic automation and control. This innovation holds promise for streamlining manufacturing processes, enhancing productivity, and facilitating intricate tasks that demand precision and efficiency in robotic operations. Through rigorous evaluation and validation, the system demonstrates its potential to revolutionize robotic arm functionalities and pave the way for future developments in automation technologies Keywords: Arduino Uno , Ultrasonic sensor,Stepper Motor, Linear guide rail .

PEDESTRIAN ALERT SYSTEM USING DEEP LEARNING

Pedestrian detection is an essential and significant task in any intelligent video surveillance system, as it provides the fundamental information for semantic understanding of the video footages. It has an obvious extension to automotive applications due to the potential for improving safety systems. Many car manufacturers (e.g., Volvo, Ford, GM, Nissan) offer this as an ADAS option in 2017. It is used in many cars and vehicles for automatic driver assistance systems. But when the ADAS system detects pedestrians but if fails to apply brakes on time accidents might happened. To overcome this situation our project provides an audio alert to the human passengers or driver in the vehicle so that they can apply manual brakes. In this project we will require Python libraries such as Pytorch and OpenCV. Pytorch is a widely used Machine Learning library. It is popular for the YOLO (You Only Look Once) algorithm which is built for Object Detection, we are using the YOLO algorithm and customize it to detect objects on a pedestrian dataset. Python has a library pyttsx3, that is capable to convert text-to-speech offline. Itextracts the label from the detected pedestrians in the video and converts the text label into speech. The algorithm detects the pedestrians when they are in a close approach to the vehicles and sends the identifies labels to the pyttsx3 speaker engine. An audio alert is generated by the engine and alerts the passengers or drivers. Keywords - YOLO, Pytorch, Pyttsx3, Object Detection, Python.

Automated Fall Detection for Disabled Individuals Using Mobile Phone Sensors and Machine Learning: A Survey

Fall risks to health and safety are especially dangerous for those with impairments. An automated fall detection system is necessary, especially in medical and senior care. The elderly and individuals with impairments are particularly susceptible to falls, which frequently result in severe injuries and complications, thereby presenting a considerable threat to their overall health. The early discovery and response to a fall incidence can reduce immobilization and consequent health complications, saving lives. Automatic fall detection systems quickly and reliably indicate falls and dispatch medical or emergency assistance. Researchers have introduced various automatic fall detection methods using machines or deep learning. Most fall detection systems depend on wearable or stationary sensors, which restricts the user's mobility and accessibility. Conversely, mobile sensor-based fall detection leverages the widespread presence of smartphones by obtaining motion information via their integrated accelerometers and gyroscopes. Our primary objective is to develop a reliable fall detection method using a mobile phone sensor and machine learning. This paper examines several methods employed in the identification of falls and emphasizes the significance of utilizing mobile phone sensors in the process of fall detection. It also discusses recent research in this domain and highlights research challenges. This could potentially foster further innovation in the field.

Automated H2O2 monitoring during photo-Fenton processes using an Arduino self-assembled automatic system

A cheap and easy to use Arduino self-assembled automatic system was employed to continuously monitor the hydrogen peroxide consumption during the photo-Fenton degradation of caffeine, selected as model target compound. The automatic system made it possible to measure the H2O2 concentration in the reaction cell via a colorimetric reaction and to take samples for HPLC analysis minimising the operator manual intervention and exposure to UV radiation. The obtained results were compared in terms of LOD and LOQ with H2O2 measurements manually performed using UV-Vis spectrophotometry, evidencing better analytical performance when using the automatic system; LOD and LOQ were respectively 0.032mM and 0.106mM for the automatic system against 0.064mM and 0.213mM for UV-Vis spectrophotometry. Furthermore, the photo-Fenton treatment was optimised by means of a Design of Experiments (DoE) investigating the effect of added H2O2 concentration, iron concentration and caffeine initial concentration on system performances. The use of the automatic device for such monitoring provided several advantages: automation (with consequent reduction of the workload), measurement increased precision, reduced reagents consumption and waste production in agreement with the principles of Green Analytical Chemistry.

