Automatic System Research Articles

THERMODYNAMIC IMPERATIVES THE MODERNIZATION OF THE ELECTRIC DRIVE OF PUMP UNITS OF THE CENTRALIZED HEAT SUPPLY NETWORK IN THE CONTEXT OF INCREASEING ENERGY EFFICIENCY

The relationship between thermodynamic and hydraulic parameters of the heat supply network has been established. The dependence of the electric power consumed by the electric drive of the pump unit on the pressure and supply of the coolant was determined. It is proved that the regulated electric drive system has advantages over the non-regulated electric drive system in the context of energy efficiency of the pumping unit and the district heating network in general. Approaches and criteria for the synthesis of a regulated electric drive system, including its automatic control system, were determined, regulation methods were analyzed, and the most acceptable version of the electric drive was chosen: a controlled frequency converter - an asynchronous motor with a short-circuited rotor, taking into account the above, an energy-saving mechanical system of a centralized heat supply network was synthesized. The peculiarity of this system is that the protection of parameters of hydraulic and temperature regimes in the network of centralized heat supply is ensured with the help of extreme automatic control systems. At the same time, the assignment of mode parameters is provided by the temperature controller of the heat exchange point of the network. Regulation of the pressure and productivity of the pumping unit is carried out by changing, according to a certain law, the frequency and power supply of the asynchronous motor.

Estimating Lung Volume Capacity from X-ray Images Using Deep Learning

Estimating lung volume capacity is crucial in clinical medicine, especially in disease diagnostics. However, the existing estimation methods are complex and expensive, which require experts to handle and consequently are more error-prone and time-consuming. Thus, developing an automatic measurement system without a human operator that is less prone to human error and, thus, more accurate has always been a prerequisite. The limitation of radiation dose and various medical conditions in technologies like computed tomography was also the primary concern in the past. Although qualitative prediction of lung volume may be a trivial task, designing clinically relevant and automated methods that effectively incorporate imaging data is a challenging problem. This paper proposes a novel multi-tasking-based automatic lung volume estimation method using deep learning that jointly learns segmentation and regression of volume estimation. The two networks, namely, segmentation and regression networks, are sequentially operated with some shared layers. The segmentation network segments the X-ray images, whose output is regressed by the regression network to determine the final lung volume. Besides, the dataset used in the proposed method is collected from three different secondary sources. The experimental results show that the proposed multi-tasking approach performs better than the individual networks. Further analysis of the multi-tasking approach with two different networks, namely, UNet and HRNet, shows that the network with HRNet performs better than the network with UNet with less volume estimation mean square error of 0.0010.

A new automatic sugarcane seed cutting machine based on internet of things technology and RGB color sensor.

Egypt is among the world's largest producers of sugarcane. This crop is of great economic importance in the country, as it serves as a primary source of sugar, a vital strategic material. The pre-cutting planting mode is the most used technique for cultivating sugarcane in Egypt. However, this method is plagued by several issues that adversely affect the quality of the crop. A proposed solution to these problems is the implementation of a sugarcane-seed-cutting device, which incorporates automatic identification technology for optimal efficiency. The aim is to enhance the cutting quality and efficiency of the pre-cutting planting mode of sugarcane. The developed machine consists of a feeding system, a node scanning and detection system, a node cutting system, a sugarcane seed counting and monitoring system, and a control system. The current research aims to study the pulse widths (PW) of three-color channels (R, G, and B) of the RGB color sensors under laboratory conditions. The output PW of red, green, and blue channel values were recorded at three color types for hand-colored nodes [black, red, and blue], three speeds of the feeding system [7.5 m/min, 5 m/min, and 4.3 m/min], three installing heights of the RGB color sensors [2.0 cm, 3.0 cm, and 4.0 cm], and three widths of the colored line [10.0 mm, 7.0 mm, and 3.0 mm]. The laboratory test results s to identify hand-colored sugarcane nodes showed that the recognition rate ranged from 95% to 100% and the average scanning time ranged from 1.0 s to 1.75 s. The capacity of the developed machine ranged up to 1200 seeds per hour. The highest performance of the developed machine was 100% when using hand-colored sugarcane stalks with a 10 mm blue color line and installing the RGB color sensor at 2.0 cm in height, as well as increasing the speed of the feeding system to 7.5 m/min. The use of IoT and RGB color sensors has made it possible to get analytical indicators like those achieved with other automatic systems for cutting sugar cane seeds without requiring the use of computers or expensive, fast industrial cameras for image processing.

