Real-time Values Research Articles

Real-time Water Quality Monitoring System using IoT: Application at Melaka River, Malaysia

The rapid development around the Malacca city has a huge impact on the environment. Among the effects that can be seen is water pollution along the Malacca River. To overcome the problem, Hydro Quality Monitoring System (HydroQS) has been built and is a co-developed product between Universiti Teknikal Malaysia Melaka (UTeM) and Perbadanan Pembangunan Sungai dan Pantai Melaka (PPSPM). This device acts as one of the solutions to continuous monitoring the river water quality in real time values such as pH, Dissolved Oxygen (DO), Total Dissolved Solid (TDS), Turbidity and Temperature via Arduino interface. Prior to conduct field test, the HydroQS will perform floating and sensors functionality test at laboratory environment. As a results, the HydroQS capable to float and the sensors able to capture the water quality data.

STUDY ON AUTOMATION PROGRAMMING TO MONITOR AND CONTROL A SCREW ELECTRO-COMPRESSOR

The article describes the programming architecture of a programmable logic controller (PLC) and an operator panel that is part of an automation cabinet of a screw electro-compressor. Behind the operation of a screw electro-compressor, there is a programming logic presented in the article that is implemented according to the requirements of each project. The article presents all the states of a screw compressor that are implemented in the PLC by a programmer in the form of software sequences. The article depicts the logic diagrams used to facilitate the software implementation process. Following the software implementation in the PLC of the logic diagrams, the article has a second software that contains a series of screens for the operator panel. The operator panel is a touch-screen type and communicates with the PLC through a defined communication protocol displaying the parameter values in real time on the programmed screens. The software developed for the operator panel by a programmer has the role of facilitating a human operator to monitor parameters and transmit commands to the screw electro-compressor. The article ends by presenting the conclusions that argue the efficiency and safety in operation through the presented software concept of a screw electro-compressor.

The Influence of Corrosion Processes on the Degradation of Concrete Cover.

In this work, two methods were used to accelerate the corrosion of concrete. In the first method, chloride ions were injected into the concrete using the migration method. The moment of the initiation of the corrosion process was monitored using an electrochemical method of measuring polarization resistance. In the next step, the corrosion process was accelerated by the electrolysis process. Changes on the sample surface were also monitored using a camera. In the second method, the corrosion process of the reinforcing bar was initiated by the use of the electrolysis process only. Here, changes occurring on the surfaces of the tested sample were recorded using two web cameras placed on planes perpendicular to each other. Continuous measurement of the current flowing through the system was carried out in both cases. It was assumed that in conditions of natural corrosion, a crack would occur when the sum of the mass loss of the reinforcing bar due to corrosion reached the same value in tcr(real) (real time) as it reached in the tcr (time of cracking) during the accelerated corrosion test. The real time value was estimated for C1 concrete with cement CEM I. The estimated value was tcr(real) = 1.1 years and for C2 concrete with cement CEM III, tcr(real) = 11.2 years. However, the main difference that was observed during the tests was the nature of the concrete cracks. In the case of the C1 concrete sample, these occurred along the reinforcing bar, while in the C2 concrete, the failures occurred on a perpendicular plane transverse to the direction of the reinforcing bar.

Architectural Proposal for Low-Cost Brain–Computer Interfaces with ROS Systems for the Control of Robotic Arms in Autonomous Wheelchairs

Neurodegenerative diseases present significant challenges in terms of mobility and autonomy for patients. In the current context of technological advances, brain–computer interfaces (BCIs) emerge as a promising tool to improve the quality of life of these patients. Therefore, in this study, we explore the feasibility of using low-cost commercial EEG headsets, such as Neurosky and Brainlink, for the control of robotic arms integrated into autonomous wheelchairs. These headbands, which offer attention and meditation values, have been adapted to provide intuitive control based on the eight EEG signal values read from Delta to Gamma (high and low/medium Gamma) collected from the users’ prefrontal area, using only two non-invasive electrodes. To ensure precise and adaptive control, we have incorporated a neural network that interprets these values in real time so that the response of the robotic arm matches the user’s intentions. The results suggest that this combination of BCIs, robotics, and machine learning techniques, such as neural networks, is not only technically feasible but also has the potential to radically transform the interaction of patients with neurodegenerative diseases with their environment.

