Search

The speed of vehicles are increased in the recent days with advancement of engine technology. The increased speed resulting in many accidents and causing for loss of many lives. Especially in restricted areas like schools zones, etc. it is important to control the speed of vehicle for safety concern. The speed control systems available now uses rely, a static device that is inappropriate for use in high-alert circumstances. In this paper, an Internet of Things (IoT) based automatic speed control system is proposed, which works based on the maximum permitted speed limit in a given area. Drivers are given information regarding the speed limits of particular area to maintain the speed. With the use of a mobile GPS, the proposed system gathers the geographical location and the corresponding top speed restriction. The proposed system compares the speed of the vehicle and the site's maximum speed on a continual basis. A priceless life was lost as a result of a driving mistake among today's high-speed, accident-prone drivers (school zone, hills area, and highways). So, the sign boards at such locations indicates the speed to be maintained. This automated speed control system was developed includes the specific speed restriction, which is picked up by the sensor located inside the car. The automobile has an receiver, and the transmitters are placed near prohibited regions send the data to transmitter. Based on the received signal, the speed of the vehicle is reduced. [ABSTRACT FROM AUTHOR]

With the rapid development of artificial intelligence, basic tasks such as automatic sample classification and object detection of computers have also developed rapidly. How to obtain the required information quickly, accurately and comprehensively from massive information has become the research hotspots. However, traditional classification detection methods have some limitations, such as high dependence on feature extraction and model selection, which greatly reduces the detection efficiency and accuracy. Therefore, this paper proposes a deep learning algorithm for combinatorial optimization classification detection and analysis. Firstly, statistical theory is used to process the computer target sample information, and the indicators are divided automatic classification to reduce the factors in classification detection. Then, statistical theory automatically classifies computer target samples, forms an automated classification scheme, and comprehensively analyzes the classification detection results. The results show that the accuracy and classification detection time of the computer target sample automated classification of the deep learning algorithm are better than the traditional classification detection method under the condition that the processing standard is fixed. [ABSTRACT FROM AUTHOR]

In recent years, there is rise in various human-computer interaction interfaces and automatic text translation services and frameworks. In this paper, we conduct an up-to-date evaluation of commercial automatic speech recognition (ASR) software services by comparing two market leaders – Apple Siri and Google speech-to-text (STT) services. For the experiment, we select three diverse categories – conversational, scientific and fictional texts. We then record an English speaker to read those texts, pass the voice recordings to the evaluated ASR services and save the output as text files. In a real-world scenario, the recordings are never clear and that is why we do another set of experiments with artificially introduced noise of the recordings. Comparing all generated output texts to the original input, we calculate an evaluation metric, called word error rate (WER), which is a well-know and widely used indicator for measuring automatic speech-to-text accuracy. After analyzing the results, we found out that some STT services perform better on certain text categories. Finally, we discuss the results and draw conclusions. [ABSTRACT FROM AUTHOR]

With the rapid development of modern science and technology, people's lifestyle is gradually developing in the trend of intelligence and automation, and the emergence and development of smart homes is part of making life more comfortable. This research explores into the synergy between environmental awareness and automated control systems, aiming to create comfortable, sustainable, and energy-saving living environments. The study investigates various sensors, such as temperature, humidity, access control, and light sensors. By designing closed-loop systems with these sensors, different controllers like proportional-integral-derivative (PID) controllers, and other components, the lighting, heating, and cooling systems can automatically adjust due to changes in environmental conditions and user preferences through feedback signal of the system. These systems play a great role in the further development of smart homes, not only improve the quality of people's daily life, but also improve the safety and privacy of the living environment, and the wide application in different industries has also promoted the development of society. [ABSTRACT FROM AUTHOR]

Currently, disease recognition is mostly based on the use of modern technology. Image processing system is one of the technologies that can be used in the process of detecting various diseases. The aim of this research is to build a system that can be used to detect COVID-19 infections automatically using image processing. The Covid-19 disease in the human body has a specific area in the body, namely the lungs. Modern medical science is currently so advanced that X-ray machine can provide digital images of the lungs and with the help of image processing, these images can be identified as normal or infected with COVID-19. The classification method used is logistic regression using parameters of contrast, correlation, energy, homogeneity and entropy. The results show that the system built has succeeded in detecting normal lungs and lungs infected with Covid-19 with 94 percent accuracy [ABSTRACT FROM AUTHOR]

UAS applications are nowadays experiencing a tremendous development both in the civil and in military sector. One of the main issues for this kind of autonomous vehicles is induced by atmospheric turbulence, which may pose a severe problem especially for small size UAV. The present research extends previous investigations to a broader range of turbulence spectra and tests an innovative procedure based on Extended Kalman Filter (EKF) autotune for wind identification. [ABSTRACT FROM AUTHOR]

It has been demonstrated that a stabilizing gas-burning furnace's efficiency may be determined by the temperature of the combustion products, which rely on the amount of natural gas present, how it is used, how much pressure is applied to it, and how much oxygen is used. The ways to conserve gaseous fuel in industrial plant furnaces and furnaces are described. The technological process of fuel combustion in glass melting furnaces is proposed to be automatically controlled. [ABSTRACT FROM AUTHOR]

