Average Percentage Error Research Articles

Discrepancies in the number of lines of arrested growth (LAG) in the tissues of the humerus and phalanx of sea turtles.

Information on the age of vertebrate species such as sea turtles is crucial for planning management and conservation actions. The age of sea turtles has been estimated by skeletochronological analysis using skeletal growth marks in different bones. This study focused on the consistency of the number of visible lines of arrested growth (LAG) observed from the humerus and phalanx bone used for age estimation in Chelonia mydas and Caretta caretta. We collected 67 humeri and phalanges of C. mydas (n = 47) and C. caretta (n = 20) from Samandağ beach, eastern Mediterranean in 2020-2022. LAG in the humerus and phalanx of the same individual were counted by two readers, and their consistency with each other was determined by percent agreement (PA), average percent error (APE), and coefficient of variation (CV). The significance of the difference between them was determined by the McNemar test. The mean number of visible LAG counted from the humerus is greater than the mean number of visible LAG counted from the phalanx, i.e., the humerus contains more growth marks than the phalanx. However, in individuals up to 15 LAG in C. mydas and 10 LAG in C. caretta, the mean number of visible LAG observed in both bone tissues is compatible. This was supported by the differences in the resorption rates calculated in both bones, indicating that the number of LAG lost due to resorption may also differ between these two bone types. It is recommended that the back calculation and/or correction factor applied for the humerus be avoided for the phalanx.

Predicting Water Saturation in a Greek Oilfield with the Power of Artificial Neural Networks.

Water saturation plays a vital role in calculating the volume of hydrocarbon in reservoirs and defining the net pay. It is also essential for designing the well completion. Innacurate water saturation calculation can lead to poor decision-making, significantly affecting the reservoir's development and production, potentially resulting in reduced hydrocarbon oil recovery. Various techniques to estimate the water saturation in both clean and shaly formations. However, the most widely used approaches in the petroleum industry rely on petrophysical models, including Archie's equation, Waxman-Smits, Simandoux, Indonesia, and dual-water models. Most of these methods are only valid for clean sands or carbonate, while the presence of clay significantly limits the accuracy of these models. On the other hand, the estimation of the water saturation through core analysis does not usually cover a large interval of the well, is highly costly, and requires much time. In this study, an empirical equation for predicting water saturation based on the weight and biases of the artificial neural networks (ANN) was developed. 334 data points of the shale volume, formation deep resistivity, porosity, and permeability and their corresponding water saturation collected from the Epsilon Field in Greece were considered for optimizing the ANN model. The ANN model was trained on 252 data sets, where the water saturation was predicted with an average absolute percentage error (AAPE) of 0.90%. Then, an empirical equation was developed based on the optimized ANN model and its weights and biases. The developed equation predicted the water saturation for the remaining 82 data sets (testing data) with an AAPE of 1.08%. The newly established empirical correlation enhances the precision of water saturation prediction and provides a cost-effective means to acquire a continuous water saturation profile, a critical asset for oilfield management and hydrocarbon exploration.

A Study of Embedded Fuzzy Logic to Determine Artificial Stingless Bee Hive Condition and Honey Volume

Stingless Bee is particularly nutrient-dense in his honey. Therefore, numerous beekeepers for the Stingless Bee have begun this agricultural enterprise, particularly in Malaysia. However, beekeepers encounter challenges when caring for an excessively large stingless bee colony. Due to the risk of causing colony disruption, the beekeeper cannot always access the hives to monitor honey volume and hive condition. Consequently, the purpose of this paper is to aid beekeepers and prevent disruption to bee colonies by determining the condition of the hive and the quantity of honey using an embedded fuzzy logic system. Artificial hives have been created in order to easily measure the weight of a hive of stingless bees and to divide the honey compartment from the brood compartment in order to calculate the honey volume. Since the stingless bee designs its colony with honey on top and larvae on the bottom, honey volume can be determined by weighing the honey compartment using load cell and internal humidity using dht22. DHT22 is used for measuring the internal temperature and humidity, as previous papers have stated that the hive condition can be determined using the internal temperature and humidity. Morever, FLDa (Fuzzy Logic Designer app) by MATLAB was subsequently utilised to construct membership function, rules, fuzzification, and defuzzification. Then, the same input, membership function, and rules that used in FLDa will be implemented on the Nodemcu ESP8266 using eFLL (Embedded Fuzzy Logic Library). A comparison between the crisp output from FLDa and the crisp output from eFLL was conducted to determine whether eFLL is suitable for use in the NodeMCU ESP8266. As a consequence, the standard deviation and averaged percentage error of differences for hive condition, which is 0.22 and 0.17%, is less than the honey volume, which is 0.49 and 0.66%, because hive condition has a strict correlation with temperature. The hive condition will be rated bad (0% when the temperature is cold or hot state), but it will be rated good (100% when the temperature is normal state). As for honey volume, the majority of results correspond to the percentage of honey compartment weight, unless the humidity is dry state, which will cause the value to be cut in half. Finally, the fuzzy logic system is effectively implemented into an embedded system, making it easier for the beekeeper to monitor the hive condition and honey volume without interfering with the activity of stingless bees.