Sistem Pengering Biji Kakao dengan Menggunakan Metode Fuzzy Logic.

Monitoring system The cocoa bean dryer is a system created to make it easier for emping melinjo craftsmen to combine the cocoa bean drying process based on temperature, humidity, weight and drying status. This system was created in the form of an application called MONIBIK Iot, in this application there is data on Temperature, Humidity, Weight and Drying Status as well as graphic displays. MONIBIK IoT can be accessed anywhere and at any time, provided the device must be connected to an internet connection. The conclusion from the research carried out is that the test results in the process of sending data from the system to the server display data values ​​in real time. Based on the test results of the sensor in measuring the level of drying of cocoa beans, based on temperature, humidity and weight results that can be obtained in the measurement process, the sensor can work well. The test data shows that the temperature value has increased, while the humidity and weight values ​​have decreased. This shows that the cocoa bean dryer is operating properly.
 Keywords— Monitoring, Mikrokontroler, Cocoa bean, Fuzzy.

Mathematical modelling of drilling mud plastic viscosity at downhole conditions using multivariate adaptive regression splines

Viscosity is an essential mud property which enables it to transport cuttings out of a well. Determining the real time value of mud viscosity at downhole conditions is complex and till date lacks a precise model. Literature is filled with models that are only applicable at surface conditions with most of the models being artificial neural network (ANN) based. Neural networks have been criticized for their long training time because they rely on a trial-and-error approach to determine their optimal architecture. The few models related to mud viscosity at downhole conditions are based on non-linear regression which are not flexible in nature. However, Multivariate Adaptive Regression Splines (MARS) has at no time been used notwithstanding some of its known benefits over ANN and other regression methods. The focus of this study is to model mud plastic viscosity at downhole conditions with the MARS method using parameters such as temperature, pressure and initial mud viscosity. MARS is a flexible non-parametric splines-based technique that extends linear regression models by including nonlinearities and interactions between predictors. An experimental database comprising 88 and 149 data points culled from literature for oil-based mud (OBM) and water-based mud (WBM) respectively were utilized for developing the models. For each mud type, the most suitable model was determined by choosing the model with the maximum coefficient of determination (R2) value and the least values of mean square error (MSE), root mean square error (RMSE), and generalized cross-validation criterion (GCV). Using these criteria, the R2, MSE and RMSE of the MARS models according to the mud type were found to be 0.64, 104.35, 10.21 and 0.71, 12.8, 3.57 for OBM and WBM respectively while the GCV values were 129.3 and 14.38 for OBM and WBM respectively. Due to lack of mud rheology data at downhole conditions, it was difficult to compare the performance of the developed models with existing models. Parametric importance analysis indicate that temperature has the highest effect on mud viscosity estimation at downhole conditions. The developed models are valid for 20 °C ≤ Temperature ≤315 °C and 0 psi ≤ Pressure ≤40,000 psi. This pioneer work on using MARS in drilling fluid property prediction points out the implementation protocol, the dataset utilized, the relative importance of input variables and the explicit model for estimating mud viscosity at downhole conditions.

Trust evaluation model of power terminal based on equipment portrait

As the number of power terminals continues to increase and their usage becomes more widespread, the security of power systems is under great threat. In response to the lack of effective trust evaluation methods for terminals, we propose a trust evaluation model based on equipment portraits for power terminals. First, we propose an exception evaluation method based on the network flow order and evaluate anomalous terminals by monitoring the external characteristics of network traffic. Second, we propose an exception evaluation method based on syntax and semantics. The key fields of each message are extracted, and the frequency of keywords in the message is statistically analyzed to obtain the keyword frequency and time-slot threshold for evaluating the status of the terminal. Thus, by combining the network flow order, syntax, and semantic analysis, an equipment portrait can be constructed to guarantee security of the power network terminals. We then propose a trust evaluation method based on an equipment portrait to calculate the trust values in real time. Finally, the experimental results of terminal anomaly detection show that the proposed model has a higher detection rate and lower false detection rate, as well as a higher real-time performance, which is more suitable for power terminals.