Based on modern information technology, an automated system for monitoring and controlling the processes of combustion and burning of fuel in the metallurgical, chemical, and oil refining industries has been developed using the example of the use of a ВН-60 gas detector equipped with an improved alarm sensor that converts the signal about the concentration of the analyzed gas in the ambient air into gas analyzer readings. For this purpose, a methodology has been developed for making managerial decisions on managing the process based on the results of analytical monitoring and gas analysis of the environment. The combustibility of natural gas (methane) was studied and the technical characteristics of the proposed gas detector were given. The structure, as well as the algorithmic and software of the automated system for monitoring the gaseous environment of industrial production, have been developed. [ABSTRACT FROM AUTHOR]

Rabbit breeders are frequently confronted with high levels of ammonia in urine and droppings. High levels of Ammonia reduce rabbit farming productivity and can even result in rabbit death. Breeders traditionally detect ammonia levels only through the sense of smell and are not always present in the cage 24 hours a day to monitor ammonia levels in the rabbit cage, so a tool is required that can detect, monitor, and control ammonia levels in the rabbit cage for 24 hours nonstop automatically to detect ammonia levels to avoid the death of rabbits at certain hours due to an increase in ammonia levels in the rabbit cage. In this study, a tool that can automatically monitor and control ammonia levels will be developed. The tool is constructed with a NodeMCU ESP8266 microcontroller and other components such as the MQ-135 sensor, Relay, and a DC Water pump connected to a hose and misting nozzle to spray EM4 liquid as a neutralizing ammonia level. The programming language used is C language with the Arduino IDE application. In this research, ammonia levels were classified using a fuzzy method as Artificial Intelligence into four categories: high, low, normal, and moderate. This method is also used to determine whether to turn on or off the pump. If the ammonia level exceeds a predetermined threshold of 20 ppm, the dc water pump will start. As an IoT implementation, the results of the sensor readings will be displayed on the android application and Google Sheets later so that ammonia levels can be monitored remotely. The test lasted 24 days, with different treatments applied to the sensor and rabbit feed every six days. The treatment is to evaluate the tool's performance in light of the fact that ammonia gas is easily carried by the wind and the research cage used is an open house type. The use of EM4 liquid and a DC water pump is thought to be more effective than the use of an exhaust fan for open cage. [ABSTRACT FROM AUTHOR]

Critically reviewed in the present paper is the principle of operation of a model family of eight-speed automatic transmissions manufactured by the company Aisin Warner. A comprehensive kinematic analysis of the gearbox design was conducted, along with a close examination of potential defects that may arise during operation. The underlying causes of these defects were identified and discussed in detail. Explored, further in the paper, is the functionality of solenoids in the valve body and outlined, additionally, are the methods for determining their serviceability. Probable defects expected to occur during operation are categorized into three groups. While some of these defects can be registered with a suitable electronic tester, not all of them can, indeed, be identified through this means. [ABSTRACT FROM AUTHOR]

Vibrational nonequilibrium of diatomic molecules resulting in non-Boltzmann distributions is a phenomenon commonly encountered in rapid hypersonic nozzle expansions. The usual assumption of different vibrational temperatures for different species cannot simulate such nonequilibrium behaviour. State-to-state models governed by a master equation describe the evolution of population number for all vibrational levels. These equations possess singularly perturbed characteristics and stable numerical integration must account for this stiffness. Linearly implicit, or Rosenbrock–Wanner (ROW), integration methods are inherently more stable than explicit methods and less computationally complex than implicit ones of equivalent order. One historical limitation of ROW methods is the requirement that the exact Jacobian of the evolution rate vector must be computed to achieve classical order of convergence. This may be analytically intractable for large systems. Automatic Differentiation (AD) provides a solution by algorithmically determining the exact derivatives, to working precision, based upon implementations of the rate vector. State-to-state simulations of carbon monoxide vibrational kinetics under the influence of sudden a reduction in transitional/rotational temperature using linearly implicit time integration with AD is presented. Computational performance of the ROW method is compared to second-order backward-difference integration. [ABSTRACT FROM AUTHOR]

This paper describes continuous automatic variable transmission (CAVT) for the bicycle as well as other industrial applications with some modifications. The transmission system is based on a centrifugal governor. Unlike other CVT systems, this transmission system is purely mechanical and doesn't require any electronic component for its work. Thus, it addresses the problems related to the durability of the electronic components in other CVT systems. The system uses a slotted cone and a belt drive for its work. This paper includes the complete design of the system by considering suitable constraints and also a mathematical model of the system for the bicycle was developed. The range of gear ratios has been calculated by formulating and developing a mathematical model and the results were compared with existing values of gear ratios. Also, the torque and power were calculated corresponding to pedal force as input. As per the calculations shown in the paper, it can be understood that the CAVT system is mechanically feasible. This work makes an attempt to lay the foundation for the standardized procedure to design a CAVT system. [ABSTRACT FROM AUTHOR]