Identifying depression-related topics in smartphone-collected free-response speech recordings using an automatic speech recognition system and a deep learning topic model

BackgroundPrior research has associated spoken language use with depression, yet studies often involve small or non-clinical samples and face challenges in the manual transcription of speech. This paper aimed to automatically identify depression-related topics in speech recordings collected from clinical samples. MethodsThe data included 3919 English free-response speech recordings collected via smartphones from 265 participants with a depression history. We transcribed speech recordings via automatic speech recognition (Whisper tool, OpenAI) and identified principal topics from transcriptions using a deep learning topic model (BERTopic). To identify depression risk topics and understand the context, we compared participants' depression severity and behavioral (extracted from wearable devices) and linguistic (extracted from transcribed texts) characteristics across identified topics. ResultsFrom the 29 topics identified, we identified 6 risk topics for depression: ‘No Expectations’, ‘Sleep’, ‘Mental Therapy’, ‘Haircut’, ‘Studying’, and ‘Coursework’. Participants mentioning depression risk topics exhibited higher sleep variability, later sleep onset, and fewer daily steps and used fewer words, more negative language, and fewer leisure-related words in their speech recordings. LimitationsOur findings were derived from a depressed cohort with a specific speech task, potentially limiting the generalizability to non-clinical populations or other speech tasks. Additionally, some topics had small sample sizes, necessitating further validation in larger datasets. ConclusionThis study demonstrates that specific speech topics can indicate depression severity. The employed data-driven workflow provides a practical approach for analyzing large-scale speech data collected from real-world settings.

A novel algorithm for ship characteristic points extraction based on density clustering

With the widespread application of ship Automatic Identification System (AIS) in maritime operations, a large number of ship trajectories become available. This study aims to improve the safety of ships navigating through densely trafficked areas and address the challenge of sufficient data exploration while fully describing the traffic conditions in these waters. To achieve this objective, traffic flow information is extracted from AIS data collected in Zhoushan waters. A combination of multi-algorithms is employed to extract the traffic flow frame, specifically, the Douglas-Peucker compression algorithm and trajectory intersection algorithm are utilised to identify the characteristic points of ship trajectories. Subsequently, a density clustering algorithm is applied to extract the three types of characteristic points: compressed trajectory points, intersection points, and ship position points, facilitating data mining efforts. The resulting initial traffic flow characteristic points are then subject to weighted fusion, followed by image superposition processing to create an overlapping map of the ship trajectories. This process culminates in the generation of a traffic flow frame for the region. The framework integrates various track characteristic points, offering insights into the distribution of essential routes in the vicinity waters, thereby providing a comprehensive depiction of ship traffic flow patterns. The proposed framework can be applied to the route planning and serve as a reference to the maritime authorities when selecting recommended shipping lanes.

Modelling Asphalt Overlay As-Built Roughness Based on Profile Transformation-Case for Paver Using Automatic Levelling System.

The as-built roughness, or smoothness obtained during pavement construction, plays an important role in road engineering since it serves as an indicator for both the level of service provided to users and the overall standard of construction quality. Being able to predict as-built roughness is therefore important for supporting pavement design and management decision making. An as-built IRI prediction model for asphalt overlays based on profile transformation was proposed in a previous study. The model, used as basis for this work, was developed for the case of wheeled pavers without automatic screed levelling. This study presents further development of the base prediction model, including the use of an automatic screed control system through a long-distance averaging mobile reference. Formulation of linear systems that constitute the model are presented for the case of a wheeled paver using contactless acoustic sensors set-up over a floating levelling beam attached to the paver. To calibrate the model, longitudinal profile data from the Long-Term Pavement Performance SPS-5 experiment was used, obtaining a mean error of 0.17 m/km for the predicted IRI. The results obtained demonstrate the potential of the proposed approach as a modelling alternative.