CAAC: An effective reinforcement learning algorithm for sparse reward in automatic control systems

CAAC: An effective reinforcement learning algorithm for sparse reward in automatic control systems

Design and Implementation of an Automated Disaster-Recovery System for a Kubernetes Cluster Using LSTM

With the increasing importance of data in modern business environments, effective data management and protection strategies are gaining increasing research attention. Data protection in a cloud environment is crucial for safeguarding information assets and maintaining sustainable services. This study introduces a system structure that integrates Kubernetes management platforms with backup and restoration tools. This system is designed to immediately detect disasters and automatically recover applications from another Kubernetes cluster. The experimental results show that this system executes the restoration process within 15 s without human intervention, enabling rapid recovery. This, in turn, significantly reduces the potential for delays and errors compared to manual recovery processes, thereby enhancing data management and recovery efficiency in cloud environments. Moreover, our research model predicts the CPU utilization of the cluster using Long Short-Term Memory (LSTM). The necessity of scheduling through this predict is made clearer through comparison with experiments without scheduling, demonstrating its ability to prevent performance degradation. This research highlights the efficiency and necessity of automatic recovery systems in cloud environments, setting a new direction for future research.

A comparison of five methods to maximize RNA and DNA isolation yield from adipose tissue.

Adipose tissue in the human body occurs in various forms with different functions. It is an energy store, a complex endocrine organ, and a source of cells used in medicine. Many molecular analyses require the isolation of nucleic acids, which can cause some difficulties connected with the large amount of lipids in adipocytes. Ribonucleic acid isolation is particularly challenging due to its low stability and easy degradation by ribonucleases. The study aimed to compare and evaluate five RNA and DNA isolation methods from adipose tissue. The tested material was subcutaneous porcine adipose tissue subjected to different homogenization methods and RNA or DNA purification. A mortar and liquid nitrogen or ceramic beads were used for homogenization. The organic extraction (TriPure Reagent), spin columns with silica-membrane (RNeasy Mini Kit or High Pure PCR Template Preparation Kit), and the automatic MagNA Pure system were used for the purification. Five combinations were compared for RNA and DNA isolation. Obtained samples were evaluated for quantity and quality. The methods were compared in terms of yield (according to tissue mass), purity (A260/280 and A260/230), and nucleic acid degradation (RNA Integrity Number, RIN; DNA Integrity Number, DIN). The results were analyzed statistically. The average RNA yield was highest in method I, which used homogenization with ceramic beads and organic extraction. Low RNA concentration didn't allow us to measure degradation for all samples in method III (homogenization with ceramic beads and spin-column purification). The highest RNA quality was achieved with method IV using homogenization in liquid nitrogen and spin column purification, which makes it the most effective for RNA isolation from adipose tissue. Required values of DNA yield, purity, and integrity were achieved only with spin column-based methods (III and IV). The most effective method for DNA isolation from adipose tissue is method III, using spin-columns without additional homogenization.

Optical measurement instrument for detection of powdery mildew and grey mould in protected crops

This study presents an initiative aimed at developing a real-time optical measurement system for non-contact measurement of airborne fungal spores in protected crops such as strawberries, tomatoes, and cucumbers. The system is based on a customized microscope and an automatic system for trapping fungal spores. It has been tested in the field under real conditions, and image processing algorithms have been developed to identify fungal spores in high-resolution microscope images.

Abstract PO2-29-02: Towards Precision Medicine in Breast Imaging: A Novel Open Mammography Database with Tailor-Made 3D Image Retrieval for Artificial Intelligence and Teaching