Data Driven-Based Machine Learning Modelling and Empirical Correlations for Predicting Snow-Covered Area in the Swat Region, Pakistan

In recent decades, global and regional climate change has emerged as a significant challenge with potential catastrophic consequences, including hurricanes, floods, sea level rise, and temperature shifts. Snow-covered area (SCA) serves as a crucial climatic parameter reflecting climate changes, yet accurately determining SCA proves to be a challenging and time-consuming task. This study aims to develop robust prediction models for SCA by employing three machine learning (ML) approaches using readily available climatic data from Swat, Pakistan, spanning two decades. The climate data encompass precipitation, daily maximum/minimum temperatures, and SCA measurements. Three ML methods—artificial neural networks (ANN), functional networks (FN), and adaptive neuro-fuzzy inference systems (ANFIS)—were employed to model SCA. Accuracy measures, including coefficient of determination (R2), average absolute percentage error (AAPE), and root mean squared error (RMSE) were utilized to evaluate model performance. All three ML models exhibited superior performance, with high R2 values and low errors. Accuracy indicators of the ANN model are better than FN and ANFIS models, yielding the highest R2 (0.956) and minimum RMSE and AAPE values (0.61 and 0.91). ANFIS demonstrated slightly better performance than FN, with RMSE, AAPE, and R2 values of 0.65, 1.1, and 0.950, respectively. FN yielded RMSE, AAPE, and R values of 1.14, 1.72, and 0.85, respectively. Additionally, two empirical correlations were derived from the optimized FN and ANN models for SCA prediction using the same input variables. This study underscores the efficacy of ML techniques in accurately and consistently predicting SCA parameters, offering valuable insights into climate change and its consequences.

Advanced integration of IoT and AI algorithms for comprehensive smart meter data analysis in smart grids

Abstract Smart grids utilize advanced communication, information, and control technologies to build a more friendly power supply system. However, the development of technology leads to the increasing complexity of smart meter (SM) data in the power grid, and analyzing SM data becomes difficult. Therefore, in order to realize the effective analysis and forecasting of users’ electricity consumption behavior, an SM data analysis technique integrating the Internet of Things and artificial intelligence is proposed, which contains three major techniques: meter reading systems, load clustering, and load forecasting, and at last the technique is validated. Finally, the technology is validated. The experimental results showed that the average value of the Davis-Balding index for the proposed method combining recursive graphs and convolutional autoencoders was 1.87, which was significantly lower than the average value of 2.35 for other methods. The average value of the Dunn index was 0.086, which was higher than the 0.065 for the comparison method. In the load forecasting model, the model that employs multi-task learning and a gated cycle unit demonstrates superior performance across all comparison schemes, with the lowest average absolute percentage error of 12.90%. This is significantly lower than the comparison model’s 15.35%, which highlights the enhanced efficacy of the proposed model. The study realizes the effective analysis and prediction of consumers’ electricity consumption behavior and provides new ideas and solutions for the optimization and management of smart grids.

Studies of a Mechanically Pumped Two-Phase Loop with a Pressure-Controlled Accumulator Under Pulsed Evaporator Heat Loads