The present paper discusses a parameterized PSpice model of a full-bridge resonant inverter operating above the resonant frequency and an analog behavioral model of a control system with automatic regulation of the root-mean-square value of the output current. The control system is self-oscillating and allows for regulating the output power of the inverter by changing the transistors' turn-on time. Functional dependencies for designing a resonant inverter operating above its resonant frequency are defined, and parameterized descriptions of resonant circuit elements are performed. The theoretical studies are proved by computer simulation using PSpice for TI. [ABSTRACT FROM AUTHOR]

Data is the building block of technological advancement and research papers are the abstract of new technological progress. In this paper numerous research paper abstract of different fieldare used to classifythem into subject categories. The classification model is an ensembled model which involves the algorithms like CNN, LSTM and Contextual Encoder. And CNN is not that much used in classification of research papers. This paper uses Semantic Interactive Learning Model for classification. This model archives a highest accuracy of 98%. Furthermore, there are many different use case of Semantic Interactive Learning Model like in SDGs proposed by UN, Server-end application to classify data into known categories and many more. [ABSTRACT FROM AUTHOR]

The proposed system's purpose is to replace gatekeeper-operated level crossing gates with a computerized railway gate. It has two advantages. It makes a compromise by lowering the amount of time the gate is kept shut, and it also offers security to avenue users by preventing accidents. By existing arrangement, once to instruct leaves the station, the stationmaster calls the gatekeeper to advise him of his arrival. When the person near the gate receives the command, he or she closes the gate. As a result, if the teacher is late for positive reasons, the gate will be closed for a long period, causing traffic congestion. The train approaching is seen using the sensor positioned near to the gate when the automated railway gate manager is hired at the crossing path. Later, in comparison to manually controlled gates, the time it takes to close is substantially less, reducing human work. Where the chances of an occurrence are higher and a trustworthy duty is necessary, this type of gate should be used. Because the method is spontaneous, all errors caused by the physical process are excluded. [ABSTRACT FROM AUTHOR]

Echocardiography has an important role to play in the diagnosis of cardiac disease, namely in the detection of intracardiac masses. In order to improve the diagnostic accuracy of echocardiography, a unique fully automated classification approach based on sparse representation is proposed. Before calculating the mass area, a region of interest is first sliced. Then, the speckle is removed while the underlying anatomical structure is preserved with the use of a cutting-edge global denoising technique. Next, the K-singular value decomposition and an updated active contour model are used to depict the mass's contour in relation to the atrial wall. Finally, a sparse representation classifier is used to the motion, boundary, and texture data in order to distinguish between the two masses. Ninety-seven echocardiogram sequences from clinical practice are gathered for analysis. [ABSTRACT FROM AUTHOR]

The present article is a combination of review and technical aspects related to automated garage door. Primarily, various aspects related to smart home applications that involves controlling a garage door are surveyed and investigated. Based on various research articles, a novel yet simplified methodology is proposed for controlling the automated control of garage door. The complete methodology is based on IoT application of Arduino microcontroller. The proposed methodology utilizes ultrasonic sensor to sense the approaching vehicle. As the vehicle approaches the ultrasonic sensor analyses the distance between the garage door and the vehicle. Accordingly, the garage door is opened. [ABSTRACT FROM AUTHOR]

Smart homes represent an emerging area of the Internet of Things (IoT), providing innovative solutions for improving comfort, safety and energy efficiency in residential environments. This article conducts a relatively comprehensive exploration of the design and implementation of environment sensing and automatic control systems in smart homes. The study begins with an in-depth look at environmental sensing technology, elucidating the critical role that sensors play in collecting data on various environmental parameters. Specifically, it details temperature and humidity sensing, light and brightness sensing, and motion and presence sensing, clarifying their deployment and integration in smart home systems. This paper takes a closer look at automation control system design, elucidating the architecture, control algorithms, decision-making systems, user interfaces and remote safety protocols. Additionally, it highlights the need to address security and privacy concerns to ensure the integrity of these systems. The discussion extended into technical challenges and solutions, revealing the complexities of data processing and analysis, as well as those surrounding communications and interconnections. Solutions include data filtering, edge computing and standardized communication protocols. [ABSTRACT FROM AUTHOR]

In this paper we discuss the research about genetic algorithm application in fuzzy controller synthesis. Genetic algorithm is artificial intelligence approach and it allows finding fuzzy controller parameters which can provide the extreme value of optimization criterion. The authors describe the work of variation operator which includes into genetic algorithm. This research presents the results of automatic control system modeling simulation, input variables term-sets, rule base, array of fuzzy controller parameters and distribution of squared variances sum for variation of different groups of fuzzy controller parameters. Also article contains statistical analysis by testing statistical hypothesis about significant difference between variation of membership function parameters, rules consequences and rule weights. [ABSTRACT FROM AUTHOR]