Plasma Ion Bombardment Induced Heat Flux on the Wafer Surface in Inductively Coupled Plasma Reactive Ion Etch

Plasma plays an important role in semiconductor processes. With the recent miniaturization and integration, the control of plasma became essential for success in the critical dimension of a few nanometers and etch narrow and deep holes with their high aspect ratios. Recently, the etching process has reached physical limitations due to a significant increase in wafer surface temperature under the elevated amount of RF power, affecting not only the warpage phenomenon, but also etching uniformity and etching profiles. Therefore, the plasma characteristics are identified using an invasive single Langmuir probe (SLP) for wafer temperature diagnosis. Optical data is obtained through a non-invasive optical emission spectroscopy (OES) and the plasma parameters are derived to compare and verify with the SLP. Two variables, electron temperature and electron density, are substituted for the heat flux formula to derive the heat flux according to the location. Using a wafer-type temperature sensor, the trends of the derived heat flux values towards wafer chuck were investigated. This study presents a method to calculate heat flux values in real time, anticipate wafer temperatures, and potentially illuminate existing ion heating problems.

IoT based Smart Irrigation System

Abstract: Automation in the field of agriculture has brought a new wave of development. The proposed model is based on IoT and its various applications. This system consists of three sensors integrated with IoT to provide the user with real time updates and control of the field. The various sensors used are soil moisture sensor, rain sensor and temperature and humidity sensor. All these sensors help in monitoring the values of important aspects of irrigation and agriculture. With the help of the proposed model, the user would not only be able to see the various sensor values in real time but also control the moisture level and the status of irrigation in real time as well. This model will not only help in preventing water-logging or drought, but will also increase the efficiency of the yield and conserve valuable energy.

Parameters extraction of photovoltaic cells using swarm intelligence based optimization technique: research on single diode model and double diode model

Solar-energy is a clean source of energy and photovoltaic (PV) panels are constructed from solar cells (SC) which convert energy of light into electricity without any environmental effect. The researchers and policy makers focus on the huge scale adoption of solar panels due to its cleaner production. However, there is non-linear behavior in current-voltage characteristics of solar panels and shortage of data in manufacturer’s datasheet. In order to enhance the efficiency of solar panels it is mandatory to develop the PV panels scheme accurately by extracting the basic parameters. In this research study a mathematical model of two different solar cell models is used such as Single Diode Model (SDM) and Double Diode Model (DDM). The Particle Swarm Optimization (PSO) is used to extract the five and seven unknown parameters of SDM and DDM. The algorithm runs with one thousand iterations to minimize the Root Mean Square Error (RMSE) where the RMSE is the vector of five unknown parameters for SDM and seven for DDM. The superiority of proposed PSO algorithm is proved by the optimized results of unrevealed parameters with minimized RMSE of up to 10-3. Optimum parameter values for the solar cell models are applied on the real time data of a 55 mm diameter commercial RTC-France SC. Finally, the results reveal that P-V and I-V curves exhibit smallest deviation between estimated and real time values. The results reveal that the proposed PSO converges to optimal solution with least number of iterations compared to the existing metaheuristic algorithms.

Soft Sensor Design via Switching Observers.