As avionics become more power dense, electronic device cooling has become a significant barrier to aircraft integration. A mechanically pumped two-phase loop (MPTL) is a thermal subsystem that enables near isothermal evaporator operation, which is desirable for electronics cooling. The goal of this study was to integrate an MPTL with a pressure-controlled accumulator and model a predictive control technique to demonstrate improvements for transient, isothermal evaporator operation for MPTLs under pulsed evaporator heat loads. The model predictive controller enables active control of MPTL compressible volume, which has not been demonstrated for pulsed evaporator heat loads. Experimental data were collected to validate a representative numerical model. A pressure-controlled accumulator was added to an MPTL to experimentally characterize the system thermodynamic response for three pulsed evaporator heat loads. Two statistical methods were used to assess the numerical model agreement with the experimental results. Under pulsed evaporator heat loads, the mean percent error agreed within 3.45% and the mean average percent error agreed within 0.74% for the three pulsed evaporator heat loads. Finally, a traditional proportional–integral (PI) controller and an advanced model predictive controller were developed and integrated into the validated numerical model. Both control methods were evaluated for an expanded set of evaporator heat load profiles to analyze transient behavior. For evaporator heat profiles with high heat transfer rates, the model predictive controller can maintain a target ±2 K refrigerant temperature at the evaporator exit throughout the evaporator heat load duration, whereas the PI-controlled MPTL cannot. Through this work, active control of a pressure-controlled accumulator within an MPTL is shown to improve refrigerant isothermal (±2 K) operation when compared to a traditional control technique.

Boiling Heat Transfer Characteristics of Noah-2100A and HFE-649 in Pin-Fin Microchannel Heat Sink

Noah-2100A and HFE-649, as two electronics fluorinated liquids (EFLs) with low saturation temperature, high safety, excellent insulation properties, and low environmental impact, are considered as replacements for the refrigerants with high Global Warming Potential (GWP), such as HFC-134a and HFC-245fa, in electronic cooling system. However, there is still a knowledge gap of boiling heat transfer for these two EFLs, especially in pin-fin microchannel. The effect of inlet temperatures, mass flow rates, and inlet vapor qualities on boiling heat transfer for two EFLs were studied experimentally in this paper. Overall, though the Noah-2100 has a higher pressure drop-in microchannel than HFE-649, Noah-2100A shows a higher overall thermal performance than HFE-649. Newly developed correlations of the Nusselt number (Nu) and pressure drop for two EFLs in a pin-fin microchannel heat sink were also presented. The proposed correlations can achieve a 10% and 11% mean average percentage error for Nu number and pressure drop.

Predicting the Rate of Penetration while Horizontal Drilling through Unconventional Reservoirs Using Artificial Intelligence.

Estimating the rate of penetration (ROP) is one of most critical tasks for evaluating the efficiency and profitability of drilling operation, which will aim in decision-making related to well planning, time estimation, cost estimation, bit selection, operational troubles, and logistics in drilling operation. The rise in unconventional resource development underscores the need for accurate ROP prediction to optimize drilling operations in these valuable reserves. ROP prediction and optimization in unconventional hydrocarbon reservoirs are challenging due to the formations' heterogeneity, high strength, and brittleness. These reservoirs often involve complex well designs, high pressures, and high temperatures, making it difficult to maintain optimal drilling conditions. This study presents the optimization and validation of the artificial neural network (ANN) model to predict the ROP during horizontal drilling through unconventional hydrocarbon reservoirs. The ANN model was trained using 34,869 data points from five wells (Well-1 to Well-5) and achieved a high correlation coefficient of 0.96 and an average absolute percentage error (AAPE) of 4.68%. An empirical correlation was developed based on the weights and biases of the optimized ANN model. The empirical correlation performance was rigorously tested with 23,246 data points, representing 40% of the data from the same wells, yielding an AAPE of 4.75% and a correlation coefficient of 0.96. Validation of the developed equation on data from Well-6 further confirmed the model's robustness, maintaining a correlation coefficient of 0.91 and an AAPE of 5.75%. These results demonstrate the ANN model's and empirical equation's accuracy and reliability in predicting the ROP, highlighting their potential to optimize drilling operations in unconventional hydrocarbon reservoirs.

Forecasting power system flexibility requirements: A hybrid deep-learning approach

The rapid growth of Variable and Renewable Energies (VRE) worldwide poses significant challenges to power systems, particularly in managing rapid changes in load and generation. A critical research gap exists in effectively forecasting Residual Load (RL) to enhance power system flexibility, as existing methods often struggle with the volatility and non-linearities introduced by VRE. This paper addresses this gap by proposing a novel hybrid forecasting framework that integrates Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and a Convolutional Long Short-Term Memory (ConvLSTM-2D) Artificial Neural Network (ANN). This framework is designed to more accurately capture complex patterns in RL data, thereby improving forecast accuracy under high volatility conditions. We apply this methodology to forecast half-hourly RL for 2021 in the French region of Occitanie, achieving an average Mean Absolute Percentage Error (MAPE) of 4.29 %. Our results demonstrate that the proposed framework significantly outperforms existing models, offering consistent accuracy over time and proving highly suitable for practical daily use among energy stakeholders.