The issues of electromagnetic compatibility in the field of railway automation and telemechanics are considered by specialists in the Russian Federation mainly from the position of increasing the noise immunity of the operation of receivers of electric rail circuits and basic safety devices. At the same time, practically no attention is paid to the aspects of the electromagnetic compatibility of equipment and sources of interference in the framework of maintenance and repair processes. This pilot work is designed to partially eliminate this drawback and is devoted to the topic of studying the electromagnetic environment at the locations of equipment designed to assess and measure the technical and operational characteristics of the basic safety devices. The test equipment considered in the work is located at the control points of the automatic locomotive signaling at depots. From the point of view of electromagnetic compatibility, the requirements for the organization of control points, given in the regulatory documentation, are analyzed. The results of an experimental study to determine the levels and frequency composition of the signal and interference mixture at the output of the receiving coils of the basic safety devices in the electromagnetic environment of an operating control point are presented. The main provisions in the field of ensuring the electromagnetic compatibility of the equipment of control points are formulated, which are reflected in the new regulatory documentation. The results of this study may be of interest to both specialists in the field of electromagnetic compatibility and specialists engaged in the maintenance and repair of on-board equipment of train traffic regulation and control systems. The results can be used for the subsequent assessment of the electromagnetic compatibility of test equipment of control points with various sources of interference or in the framework of work aimed at improving the quality of maintenance and repair of basic safety devices. [ABSTRACT FROM AUTHOR]

Driver's irresponsible driving habits lead to accidents on the road frequently. Schools, parks, hospitals, hills, and highways are where accidents occur most frequently. A prototype model of automatic speed control system of a vehicle has been developed in an effort to lower the frequency of accidents. Particularly around the restricted area, if the vehicle's speed rises, the proposed automatically adjusts the vehicle's speed to fit the surroundings. A radio frequency transmitter is mounted on the side of the road, and a radio frequency receiver is installed inside the car. The radio frequency transmitter broadcasts the needed limiting speed to the receiver installed inside the vehicle when it travels through the restricted area. By this implementation, the zone limit is communicated to the vehicle's microprocessor and vehicle speed is obtained by placing a speed sensor placed inside the vehicle. The microprocessor compares the received signal with the actual speed of the vehicle and it also controls when sparks are used to ignite the gasoline and when fuel is to be injected into the vehicle to increase speed or slow down using fuel injection technique. The fuel valve is integrated with the actuator and is managed by an electronic control unit in this fuel injection method. This technique uses a servomotor attached to the fuel valve to determine when to open the fuel valve partially or completely, to control the vehicles speed. Hence the vehicle speed is controlled and enhanced the safety measures to reduce the number of accidents. [ABSTRACT FROM AUTHOR]

Brain tumors are the most common and antagonistic disease. As a result, its duration is limited. In this way, exact brain tumor distinguishing proof is fundamental for conceiving a genuine therapy methodology to fix and expand the existence of individuals with cerebrum tumors. The goal of data augmentation and fine-tuning methods is to separate and classify tumors. In this paper, we perform brain tumor classification in the proposed framework using Jupiyter Notebook using four different types of brain tumor datasets obtained from Kaggle. The proposed work employs three different Convolutional Neural Networks (CNN) architectures, namely Alex Net, Google Net, and VGG-16Net, on MRI slices to identify tumor types. It is observed that the proposed method achieved an accuracy of 98.74% for VGG-16 Net which is comparatively high than the state of art tumor detection method. The developed method is also compared with two other transfer learning networks such as Alex Net and Google Net. [ABSTRACT FROM AUTHOR]

This report examines the management of automatic transmission transmissions of the AF40 type, which are installed in Opel, Saab, Peugeot, Volvo, Fiat, etc. cars. For this purpose, a special bench for testing electronic regulators has been made. The measurement methodology is described in detail and the test results are explicitly indicated. The results following the repair of the solenoids are also revealed. [ABSTRACT FROM AUTHOR]

This work describes the working and analysis of Continuously Automatic Variable Transmission (CAVT) for bicycles as well as for other industrial applications with necessary modifications. The transmission system is based on a centrifugal governor mechanism. The paper discusses the working of continuous automatic transmission (CAVT) systems. The kinematic and structural analysis of the CAVT system is carried out using MSC Adams and Ansys Workbench respectively. The variation of the various parameters such as spring force acting on the cone, velocity and position of the sliders is analysed with respect to time and input angular velocity of the shaft. Further the structural analysis of the CAVT model is done and failure prone positions in various parts are observed. [ABSTRACT FROM AUTHOR]

Sentry Gun is a weapon designed to monitor national borders automatically without requiring an operator. In making the Sentry Gun system, mathematical modelling is carried out using the black box method to determine the transfer function of the plan or system to be controlled. Sentry Gun is in the form of a 1:3 model compared to the original made of Aluminum and Steel. Image processing is used as an object sensor tracking Sentry Gun. Open CV is used in the Image Processing process. A DC motor is used as an actuator in the movement of the elevation and azimuth axes (2 Dof). As feedback, a rotary encoder is used to detect the rotation of the DC motor. Due to limitations in the manufacture of mechanical systems so that at the azimuth angle using helical gear there is a backless which makes the system unstable and takes a long time to reach the settling time. The motor movement control used is PID which is based on intelligent control of the genetic algorithm so that the movement of the tool is precise, smooth and on target. System modelling was carried out using the MATLAB Identification System feature. Iterated 100 times in determining the value of Kp, Ki, and Kd. so that the values of Kp 245, Ki 180 and Kd 215 are obtained. PID tuning based on intelligent control of genetic algorithms can produce a more optimal step response system. Namely Rise Time Produced 0.96s, Overshoot 5%, Settling Time 5.29s and steady state error 0%. [ABSTRACT FROM AUTHOR]