Abstract Objectives: According to the World Health Organization (WHO), breast cancer is the most frequent malignant neoplasia and leading cause of cancer death among women worldwide. Low- and middle-income countries hold the worst survival rates mainly owing to a lack of access to appropriate diagnosis and treatment related resources. For proper early diagnosis, it is established that besides the physical structure itself (e.g., mammography units), there's a need for adequate interpretation of imaging and that might be a particularly major problem in low-income societies once there is a tendency of greater education setbacks. Mammography datasets can improve this resource-driven gap by enabling the development of artificial intelligence technologies (AI) which can make breast cancer diagnosis more accurate in a cost-effective and scalable way. We aim to create a new database of high quality digital mammography images suitable for AI development and education. Methods: Our mammography database was developed by means of retrospective selection of 100 exams performed by Hospital São Paulo - Federal University of São Paulo ranging from 2019 to 2023. The project is assumed to be safe, versatile, and usable, and required an extensive search for the appropriate tool. Ambra Health, an American company, has developed cloud-based software for medical image management and stood out as a viable alternative. Their platform meets international data security criteria, they also made the intended careful customization possible, in addition to the possibility of associating image and text attachments. The categories were created in accordance with the BI-RADS® descriptors, a wide range of clinical scenarios and additional materials available, and they served as the basis for the advanced search feature, which intuitively filters exams that meet the selected criteria simultaneously. The platform was integrated with an automatic anonymization system upon upload, ensuring data privacy. After submission, the exams are retained in a restricted area for anonymization verification, categorization, and attachment management, before being released to the end-user. So as to broaden geographic coverage, the descriptors were entered in American English, respecting the origin of the BI-RADS® lexicon, as for the website structure, automatic translation to the accessing browser standard language was selected. Results: Our website is active and available at http://mamografia.unifesp.br, with access granted upon a simple registration process. 941 mammography images from 100 anonymized cases, 62% of which include 3D images, can be filtered based on the combination of 113 clinical and imaging variables, as well as attachment availability. The language is adaptable to the user's native language, and categorized searches can be accessed directly from the browser or downloaded as customized datasets. Additionally, features such as saved searches or starred exams are also available. Conclusion: We have developed an online and free mammography database that is completely innovative by integrating various resources into a single platform. We provide high-resolution and 3D digital images that can be searched using an advanced search system. Moreover, we offer supplementary clinical information in various attachment formats, favoring a rich clinical correlation. In this way, we have achieved the ambivalence of our goal, which was to promote education and research. *"images speak louder than words" Database: https://mamografia.unifesp.br Tutorials: https://www.youtube.com/@Mamografiaunifesp e-mail: acesso@mamografia.unifesp.br password: acesso@mamografia12 (valid until dec/23) Citation Format: Natália Cordeiro, Gil Facina, Afonso Nazário, Vanessa Sanvido, Joaquim Araujo Neto, Morgana Silva, Simone Elias. Towards Precision Medicine in Breast Imaging: A Novel Open Mammography Database with Tailor-Made 3D Image Retrieval for Artificial Intelligence and Teaching [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO2-29-02.

REAL TIME FACE ATTENDANCE SYSTEM USING DEEP LEARNING

One's face is a physical depiction of who they are. As a result, we have suggested a facial recognition- based automatic student attendance system. The usage of face recognition technology in everyday life is particularly beneficial for security control systems. Face recognition is used by the FBI (Federal Bureau of Investigation) for criminal investigations as well as the airport security system to identify criminals. In the first step of our suggested method, video framing, the camera is activated using a user-friendly interface. The Viola-Jones method is used to identify and segment the face ROI from the video frame. If necessary, image size scaling is done at the pre-processing step to prevent information loss. After using the median filter to reduce noise, color pictures are transformed into grayscale versions. Then, to improve the contrast of the pictures, contrast-limited adaptive histogram equalization (CLAHE) is applied. Principal component analysis (PCA) and improved local binary pattern (LBP) are used appropriately in the face identification stage to extract the characteristics from facial pictures. By minimizing the lighting impact and boosting identification rates, the improved local binary pattern outperforms the original local binary pattern in our suggested method. The features that were retrieved from the training pictures and the features that were extracted from the test images are then compared. The face pictures are then identified and categorized using the upgraded LBP, PCA, and algorithm that produced the best results. The acknowledged student's attendance will then be recorded and saved in the excel file. A warning will be sent if a student signs in more than once. The student who is not enrolled will also be allowed to register right away. When two photographs per person are trained, the average recognition accuracy is 100% for high-quality images, 94.12% for low-quality images, and 95.76% for the face database.