The goal of the paper is the design of soft sensors for single input single output (SISO) nonlinear processes. This goal is of essential importance for process monitoring, fault detection and fault isolation. The observer-based technique, being a fruitful direction in soft sensor design, is followed to develop soft sensors for nonlinear processes with known dynamics and unknown physical parameters. A new and general approach, based on the identified I/O linear approximant system descriptions, around prespecified operating points, and a bank of switching linear observers, will be developed. The system property of the I/O reconstructability of the state space linear approximant of a nonlinear model is presented. The design of each observer is based on the I/O measurements and structural characteristics of the nonlinear process. Observer-oriented target areas are introduced, and the respective dense web principle is formulated. The design is completed by the design of a data-driven rule-based system, providing stepwise switching among the observers of the bank. The number of observers of the bank is equal to the number of the linear approximants of the nonlinear process model and is equal to the number of the respective target operating areas. The target operating areas are required to satisfy the dense web principle. The information provided by the soft sensor is the estimation of the non-measured variables of the process. The information used by the soft sensor is the identified I/O approximants of the process as well as the real time values of the measurement variables. The efficiency of the design scheme is illustrated through symbolic and numerical simulation results for a chemostat. The nonlinear model of the chemostat is initially approximated by a set of ten linear approximants. After, the I/O approximants are identified, the respective observers are designed and the target operating areas are determined, where several cases of the satisfaction of the dense web principle are investigated. The soft sensor is composed in terms of the designed observers. Simulation results illustrate the satisfactory performance of the designed soft sensor.

Online monitoring of the technical condition of energy saturated agricultural equipment using neural networks

The article presents a technique for continuous monitoring of the technical condition of energy-saturated agricultural machinery (SHT) using digital technologies, aimed at introducing new intelligent methods for diagnosing machines in the agro-industrial complex. It is noted that the main task of the digital monitoring system is to analyze the effective operation of equipment. The proposed neural network can continuously receive data on the technical condition of agricultural machinery in real time, analyzes and structures input values, such as engine speed, hourly fuel consumption, coolant temperature, which depend on the load and operating modes of the machine engine. The advantage of the digital method of monitoring the parameters of the technical condition is its assessment in the process of diagnosing in real time. The method allows to determine not only the cause of engine failure, but also to evaluate the efficiency of complex energy-saturated agricultural machinery in general. The developed architecture of the neural network is capable of analyzing and transmitting data obtained during the diagnostic process to a special server for storing information. The proposed method for continuous monitoring of the technical condition of complex energy-saturated equipment according to controlled parameters, based on the use of neural networks, can be quickly adapted to different brands of equipment when diagnosing.

AI-based sound source localization system with higher accuracy

This paper focuses on the research of sound source detection technology. Combining the artificial intelligence (AI) and 6th generation (6G) communications, it is a hot topic to build a centralized microphone array to locate the sound source in a particular scene. However, degrees of vagueness still exist concerning the correlations and influences between the array establishment, localization implementation and its performance. To this end, we propose a sound source localization system (SSLS) with a high accuracy, ultra reliability and low latency communication. Meanwhile, an AI-based back propagation (BP) neural network enables the SSLS to predict the sound source position (SSP). SSLS is mainly composed of a data acquisition terminal and a data processing terminal. After relying on the Visual Studio and adopting the C/C#, a reliable SSP measurement is provided by the SSLS. When SSLS is running, sound source signals (SSSs) are collected through the array to obtain the time difference of arrival (TDOA) values in real time. Different from existing methods, this SSLS changes the SSS into a pulse signal by detecting the signal with a fixed energy intensity. At the same time, an intensity-based trigger interruption is designed to complete the TDOA acquisition. In this way, besides a reduction in TDOA calculation, the signal recognition is also improved. Further, while using TDOAs to locate the sound source, localization steps with a BP network are presented. Considering extreme sound source angles, a multi-plane based data fusion method is proposed. With the setting of different angles, especially extreme angles, we assess both quantitative and qualitative results of the proposed method, demonstrating that our method can achieve high-accuracy localization results regardless of angle degrees and the number of planes. Finally, comparison experiments confirm that the proposed SSLS significantly outperforms existing methods.

Cutting tool wear monitoring in milling processes by integrating deep residual convolution network and gated recurrent unit with an attention mechanism

The conditions of cutting tool wear in the machining process have a great influence on the dimension accuracy and surface integrity of a workpiece. Real-time tool condition monitoring can alert a worker to changes in a tool in time before tool failure occurs to reduce downtime and improve product quality. However, traditional monitoring methods are no longer able to meet the requirements of real-time monitoring in current intelligent manufacturing. Therefore, an online monitoring method based on deep learning to monitor the cutting tool conditions is proposed In this research. In this monitoring model, one-dimensional wide-kernel convolution is used to filter the high-frequency noise in the multi-dimensional raw signals, and then a deep residual convolution layer is adopted to adaptively extract the multi-dimensional spatial features of the signals, and a gated recurrent unit (GRU) layer with an attention mechanism is employed to adaptively extract the hidden temporal features of the sequence data and adaptively assign the corresponding weights, and finally, a regression layer is used to predict the wear value of the tool according to the extracted features. The PHM 2010 challenge dataset is used to illustrate and evaluate this proposed method. The experiment results show that the proposed method can accurately predict the future flank wear values in real time based on the acquired multi-dimensional signals and avoid the complexity of the manual feature extraction.