Моделирование платформы Гью – Стюарта, размещенной на мобильном объекте, в обстановке внешних возмущений

Problems that arise when creating automatic stabilization systems located on mobile objects are being solved. The advantages and disadvantages of known types of automatic stabilization systems are noted. The Hugh – Stewart platform is one of the devices used in these systems. The article presents the results of mathematical modeling of the Hugh – Stewart platform located on a mobile object, in an environment of external disturbances. This situation is typical for a platform located on a ship, constantly exposed to unpredictable disturbances in the water environment. The Hugh – Stewart platform consists of six hydraulic cylinders, some ends of which are connected directly to the deck of the ship, and the other ends of the hydraulic cylinders are connected to the platform for installing equipment, instruments, devices, whose position in space must be stable. Stabilization of the Hugh – Stewart platform is ensured by changing the length of six hydraulic cylinders according to certain algorithms to ensure the stability of the automatic stabilization system. Mathematical modeling of the Hugh – Stewart platform is complicated not only by the fact that the rotational interaction of six interconnected solid bodies (hydraulic cylinders) platform components – must be taken into account, but also by the need to take into account the influence of unknown disturbances on the platform (when modeling the Hugh – Stewart platform located on a ship, it is necessary to take into account the influence of unpredictable water (sea, ocean) disturbances). The article constructs a kinematic model of the Hugh – Stewart platform, substantiates the choice of sliding modes when implementing hydraulic cylinder control algorithms to stabilize the platform, and proposes adaptation of these modes based on neural networks to take into account the influence of unknown disturbances. Simulation results in the MATLAB software environment prove the effectiveness of the proposed models and control algorithms of the Hugh – Stewart platform.

Positioning possibilities for human geographies of the sea: Automatic Identification Systems and its role in spatialising understandings of shipping

AbstractThis paper positions possibilities for human geographies of the sea. The growing volume of work under this banner has been largely qualitative in its approach, reflecting, in turn, the questions posed by oceanic scholars. These questions necessitate corresponding methods. Whilst this is not necessarily a problem, and the current corpus of work has offered many significant contributions, in making sense of the human dimensions of maritime worlds, other questions—and methods—may generate knowledge that is useful within this remit of work. This paper considers the place of quantitative approaches in posing lines of enquiry about shipping, one of the prominent areas of concern under the banner of ‘human geographies of the seas’. There is longstanding work in transport geographies concerned with shipping, logistics, freight movement and global connections, which embraces quantitative methods which could be bridged to ask fresh questions about oceanic spatial phenomena past and present. This paper reviews the state of the art of human geographies of the sea and transport geographies and navigates how the former field may be stimulated by some of the interests of the latter and a broader range of questions about society‐sea‐space relations. The paper focuses on Automatic Identification Systems (or AIS) as a potentially useful tool for connecting debates, and deepening spatial understandings of the seas and shipping beyond current scholarship. To advance the argument the example of shipping layups is used to illustrate or rather, position, the point.

A novel method for extracting near-misses from AIS data involving collision avoidance behaviour

This paper presents a novel method for extracting near-misses involving collision avoidance behaviour based on automatic identification system data. Previous methods for near-misses extraction primarily focused on detecting collision risks from ship-to-ship encounter scenarios. The proposed method introduces avoidance behaviour as an additional factor for extracting near-misses. It further analyzes the subject of avoidance behaviour through the avoidance intention, aiming to extract near-misses where one ship's avoidance behaviour is prompted by another ship within the same ship-to-ship scenario. The method consists of three primary stages: (1) Detection of encounter scenarios with collision risk using the velocity obstacle method; (2) Identification of encounter scenarios with maneuvering behaviour based on course data; (3) Recognition of near-misses involving avoidance behaviour by estimating avoidance intention. Finally, the effectiveness and innovativeness of the proposed method are validated through a comparative experiment, and the influence of various parameters is thoroughly analyzed in an applied experiment.