Data Driven Approach for Predicting Pore Pressure of Oil and Gas Wells, Case Study of Iraq Southern Oilfields

Precise forecasting of pore pressures is crucial for efficiently planning and drilling oil and gas wells. It reduces expenses and saves time while preventing drilling complications. Since direct measurement of pore pressure in wellbores is costly and time-intensive, the ability to estimate it using empirical or machine learning models is beneficial. The present study aims to predict pore pressure using artificial neural network. The building and testing of artificial neural network are based on the data from five oil fields and several formations. The artificial neural network model is built using a measured dataset consisting of 77 data points of Pore pressure obtained from the modular formation dynamics tester. The input variables are vertical depth, bulk density, and acoustic compressional wave velocity, with the activation function of tangent sigmoid. The average percent error, absolute average percent error, mean square error, root mean square error, and correlation coefficient (R2) were applied for evaluation. The results revealed that the best artificial neural network structure was (3-8-1), with average percent error, absolute average percent error, mean square error, root mean square error, and correlation coefficient R2 of -0.52, 1.01, 3994, 63.2, and 0.995, respectively. A C++ computer program is provided with a calculation sample to simplify the implementation of the proposed artificial neural network. The dependency degree of pore pressure on each input parameter is investigated, revealing the highest impact of depth on pore pressure prediction. Furthermore, to check the validity of the artificial neural network against the different datasets, the artificial neural network performance was compared with 84 new data points and showed an advantage over the existing models. The very good performance of artificial neural network for different types of oil reservoirs and formations reveals an insignificant effect of lithology on the prediction of pore pressure.

Design and validation of a power modulation system for residential demand-side management

Residential loads have great potential to provide flexibility and other services to the grid, but many legacy or non-networked loads need additional hardware to enable such functionality. Currently available devices (e.g., smart thermostats or load control switches) that equip legacy loads with energy management features, provide narrow functionality to address specific use cases.We propose the Smart Dim Fuse (SDF), a unified system with general purpose hardware, to enable legacy residential loads with versatile grid-interactive functionalities. By combining sensing, power electronics, and load modeling into a single architecture, the SDF offers comprehensive capabilities that would otherwise require a large number of disparate devices that are not inherently compatible. Based on a thoroughly tested prototype, we suggest that such a device can deliver this flexibility at a levelized cost of 0.018-0.052 $/kWh. The prototype power electronics operates at efficiencies between 96.4-98.5% at full load. The system can deliver fast load power modulation with a mean average percentage error below 1.8%.

Co-estimation of state of health and remaining useful life for lithium-ion batteries using a hybrid optimized framework

The accurate estimation of the state of health (SOH) and remaining useful life (RUL) in lithium-ion batteries (LIBs) is an important factor in assessing their operational characteristics such as robustness, efficiency, and effectiveness. The SOH and RUL are co-estimated to mitigate the LIB risk, limit unwanted fault occurrences, and ensure LIB safety. However, estimating the SOH and RUL is difficult considering the complex internal mechanism of the LIB and transients in operational performance due to aging. The currently available SOH and RUL estimation methods have demonstrated the inability to extract the right samples of data from battery parameters, choosing the right model hyperparameters and minimizing the capacity regeneration effect to produce reliable results. Furthermore, several research works using different optimization techniques suffer from premature convergence, local minima trap and slow convergence. Therefore, the novelty of this work is to deliver an improved hybrid optimized framework to co-estimate the SOH and RUL of LIBs which successfully addresses the above-mentioned limitations. The work utilizes a hybrid optimized gravitational search algorithm (GSA) and particle swarm optimization (PSO) model to optimize the cascaded forward neural network (CFNN) model parameters. First, the LIB dataset is acquired from the reliable NASA battery database, and its operational profiles are analysed to extract suitable parameters. Then, an appropriate data framework consisting of 31 samples is developed from the LIB using the systematic sampling method. The final dataset is developed considering four batteries which depicts high data dimensionality. The 31-dimensional data samples from the complete charging profile are acquired to train the proposed hybrid GSA-PSO-CFNN model for SOH estimation which attains improved search capability with fast convergence. Furthermore, the capacity regeneration effect from the outcomes with SOH estimation is minimized using the sliding window for RUL estimation. Moreover, the GSA-PSO-CFNN model is validated with other GSA-PSO optimized models and the MIT-Stanford battery dataset. High accuracy is achieved, with an average root mean square error (RMSE) and mean absolute percentage error (MAPE) for SOH and RUL estimation with the NASA dataset being 0.04715, 0.0225, and 2.3983, 1.4475, respectively, which confirms the high applicability of the GSA-PSO-CFNN model. The discussed co-estimation is applicable with the current battery management system (BMS) in electric vehicle (EV) applications for accurate estimation outcomes.