The many properties of water (physical and chemical) have been the focus of many scientific investigations on the importance of water in human life. Unfortunately, many reasons led to water pollution and the loss of some of its essential properties. Magnetic water technology is one of the technologies that contribute significantly to improving some of the properties of water. The process of magnetizing water is by passing it through tubes surrounded by fixed or changing magnetic fields. This research describes an excellent automatic water magnetization system based on measuring dissolved salts in water TDS, used in a water purifier. A programmed system has been designed and implemented by measuring the percentage of dissolved salts in the water at the entrance and exit of the water magnets and calculating the difference between the two values. It works automatically using the Arduino controller to control the water magnetization processes according to the appropriate intensity that affects the percentage of dissolved salts in the water. Fixed magnets of various strengths (500G, 100G, 1500G) are employed. The findings suggest that an excellent model enhances water efficiency features and gives essential, accurate, and timely data on water properties that may be used for scientific purposes in this field. [ABSTRACT FROM AUTHOR]

The general population is concerned about the usage of the internet as a result of advances in information and technology. News website on the internet are an example of a technology that has been developed to disseminate the most up-to-date worldwide information. When it comes to numbers, newsreaders are more than adequate for obtaining the needed information. However, as a result of the amount of data collected, there will be an information explosion as well as the possibility of information redundancy. One of the methods targeted at supporting the input query in locating the required or relevant information is the search system. In weighing to measure statistics from a collection of papers on the search for information about the Covid 19 vaccine on tempo news, the approaches TF-IDF and VSM (Vector Space Model) are used, followed by tokenizing the text to split it, stopword removal, or filtering to remove extraneous words like conjunctions and others. The next process is sentence stemming, which aims to reduce word inflection to its most basic form. News document 1 (DOC 1) has a weight of 17.242, news document 5 (DOC 5) has a weight of 11.687, news document 4 (DOC 4) has a weight of 9.243, news document 2 (DOC 2) has a weight of 5.737, and news document 3 (DOC 3) has a weight of 0. The precision with which a document's word weighting is calculated can be determined using the Term Frequency (TF) - Inverse Document Frequency (IDF) approach and Vector Space Model. [ABSTRACT FROM AUTHOR]

The development of world technology is growing rapidly, including the health sector in the form of a tool for detecting various kinds of diseases. These technologies include systems that can recognize images and perform classification automatically. An automated system like this is called an intelligent system which is very useful in the health sector because it can help even replace a doctor or health expert in carrying out the task of diagnosing disease. The aim of this research is to build a system that can be used to detect COVID-19 infections automatically using image processing. The classification method used is an image processing technique using machine learning, namely a support vector machine (SVM) with entropy and correlation parameters. The image that will be recognized is the image of the lungs from the X-ray machine. The results show that the system built has succeeded in detecting normal lungs and lungs infected with Covid-19 with 98 percent accuracy [ABSTRACT FROM AUTHOR]

This research aims to contribute to ending the electricity crisis in Iraq, as it is country rich in solar energy due to its location close to the equator. Electric power stations, which use traditional fuels, are unable to provide the population's electricity needs throughout the day. Moreover, electricity distribution lines sometimes malfunction due to high temperatures. This paper recommends solar energy as an optimal solution to the electricity problem in Iraq. Because it is renewable and environmentally friendly. The paper also focused on techniques that would improve the efficiency of the solar power system. This was achieved by designing and building a solar tracking system to absorb the largest possible amount of solar energy and increase electrical energy production, in addition to building a cooling system consisting of a small pump and nozzles that pump water in the form of a volatile mist to get rid of excess heat from the solar panels at the temperature It rises to extreme levels. The system is also equipped with a cleaning mechanism to rinse the solar panel once, which is harmful to getting dirt and dust because dust is the negative factor that affects the generation capacity of the solar panels. The solar tracking system and the cleaning system are operated by a control unit made in the local markets that work with the Internet of Things (LOT) system and is programmed in the Python language and depends on data completely on the Internet. As for the cooling system, it supplies energy during the day directly from the solar panel, depending on the temperature sensor. The results reveal that the solar tracking, cooling, and cleaning system has significantly improved the production capacity of the solar system and can achieve an energy gain of 39.68%. [ABSTRACT FROM AUTHOR]

The paper analyzes the influence of the adaptive mechatronic system parameters on the process of stabilizing the step of moving materials during automatic assembly. Dependences of variances and mathematical expectations of the displacement step are obtained depending on the parameters of the system under a random external action. Numerical simulation of the operation of the algorithms used is carried out, recommendations are given for the optimal choice of system parameters. [ABSTRACT FROM AUTHOR]