MPPT algorithm based on Multiple Linear Regression model for Solar PV systems

Currently, there are numerous maximum power point tracking (mppt) algorithms implemented in systems powered by photovoltaic (PV) panels. These systems are indirectly affected by sudden changes in weather conditions, making them unstable and inefficient. This work presents a model to establish the optimal working point of photovoltaic panels to be used in installations sensitive to changes in environmental conditions, such as solar pumping, to obtain maximum power in any temperature and irradiance condition of the panels. The model reduces the response time of the system because it also has a control system implemented whose setpoint value is the one provided by the algorithm itself. This is possible with the help of external temperature and solar radiation sensors that provide these values in real time. The setpoint is the working voltage target for the panels (Vmpp), for which the system does not become unstable despite the oscillations allowed by the control system. The algorithm is based on a model that consists of a Multiple Linear Regression through which the entire P-V curve of any photovoltaic panel can be represented for any temperature and radiation. The characteristics of the model allow a control system design that makes the system more efficient.

IoT: Based Weather Monitoring System

Abstract: The Weather Monitoring System using the IoT is one such application of IoT that has paved the way for organizations to create new and efficient solutions. Because of the rapidly changing climate, the weather forecast is uncertain and inaccurate these days. The IoT based Weather Monitoring system features to monitor temperature and humidity level, Barometric pressure, light intensity, air quality and rainfall. The required hardware includes Raspberry Pi, Arduino Mega, DHT11, Light intensity sensor BH1750, MQ-135, BME-280, and raindrop sensor.. All the above sensors are used to measure the given parameters. Arduino IDE and Node-RED software is used for programming and connecting hardware devices and online services. All sensors and hardware components are connected and built with Raspberry pi and Arduino. The arduino board is programmed with necessary code through Arduino IDE. The output of the arduino is stored in the local database using the influxdb. A Web Application dashboard is developed using HTML, CSS and JavaScript. The web application collects data from the Node-RED and the dashboard visualizes all the sensor data like temperature, humidity, barometric pressure, light intensity, air quality and rainfall. The paper gives a description of using IoT to help the user to identify and monitor the temperature, humidity, barometric pressure, air quality, light intensity and rainfall in the environment. This dashboard will update real time values of the above parameters to helpmonitor, control and maintain a particular environment.

Predicting Load Current of Electrical Distribution System Using Artificial Intelligence Techniques

Artificial Intelligence (AI) is currently used by people working in different fields of science, also the need for Smart Grids in electrical networks requires the use of intelligent technologies. Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) methods are the creation of AI. For the first time, AI methods will be used to determine the Load Current (LC) of distribution network’s buses. The objective is to test and compare ANN and ANFIS methods in order to predict LC in every bus of the I3E 33 bus system and to evaluate the possibility of using them in systems where iterative methods do not converge and also to obtain the predicted values in real time. For this comparison, two discrepancies indicators have been used to test the reliability of the two methods. Generally, the obtained results from the study have been good enough to recommend the employment of AI techniques to solve difficult problems that lack data or have large and complicated systems. In addition, ANFIS method is more precise and requires less time for training and predicting compared with ANN method, indicating that combining two or more intelligent methods can be beneficial.

Strength Predictive Modelling of Soils Treated with Calcium-Based Additives Blended with Eco-Friendly Pozzolans-A Machine Learning Approach.