Novel sample double dilution calibration method for determination of lithium in biological samples using automatic flow system with in-syringe reaction

A novel sample double dilution calibration method (SDDCM) and an automatic flow system with in-syringe reaction and spectrophotometric detection were developed for determining lithium in biological samples. The method is based on the reaction of lithium with Thorin in an alkaline medium and the signal was measured at 480 nm. The reaction was performed simultaneously for both standards and samples in three syringes of the automatic flow system. The method was validated and successfully applied to the determination of lithium in synthetic and pharmaceutical samples, with results consistent with the ICP OES method. The novel calibration method, developed for the determination of lithium in biological samples, uses a sample with two dilution degrees. Using the method, the concentration of the analyte is determined by relating the signal for a less diluted sample to the calibration plot for a more diluted sample and vice versa. The implementation of the calibration method was facilitated by preparing solutions directly in the flow system. The use of two sample dilutions makes it possible to determine the analyte in the sample without preliminary preparation. Moreover, obtaining two results based on signals for a sample diluted to different degrees allows them to be verified for accuracy. The proposed approach was successfully verified by the determination of lithium in certified reference materials of blood serum and urine. Using the developed method lithium was determined within the concentration range of 0.06–1.5 mg L-1, with precision (CV, %) less than 6.7, and accuracy (RE, %) better than 6.9. The detection limit was 0.03 mg L-1.

Automatic early warning of rockbursts from microseismic events by learning the feature-augmented point cloud representation

This study proposes an automatic early warning system for rockbursts in deeply buried tunnels using microseismic data. A novel machine learning model is introduced, which is the first to treats the microseismic event data as a high-dimensional point cloud. The model utilizes a fast approximated convolution (FAC) method to effectively and efficiently learn features from the raw event data with small model sizes. The model is trained and tested using microseismic data from the Hanjiang-To-Weihe water diversion project. Two competing models, a time series model and a traditional threshold model, are also constructed for comparison. Results show that the proposed model achieves reliable early warnings for 90.1% of rockbursts in the tunnel. This study presents a new and feasible rockburst prediction method that can be used independently or as a supplement to current assessment and management approaches for both tunnel boring machines (TBMs) and drilling- and blasting-excavated tunnels.

Training of a Neural Network System in the Task of Detecting Blue Stains in a Sawmill Wood Inspection System

This article presents the operation of an automatic pine sawn timber inspection system, which was developed at the Woodinspector company and is offered commercially. The vision inspection system is used to detect various wood defects, including knots, blue stain, and mechanical damage caused by worms. A blue stain is a defect that is difficult to detect based on the color of the wood, because it can be easily confused with wood defects or dirt that do not impair its strength properties. In particular, the issues of detecting blue stain in wood, the use of artificial neural networks, and improving the operation of the system in production conditions are discussed in this article. While training the network, 400 boards, 4 m long, and their cross-sections of 100 × 25 [mm] were used and photographed using special scanners with laser illuminators from four sides. The test stages were carried out during an 8-hour workday at a sawmill (8224 m of material was scanned) on material with an average of 10% blue stain (every 10th board has more than 30% of its length stained blue). The final learning error was assessed based on defective boards detected by humans after the automatic selection stage. The system error for 5387 boards, 550 m long, which had blue staining that was not detected by the scanner (clean) was 0.4% (25 pieces from 5387), and 0.1 % in the case of 3412 boards, 610 mm long, on which there were no blue stains, but were wrongly classified (blue stain). For 6491 finger-joint boards (180–400 mm), 48 pieces were classified as class 1 (clean), but had a blue stain (48/6491 = 0.7%), and 28 pieces did not have a blue stain, but were classified as class 2 (28/3561 = 0.7%).