In this paper, Merge the estimation of state variable with FBG for optimal Automatic generation control (AGC) is proposed. AGC represents the FC system within its rated limits after the power system has been exposed to a slight disturbance. Optimal control systems, LQR are based on the feedback gain calculation, which imposes that the majority of the state variables are known and parameters of the system are measurable. As the operation of power systems is accompanied by dynamic changes and the state variables need continuous measurement in addition to the system parameters, the current research suggested a modify (AGC) by integrating the state variables and the feedback gain. Continuous estimation of state variables is performed through Extended Kalman Filter (EKF). The effectiveness of the proposed automatic generation controller was tested on two generating areas (thermal/hydroelectric), and the results indicated the efficiency of the controller in returning the system to its operating conditions before it was exposed to disturbance. [ABSTRACT FROM AUTHOR]

A promising direction in the development of the transport complex is designing and implementing unmanned navigation (UN), ensuring the reliable operation of marine control systems. This includes a human operator as the key link, in the mode of optimising ship control in interaction with electronic monitoring, control and information display devices. This task requires the presentation of information in a form and quantity appropriate to a person's ability to process it and make effective management decisions. It is important to fully automate the ship's technical systems and the time factor, through interaction with the ship's video monitoring system, operating in real time on telecommunications input and output control in the control unit and displayed on the operator's working monitor. The study provides a statement and formulation of the problem of establishing a sustainable system for remote operator control of a ship. It suggests general principles of mathematical support for ship control in the mode of automatic operation and regulation of ship motion as applied to the tasks of continuously transmitted ship coordinates, course, angular rate of turn and speed. The authors present a schematic diagram of the circulation of control information in the unmanned navigation operator-ship system, as well as a functional diagram of an integrated automated ship control system through a stable broadband connection between the operator and the ship, or group of ships. [ABSTRACT FROM AUTHOR]

The proposed objective of the work is to enhance accuracy of automatic detection and classification of liver cancer. The sample size for each group in a study comparing the performance of SVM classifier with decision tree classifier was determined using GPower to be 10. The desired power for the study is 0.80 and the alpha level is 0.08. For performance analysis, the images are trained with 70%, tested and validated with the remaining 30%. The SVM algorithm achieved an accuracy of 95.32% which was higher than the accuracy of 80.26% achieved by the Decision tree algorithm. Independent sample T-test was conducted to compare the results and found that the difference in accuracy was statistically significant (2 tailed) (p=0.004). The SVM algorithm outperforms with higher accuracy compared to the Decision tree algorithm. [ABSTRACT FROM AUTHOR]

The paper considers the development of an algorithm for controlling the automatic tool calibration system for robotic machining. It shows the relevance of the issue, proved by the design characteristics of industrial robots and their growing introduction in the production tasks. The authors describe the proposed principle of carrying out the calibration. It is shown that if geometrical parameters of the complex equipment do not change, tool overhang measurement and two calibrated reference tools available are enough for new tool calibration. The authors present a mathematical model for tool calibration parameters calculation. The authors also describe a principle of technical execution of automatic tool calibration system. They develop a control algorithm of an automatic calibration system using the interruption mechanism and consisting of several sequentially called subprograms. To check the adequate operation of the developed algorithm, the pilot sample of the automatic calibration system is made up. The system model is integrated into the robotic machining complex based on the industrial robot KUKA KR60-3. Experimental testing of the developed algorithm and the automatic calibration system functioning is conducted. The experiment shows a sufficient level of correspondence of the results of manual and automatic calibration and significant saving of time provided for the automated calibration. [ABSTRACT FROM AUTHOR]

Implementation of Machine Learning/Deep Learning techniques in Image Processing have paved way to advanced object detection, classification and tracking in a real time environment. Recent advancements are enabling these techniques to be used in the field of real time Infra Red (IR) object detection. IR image processing is being used extensively in the field of military, industrial and medical applications. This paper emphasizes upon the various Deep learning techniques which can be used for automatic target detection/recognition in infra red maritime imagery of sea borne military assets. Our review begins with the introduction to Object Detection, IR imaging, techniques available and various architectures for feature extraction and object detection. Then, we also provide a comparison of performance/detection capabilities on widely used PASCAL VOC 2007 and 2012 datasets. [ABSTRACT FROM AUTHOR]

Recently, a drastically increased focus has been observed in multilingual Automatic Speech Recognition (ASR). To cater to multiple low resource languages, a speech recognition system is used. This is performed by taking advantage of low amounts of labeled corpora in multiple languages has. The prosperity of low-resource multilingual and code-switching ASR often depends on the variety of languages in terms of linguistic characteristics as well as the amount of data available. This work focuses on modifying the multilingual and code-switching ASR system through two different subtasks including a total of seven Indian languages. To counter this the model has been provided with several hours of transcribed speech data, comprising of train and test sets, in these languages including two code-switched language pairs, Hindi-English and Bengali-English. In this work, a modified ESPNet architecture is proposed to perform multilingual ASR which improved the performance of the baseline system resulting in accuracy of Word Error Rate (WER) is 27.69%. [ABSTRACT FROM AUTHOR]