The unconfined compressive strength (UCS) of a stabilised soil is a major mechanical parameter in understanding and developing geomechanical models, and it can be estimated directly by either lab testing of retrieved core samples or remoulded samples. However, due to the effort, high cost and time associated with these methods, there is a need to develop a new technique for predicting UCS values in real time. An artificial intelligence paradigm of machine learning (ML) using the gradient boosting (GB) technique is applied in this study to model the unconfined compressive strength of soils stabilised by cementitious additive-enriched agro-based pozzolans. Both ML regression and multinomial classification of the UCS of the stabilised mix are investigated. Rigorous sensitivity-driven diagnostic testing is also performed to validate and provide an understanding of the intricacies of the decisions made by the algorithm. Results indicate that the well-tuned and optimised GB algorithm has a very high capacity to distinguish between positive and negative UCS categories (‘firm’, ‘very stiff’ and ‘hard’). An overall accuracy of 0.920, weighted recall rates and precision scores of 0.920 and 0.938, respectively, were produced by the GB model. Multiclass prediction in this regard shows that only 12.5% of misclassified instances was achieved. When applied to a regression problem, a coefficient of determination of approximately 0.900 and a mean error of about 0.335 were obtained, thus lending further credence to the high performance of the GB algorithm used. Finally, among the eight input features utilised as independent variables, the additives seemed to exhibit the strongest influence on the ML predictive modelling.

Systemic analysis of a manufacturing process based on a small scale bakery.

The main aim of the article is to present two new innovative concepts of reliability of a functioning manufacturing system in the process of making bread in small-scale bakeries. Reliability is understood as one of the representations of an operator acting on specific streams in time to to t. One of these represents the global reliability of a system as a function of parallel action of all the streams of the system in time to to t and is denoted as Pg(t). The second representation of reliability is a scalar value, Pss It shows a new function of global reliability of a manufacturing process as a product of system stream reliability. In order to plot the flow of the manufacturing process’s global reliability function, we need to perform detailed calculations, computations, and analysis of the differences of individual values in real time, as well as plan an algorithm of the flow of system streams. This needs a lot of effort, translating however, to a detailed picture of the process. In the analysed example, measurements and research revealed an important increase of the value of reliability in a transition from a traditional to a robotised bakery. The article also presents a new concept of the reliability of a technological process, based on the analysis of relations of elements of the following streams: energy, matter, information, time, and finances. It shows the method of specifying streams and the method for defining the reliability of important and supportive relations. Important relations between stream elements are defined as having the reliability value of one in time. Supportive relations bear the reliability within a continuum between zero and one. Important relations are designated based on research, experience, and knowledge. Stream systemic reliability Pss is a scalar value, i.e. a number from the continuum between zero and one. The Pss value characterises failure-free operation of the whole system. Its average value in the normative time tn expresses the efficiency of the manufacturing system. The value Pss is a quotient of the number of important relation and the sum of important and supportive relations. The formula for Pss shows the method of optimising the process through the increasing of the number of important relations between the input stream components. The concept has been applied to study the efficiency of operation of a small-scale bakery. Systemic analysis of a bakery allows for important increase in the reliability of baking bread if robotisation has been implemented. The concept of systemic-stream reliability Pss may be applied to analyse the efficiency of any technological process and optimisation of any manufacturing process.

Real-time Forecasting of Electrical Power System Loads using Moving Average-Extreme Learning Machine (MA-ELM) Algorithm

Load Forecasts are the primary factors which considered by electricity utility companies while planning power generation, power infrastructural development and load flows etc. Different forecasting techniques have been proposed from statistical to artificial intelligence-based models and the area of research is still growing. In our research work, considering the real time data of 33KV bus system which is having 34 buses and 54 lines. In this case, forecast the day ahead scheduling of various parameters such as load real power (Pload), voltage magnitude at each bus, apparent power flow between buses and total transmission losses for hourly basis and also forecasted the mentioned parameters for 5 days. The actual real time values are compared with forecasted values using two existing methods namely Extreme Learning Machine (ELM), moving average and proposed Moving Average–Extreme Learning Machine (MA-ELM) algorithm. In addition to this, forecasted the loads and losses for short term and long-term forecasting cases and verified through MATLAB programming.