Анализ электромагнитных процессов блока возбуждения трехкаскадного стартер-генератора для авиационной энергосистемы

This article discusses the excitation system of a three-stage synchronous generator for aviation use in two distinctive modes of operation: as an electric power generator for an on-board power supply system and an electric starter for gas turbine engines of the aircraft. The article presents the calculation of the starter and generator operating modes of the excitation system. The basic design relations for the semiconductor converter circuit have been obtained and confirmed by simulation modeling in the PowerSIM environment. The problem of synthesizing an automatic control system for the TSG excitation unit operating as part of an on-board power supply system using a method for calculating the parameters of regulators based on the motion separation method has been solved. A two-circuit control system has been created for the generator mode of operation, including PI and PID regulators with output voltage regulation of the main generating stage. To control the excitation unit in the electric star¬ter mode of operation, a single-circuit control system for the current of the excitation winding with the addition of a resonant component has been designed. The evaluation of the efficiency of the designed control systems was carried out on the basis of simulation modeling using PowerSIM and MATLAB/Simulink application software packages for a generator feeding a three-phase AC network in accordance with the requirements of GOST R 54073-2017.The results of mathematical modeling formed the basis for the design of an experimental sample of the power unit in a hybrid integrated design for the TSG voltage regulation system.

Changes in the Arctic Traffic Occupancy and Their Connection to Sea Ice Conditions from 2015 to 2020

Arctic shipping activities are increasing in the context of sea ice decline. However, research gaps persist in studying recent Arctic shipping activities across various vessel types and their connection with sea ice conditions. Utilizing Automatic Identification System (AIS) data and sea ice satellite observations between 2015 and 2020, these matters are delved into this study. A discernible overall growth trend in Arctic traffic occupancy occurs from 2015 to 2020 during summer and autumn. Excluding passenger ships, the traffic occupancy trend for each ship type closely parallels that for all ships. Variations in traffic occupancy along the Northeast Passage dominate that in the entire Arctic. As sea ice diminishes, both Arctic traffic occupancy and its variability noticeably increase. Further examination of the relationship between shipping activities and ice conditions reveals that increased traffic occupancy corresponds significantly to diminishing sea ice extent, and the constraint imposed by sea ice on Arctic traffic occupancy weakens, while the 6-year AIS data could lead to uncertainties. In summary, as the Arctic sea ice declines continuously, not only sea ice but also additional social, military, and environmental factors constraining marine activities should be considered in the future operation of Arctic shipping.

Study of Subjective Answer Evaluation using Natural Language Processing and Machine Learning

Now a days, as we are moving towards automation there is a need for an automatic subjective answer evaluation system. Manual evaluation is a time and energy-consuming task. Currently, the online answer evaluation is available for mcq based question, hence evaluation of the theory answer is hectic for the checker. Evaluator manually checks the answer and allot the marks. Sometimes quality of evaluation may change according to mood of evaluator.The current system takes more manpower and time to evaluate the answer. It will be very helpful for educational institutions if the process of evaluation of descriptive answers is automated to capably assess student’s exam answer sheets. The system will evaluate the answer based upon the number of words and number of letters from extracted text from the pre-processed data

Система автономного управления дорожно-строительной машиной

The increasing requirements for the accuracy of construction work lead to the need to create modern control systems that will eliminate the human operator from the process of controlling road construction machines, increase the energy efficiency and productivity of these machines, as well as the quality of the finished construction object. The use of unmanned control systems has become an absolute technological break-through. Improving the accuracy of work with the help of unmanned control systems is an urgent and complex area of research. This is due to the need to improve algorithms and improve sensors to more accurately de-termine the coordinates of the machine and make decisions. Only by possessing high accuracy, modern unmanned vehicles are able to minimize errors in construction work and associated safety risks. To do this, it is necessary to provide the ability to recognize and analyze various factors, such as moving objects, weather conditions, terrain, etc. The article presents the results of a functional analysis of a number of road construction machines, defines the range of tasks that a modern unmanned control system should solve, describes a possible implementation option for such a system, and shows the differences in flowcharts of working processes of construction machines both without automatic control systems and equipped with standard and developed automatic control systems.