Epilepsy has now become a serious global health issue. Approximately 1% of people globally experience epileptic seizures. Predicting seizures before they start helps use medication to avoid them. Using modern computer tools, machine learning, and deep learning techniques, EEG has been used to predict seizures. The main difficulties in designing epilepsy prediction algorithms are feature selection and classification. Automatic detection and prediction of epileptic seizures are carried out much faster through deep learning techniques than traditional approaches. Designing a deep learning network from scratch or using a pre-trained network not only depends on the size, variability of data, complexity of the network, number of layers, network parameters, depth, etc. This work presents A Fast and Novel Deep Learning Approach for the Automatic Classification of Epileptic Seizures using Spectrograms. The seizure data was acquired from the TUH EEG Corpus. Pre-trained networks were used on four sets of spectrogram images of normal and seizure data and reorganized within the networks, tuned on the parameters, such as Convolution layers, Classification layers, etc. The above networks were trained and tested for each set of spectrograms, and the performance was evaluated based on testing accuracy and time for the training and validation process. The proposed method achieved remarkable accuracy with the capability of retrieving the signal information at each instant. Tuned ResNet-18 achieved an average accuracy of 98.9%, with an operating time of 1056 to 3606 seconds, while modified AlexNet proved to be the fastest, with an operating time of 421 to 425 seconds with an average accuracy of 98.2 for various features selected. [ABSTRACT FROM AUTHOR]

This article describes algorithm for calculating universal water chemistry index for cycle chemistry. The basis of the algorithm is to compare the monitored parameters with their normalized values. The water chemistry index (WCI) is implemented as the monitoring algorithm to evaluate water chemistry and to determine the priority actions for the operating personnel. The aim of the chemistry index calculation is to determine the quality of the degree of danger of deviations or failures of water chemistry. According to the Russian guidelines, monitored parameters have either only the top rated value, or only the lower, or both upper and lower values together. In order to optimize cycle chemistry is necessary to evaluate not only each monitored parameter, and water chemistry of individual part of steam/water cycle and the unit as a whole. It is proposed to introduce the calculation of water chemistry indices of individual part of steam/water cycle and the unit as a whole. The basis for calculating the water chemistry index is the approach the lower the index value of the quality, the better cycle chemistry considered part of steam/water cycle. The index value equal to one is the boundary between normal cycle chemistry and its failure. If the Water Chemistry Index is more than one it means the controlled parameters exceed the normalized values. As more the index of the part of steam/water cycle exceeds one as more important the violation of cycle chemistry. If the value of one of the monitored parameters is outside of established norms, the index of site quality of the tract is more than one. The algorithm allows quickly identifying failures of cycle chemistry; objectively evaluate quality of the waters chemistry. [ABSTRACT FROM AUTHOR]

Ketapang ferriage port is a port that connects the island of Java with Bali and Lombok. This port has a large role in distributing logistics and food to and from Java Island. Therefore, the safety and security of shipping must be guaranteed. One of the tools used for improving the safety and security of shipping is the Automatic Identifications System (AIS). This research aims to analyze (AIS) installed on the ship system based on the criteria in applicable regulations and find out the compliance of ship operators in the delivery of information on AIS on the Ketapang-Gilimanuk and Ketapang - Lembar tracks. In obtaining data on this study using observation techniques, observing the actual conditions directly. Based on the results of the study it can be concluded that all ships operating on the route have installed AIS, but the installed AIS Class is not by applicable regulations where 21 ships still use AIS Class B, as well as the irregular activation of AIS because there are still ships operating but not detected AIS at Local Port Service (LPS) stations, data information submitted to AIS is incomplete, for AIS Class A which completes information on AIS by 64.29% while AIS Class B is 83.33% [ABSTRACT FROM AUTHOR]

The process of summarization is taking a longer piece of material and reducing it to a shorter version without losing any of the essential information or meaning. Due to the exponential increase in available information and data, automatic text summarizing has emerged as a useful alternative to the time-consuming and error-prone process of manual summation of massive amounts of texts. The summaries the algorithm generates help users better understand the material presented in the original document. There are two main types of summarization: abstract and extractive. The number of available automatic summarization tools for Indian languages is low. Our focus in this area has been on creating an automatic English-language text summarizer utilizing the T5 transformer model; we've employed a manual dataset for testing and training. Here we have used news summary dataset. [ABSTRACT FROM AUTHOR]

Hybrid UAVs have unique capabilities to take-off and land vertically. For these unique vehicles, there are several ways to achieve these phases of flight successfully. This paper proposes four strategies for a convertible hybrid unmanned aerial vehicle, to transition from forward flight to hover, and then land vertically. Strategy 1 and Strategy 2 suggest reducing forward airspeed gradually to zero while maintaining altitude by regulating the workload between the four motors and the main engine. The difference between these two strategies is the speed of reducing the forward airspeed to begin hovering. Strategy 3 proposes gradually losing altitude while reducing forward airspeed until transition airspeed is reached and hovering to commence. Strategy 4 suggests a way to drop the aircraft vertically by deep stalling in effort to lose altitude quickly but engage the four motors before the aircraft reach the ground. The paper concludes with a brief description of the role of the automatic flight control systems responsible for regulating this transition phase for convertible hybrid UAVs. [ABSTRACT FROM AUTHOR]