TransEBUS: The interpretation of endobronchial ultrasound image using hybrid transformer for differentiating malignant and benign mediastinal lesions

The purpose of this study is to establish a deep learning automatic assistance diagnosis system for benign and malignant classification of mediastinal lesions in endobronchial ultrasound (EBUS) images. EBUS images are in the form of video and contain multiple imaging modes. Different imaging modes and different frames can reflect the different characteristics of lesions. Compared with previous studies, the proposed model can efficiently extract and integrate the spatiotemporal relationships between different modes and does not require manual selection of representative frames. In recent years, Vision Transformer has received much attention in the field of computer vision. Combined with convolutional neural networks, hybrid transformers can also perform well on small datasets. This study designed a novel deep learning architecture based on hybrid transformer called TransEBUS. By adding learnable parameters in the temporal dimension, TransEBUS was able to extract spatiotemporal features from insufficient data. In addition, we designed a two-stream module to integrate information from three different imaging modes of EBUS. Furthermore, we applied contrastive learning when training TransEBUS, enabling it to learn discriminative representation of benign and malignant mediastinal lesions. The results show that TransEBUS achieved a diagnostic accuracy of 82% and an area under the curve of 0.8812 in the test dataset, outperforming other methods. It also shows that several models can improve performance by incorporating two-stream module. Our proposed system has shown its potential to help physicians distinguishing benign and malignant mediastinal lesions, thereby ensuring the accuracy of EBUS examination.

Identification of rheumatoid arthritis using bone image features with radial basis function neural networks

AbstractRheumatoid arthritis (RA) leads to the destruction, deformation, and loss of function and causes joint damages to 85% of patients. The detection of bone mineral density from traditional x‐ray images consumes more time and it is observer dependent which decreases the evaluation performance when RA is in its early stage. Therefore, it is necessary to develop an observer‐independent computer‐aided automatic analysis system for evaluating JS narrowing. An efficient RA detection system based on feed‐forward neural network is proposed in this article. Initially the dataset is preprocessed to remove blur, redundant data and nonlinearities in RA images using wiener filter in less computation time. The edge boundaries are detected using nonlinear partial differential equation as the texture features of bones has great impact on the system accuracy. The detection strategy is implemented by optimized gray‐level co‐occurrence matrix based on bone image features. The selected features are optimized by genetic algorithm to improve the classification performance. The images are then classified as inflamed and noninflamed by radial basis function neural network. The effectiveness of the proposed classification approach is verified in terms of accuracy, sensitivity, and specificity and compared with the conventional convolutional neural network, artificial neural network, and support vector machine classifiers. Among the several existing techniques, our method found to be much effective with a maximum accuracy of 98.5% as some novel approaches are proposed and also tends to possess more compatible than the present system. Our computerized rheumatoid arteries detection approach will give more precise and flawless consistency rate.

Integrating security in hazard analysis using STPA-Sec and GSPN: A case study of automatic emergency braking system

Integrating security in hazard analysis using STPA-Sec and GSPN: A case study of automatic emergency braking system

An ontology-based knowledge representation using OWL for Indonesian local regulations

<div class="WordSection1"><p>Inconsistencies in legislation can significantly hinder the effectiveness and efficiency of the central and local government administrations. The Indonesian government requires standard harmonization of each piece of legislation to prevent such problems. However, this process is often manual and requires the involvement of multiple experts with varying backgrounds. This leads to high resource expenses regarding human resources, cost, and time. To address this, a software system should be developed to detect potential disharmony among legislation. However, the system requires a well-constructed legislation conceptual model represented in an appropriate modeling language. This research aims to develop the Indonesian local regulation ontology in web ontology language (OWL), where no such ontology exists. The ontology was created using the Ontology Development 101 methodology and evaluated using competency questions and expert judgment approaches. The resulting ontology becomes a basis for developing an automatic recommendation system to detect potentially inconsistent legislation in future works.</p></div>

Car Washing System using raspberry Pi Pico as PLC

Abstract: Using the Raspberry Pi Pico microcontroller, we have designed and implemented an automatic vehicle wash system in this work. By automating steps including scrubbing, rinsing, drying, and applying soap, the system seeks to expedite the automobile washing process. We go over the Raspberry Pi Pico, actuators, and sensor hardware configuration in addition to the software architecture that controls the system. We also emphasize the advantages of utilizing Raspberry Pi Pico for this application, such as its affordability, portability, and versatility. The efficacy and dependability of the suggested automatic car wash system are shown by the experimental results.