CONSERVE CONVENTIONAL ENERGY RESOURCES BY SMART CITY LIGHTS

Our conventional energy resources are being rigorously used day by day. If the same practice goes on there will be no time that it will get extinct and probably, it will be all history to our future generation. Proposed methodology “CONSERVE CONVENTIONAL ENERGY RESOURCES BY SMART CITY LIGHTS”. This proposed system will consume very less electricity as the lighting of the area will be such a way that, it will work productively efficient throughout. In any city, street light is one of the major power consuming factors, most of times we see street light are on even after sunrise thus wasting lot of energy. To avoid this problem of energy wastage, this proposed methodology has automatic street light control base system with the super combination of IoT. This is less power consuming and security base system. The main perspective of this proposed system is to save the electricity. Key Words - Conventional Energy, MTBF, MTTR, Odd-Even, Display Reader, Regenerative Speed Breaker

The application of deep learning in autonomous driving

Autonomous driving technology is currently a globally prominent subject, with its relevance and impact readily apparent. Leveraging advanced sensors, sophisticated algorithms, and state-of-the-art computer vision techniques, autonomous vehicles can autonomously navigate, mitigate traffic accidents, and alleviate urban congestion. Furthermore, this technology is poised to accelerate innovation within the automotive sector, drive industrial advancement, and enhance people's convenience and safety in their travel experiences. The importance of autonomous driving technology is not only reflected in its own advantages, but also in its ability to lead the development direction of intelligent transportation in the future, helping to promote the development of intelligent transportation systems, realize the intelligent interconnection between vehicles and vehicles, vehicles and road infrastructure, and further enhance the safety and convenience of traffic travel. Therefore, autonomous driving technology is a significant innovation that will bring a better and more convenient future for mankind. In this study, a method based on sensor internal and external parameter calibration transformation matrix, road target detection algorithm, automatic vehicle detection method, pedestrian intention prediction technology and automatic pedestrian recognition system are analyzed, which are applied to object detection and obstacle avoidance. This article provides a good overview of the field of autonomous driving.

REKONFIGURASI 26 KANAL IRIGASI MENGGUNAKAN METODE BINARY FIREFLY ALGORITHMS (BFA)

Reconfiguration of irrigation water channels on agricultural land aims to meet the water needs of rice fields. In its use, irrigation water must be used optimally. An automatic irrigation system is needed to provide water to plants in the desired conditions. In the irrigation control system, a servo motor simulates the control system using the Matlab 2013b software programme. Control simulations applied to irrigation prototypes are used to increase efficiency and determine the amount of irrigation water needed to regulate irrigation water distribution according to needs. As a controller comparison, an artificial intelligence method is used for controller tuning based on Binary Firefly Algorithm (BFA). The farthest water discharge results can be reduced in the reconfiguration model using the BFA method. So reconfiguring the network with BFA can reduce water discharge losses by 43.4538% of the water discharge losses from before

Detection of artichoke on seedling based on YOLOV5 model

Robotic systems have become essential in the industrial field today. Robotic systems used in many areas of industry enable the development of mechanization of agriculture. Researches in recent years have focused on the introduction of automatic systems and robot prototypes in the field of agriculture in order to reduce production costs. The developed smart harvest robots are systems that can work uninterrupted for hours and guarantee minimum cost and high production. The main element of these systems is the determination of the location of the product to be harvested by image processing. In addition to the programs used for image processing, deep learning models have become popular today. Deep learning techniques offer high accuracy in analyzing and processing agricultural data. Due to this feature, the use of deep learning techniques in agriculture is becoming increasingly widespread. During the harvest of the artichoke, its head should generally be cut off with one or two leaves. One main head and usually two side heads occur from one shoot. Harvest maturity degree is the time when the heads reach 2/3 of their size, depending on the variety character. In this study, classification was made by using the deep learning method, considering the head size of the fruit. YOLOv5 (nano-small-medium and large models) was used for the deep learning method. All metric values ​​of the models were examined. It was observed that the most successful model was the model trained with the YOLOv5n algorithm, 640x640 sized images with 20 Batch, 90 Epoch. Model values ​​results were examined as “metrics/precision”, “metrics/recall”, “metrics/mAP_0.5” and “metrics/mAP_0.5:0.95”. These are key metrics that measure the detection success of a model and indicate the performance of the relevant model on the validation dataset. It was determined that the metric data of the “YOLOv5 nano” model was higher compared to other models. The measured value was Model 1= Size: 640x640, Batch: 20, Epoch: 90, Algorithm: YOLOv5n. Hence, it was understood that “Model 1” was the best detection model to be used in separating artichokes from branches in robotic artichoke harvesting.