To perform the credit card fraud detection based on the approved and fraudster users using Artificial Neural Network algorithm compared with Support Vector Machine algorithm. Materials and Method: Credit card fraud detection is performed using Artificial Neural Network algorithm (N=1780) and Support Vector Machine algorithm (N=1780). The credit cards dataset was trained to Artificial Neural Network algorithm and Support Vector Machine algorithm and tested to detect the credit card fraudulent. Optimal value is identified when the split size of training and test dataset used in the Artificial Neural Network algorithm is 30% and Support Vector Machine 70%. Results and Discussion: Artificial Neural Network achieved significantly better accuracy rate (98.57) in comparison with Support Vector Machine (96.78%) and attained the significance value of p = 0.036. Conclusion: Artificial Neural Network achieved significantly better prediction rate for credit card fraud detection than Support Vector Machine. [ABSTRACT FROM AUTHOR]

Awareness of plant disease is a key task in the agriculture field. Early diagnosis of plant leaf diseases may aid farmers in avoiding losses caused by a variety of plant leaf diseases. Plant leaf diseases are detected through image processing by examining the trends of leaf photographs over time. Furthermore, due mostly to the worldwide pandemic coronavirus, global potato consumption is increasing significantly. Potato infections, on the other hand, are the leading cause of crop quality and availability decreases. Incorrect disease classification and late detection will aggravate plant conditions greatly. Fortunately, many plant diseases may be identified using leaf features. Manually watching and detecting leaf diseases is a time-consuming, expensive, and labor-intensive approach. As a consequence, utilising an automated image processing system to identify diseases is preferred. image enhancement, image pre-processing, segmentation techniques, feature extraction, and other image processing techniques, as well as image classification are accessible for identifying plant leaf diseases. This paper presents an overview of the various computer vision algorithms used to diagnose plant leaf diseases. Some deep learning algorithms are investigated. Automatic Detection of Plant Leaf Diseases Using Image Processing: A Survey. [ABSTRACT FROM AUTHOR]

Cargo transportation activities in the Mahakam River channel are increasing every year. The threat of collision to the bridge support structure is a priority of attention. The ships safety system needs to be improved when it is under the Mahakam bridge. The alternative is to apply the Automatic Identification System (AIS) in shipping activities. AIS will reduce accidents to the smallest point by sending ship data, such as ship name and type, call sign, ship nationality, maritime mobile services identities (MMSI), International Maritime Organization (IMO) Number, weight of a ship, ship specifications, navigation status, coordinate points, voyage destinations with estimated arrival time, ship speed, and the presence of the ships bow. The purpose of this study is to describe the usefulness of AIS in various threats of ship accidents on the Mahakam River. This research is descriptive qualitative with data and data sources derived from transcription of interviews with the captain, related stakeholders, and other primary documents. [ABSTRACT FROM AUTHOR]

In this paper an optimal proportional-integral-derivate (PID) parameters for automatic generation control (AGC) of the two area power system is presented, which is the classical method adopted by researchers. Multi-area (AGC) is scanned for the purpose of increasing stability by means of improved PID controllers under 1% (ΔP). Two technique are used to tune the gains of Proportional-Integral-Derivative (PID) which it Particle Swarm Optimization (PSO) and Fuzzy-PID. A comparison between the two method is done to choose the be tuning parameters that achieve the stability of the system by returning the oscillation in the waves to a state close to zero. Finally, the results of dynamic stability were compared, where it was proved that PSO is better than Fuzzy in terms of overshoot, rise time, settling time. [ABSTRACT FROM AUTHOR]

Today, we live in 21st century where automation is playing a major role in human race. Automation ensures higher production rates, improved safety and shorter workweeks for labours. Many farmers today can't afford to use automation and control machines owing to their high cost. Many villages in India have to use fuel based irrigation system due to a shortage of electricity. Fuels cause global warming and pollution in environment. In this paper our aim is to propose an automatic irrigation system which is cost effective and farmers can use it in an agricultural field. As the system is automated, the pump turns ON/OFF only when the moisture content of soil is below critical level which is regulated by a soil moisture sensor. The objectives of this paper are to control the automatic irrigation system with the help of a GSM module. The farmer at his own discretion can send a message to ON/OFF the pump without going to the field. PIC microcontroller, LCD display, sensors have been used as prime components. Farmer can control the system in automatic or manual mode also substantially reduce the electricity and water cost. We would discuss the design and implementation of the automated irrigation system. Finally, a prototype is designed and implemented and the experimental results are presented [ABSTRACT FROM AUTHOR]

The new world has witnessed ideas that pragmatically changed the way, one lived based on everyday technology. The interconnection between things and making the system smart is one of them. Vehicles connected to the internet and sensors could a be a game-changing technology. This paper showcases a new idea of an inter and intra connection using the internet furtherly building to the existing IoT-connected vehicles. In this work, we described real-time safety protection with a number of conditions and controls with solutions using the NI Lab VIEW technology. [ABSTRACT FROM AUTHOR]