Ferritin level in sickle cell patients at the sickle cell referral center of Lomé in 2022

Aim: Major sickle cell sufferers residing in tropical regions find themselves at the crossroads of several situations that can modify their iron status. Our aim was to describe ferritin levels in sickle cell patients at Centre National de Recherches et de Soins aux Drépanocytaires (CNRSD) of Lomé. Methods: This was a cross-sectional study of sickle cell patients seen in medical consultations from May 16 to July 15, 2022. We included all sickle cell patients with anemia, who were seen in a medical monitoring during the study period and who gave their free consent. MINDRAY automatic system BC 6000, Mini-Vidas automatic system from BIOMERIEUX, and Minicap Flex Piercing from SEBIA were used to perform hemograms, ferritin levels, protidograms, and hemoglobin electrophoresis, respectively. The variables studied were patients’ medical history, age, sex, hematimetric indices, ferritin levels, hemoglobin fractions, and protidograms. We used Statistical Package for the Social Sciences (SPSS) software to analyze data. Results: 117 patients with a mean age of 15.42±11.11 years were included. The sex ratio M/F was 0.98. Ferritin levels was below 15 ng/mL in 4 (3.42% of cases) and below 30 ng/mL in an inflammatory context in 7 patients (5.98%). Median ferritin level was 178.37 ng/mL; [Q1=59.39; Q3=411.35]. SS patients were most frequent (75.21%). The mean hemoglobin level of patients was 8.10±1.21 g/dL. Non-microcytic anemia was most frequent (57.30%). Conclusion: Elevated ferritin levels were more frequent than iron deficiency in sickle cell patients at the CNRSD of Lomé. Thus, any prescription of iron should be motivated by proof of the existence of iron deficiency.

The Development of Automatic Sprinkler System Concepts for Maritime Vehicle Carriers

AbstractClosed ro-ro spaces on maritime vehicle carriers are usually protected by a total-flooding carbon dioxide system. Such systems have many benefits, for example that there are no residues that can adversely affect the protected objects (in this case thousands of vehicles) and the agent is electrically non-conductive. However, there could be a considerable time delay from the start of a fire until the carbon dioxide system is discharged. Experience has shown that this delay time can cause significant fire damage and jeopardize the performance of the system. Within the EU funded LASH FIRE project, design and installation guidelines for supplementary automatic water-based fire sprinkler systems were developed. An important design feature is that the system automatically activates at an early stage of a fire. This would allow more time to fight the fire manually or to safely evacuate the space and discharge the CO2 system when the fire is controlled to one or a few vehicles instead of at a time when it has escalated in size. The work was partly based on a comprehensive literature review that identified relevant standards and information applicable to the design of automatic fire sprinkler and deluge water spray systems. Large-scale fire tests verified that the suggested system designs were able to provide control of realistic vehicle fires, including fires in passenger cars and a freight truck.

Your Career Path Matters in Person-Job Fit

We are again confronted with one of the most vexing aspects of the advancement of technology: automation and AI technology cause the devaluation of human labor, resulting in unemployment. With this background, automatic person-job fit systems are promising solutions to promote the employment rate. The purpose of person-job fit is to calculate a matching score between the job seeker's resume and the job posting, determining whether the job seeker is suitable for the position. In this paper, we propose a new approach to person-job fit that characterizes the hidden preference derived from the job seeker's career path. We categorize and utilize three types of preferences in the career path: consistency, likeness, and continuity. We prove that understanding the career path enables us to provide more appropriate career suggestions to job seekers. To demonstrate the practical value of our proposed model, we conduct extensive experiments on real-world data extracted from an online recruitment platform and then present detailed cases to show how the career path matter in person-job fit.