Big Data Generation and Comparative Analysis of Machine Learning Models in Predicting the Fundamental Period of Steel Structures Considering Soil–Structure Interaction

The computing of the fundamental period of structures during seismic design is well documented in design codes but is mainly dependent on the height of the structure, which is considered to be the most influential parameter. It is, however, important to consider a phenomenon called the soil–structure interaction (SSI), as this has been found to have a detrimental effect, especially for buildings founded on soft soils. A pilot research project foresaw the use of machine learning (ML) algorithms trained on relatively limited datasets for the development of a more accurate and objective fundamental period formula. Therefore, a dataset that consists of 98,308 fundamental period data points was created through the use of a High-Performance Computer (HPC), which is the largest dataset of its kind. The HPC results were then used to train, test, and validate different ML algorithms. It was found that XGBoost-HYT-CV with hyperparameter tuning performed the best with a correlation of 99.99% and a mean average percentage error (MAPE) of 0.5%. Furthermore, the XGBoost-HYT-CV model outperformed all under-study ML models when using an additional dataset that consisted of out-of-sample building geometries and soil properties, with a resulting MAPE of 9%. Finally, irregular buildings were also used to test the performance of the proposed predictive models.

Two-stage dual-attention spatiotemporal joint network model for multi-energy load prediction of integrated energy system

Accurate prediction of multi-energy load is essential for the scheduling, safe operation, and stability of integrated energy systems. This paper proposes a novel two-stage multi-energy load prediction model with dual-attention spatiotemporal joint network for integrated energy system. In the first stage of multi-task prediction, the integrated self-attention long short-term memory (LSTM) network is built to forecast the multi-energy load initially, and an error correction model on basis of BP network is constructed to reduce the impact of error accumulation on the second stage prediction. In the second stage of single-task prediction, a parallel spatiotemporal joint network model is proposed to extract the static spatial feature and dynamic temporal feature between multi-energy loads and between loads and meteorological factors, which fully captures the complex interactions and dependencies between the multi-energy loads. The prediction model integrates self-attention and graph attention mechanisms in both stages to improve the feature extraction capabilities. The simulation results demonstrate that, compared to bidirectional LSTM, LSTM-gated recurrent unit, time convolutional networks-LSTM and variational mode decomposition-convolutional neural network-LSTM models, the proposed multi-energy load prediction model reduces the average absolute percentage error by 66.25%, 16.83%, 53.58% and 5.29%, respectively.

Use of bore-epoxy anchorage system with CFRP laminates for shear strengthening of RC beams: An experimental and analytical investigation

Shear strengthening of reinforced concrete (RC) beams with externally bonded reinforcement (EBR) using carbon fiber reinforced polymer (CFRP) plates and sheets have become a widely accepted practice. To avoid the associated premature debonding failure, this study presents an experimental and analytical investigation on the use of bore-epoxy as an anchorage system for CFRP plates and sheets bonded on both sides of shear deficient RC beams of rectangular cross-sections. Nine (9) RC beams were prepared and strengthened with CFRP plates and sheets as EBR with bore-epoxy anchorage system considering different bore diameters and tested under four point bending. The results indicate that RC beams strengthened with bore-epoxy anchorage system have high shear strength as compared with the unstrengthened control beam and those strenghtended with the conventional EBR approach (without boring). The increase in the shear strength over the control beam was found to be up to 67 % and 121 % for cases of CFRP plates and sheets, respectively. Moreover, the increase of shear strength in comparison with the conventional EBR method ranged from 9.33 % to 20.36 % for CFRP plates and from 12.97 % to 36.10 % for CFRP sheets. Consistently, the contribution of bore-epoxy on the shear strength increased with the increase of bore diameter. Consequently, the existing formulas in the ACI440.2 R for predicting the shear strength of FRP strengthened RC beams were modified to incorporate the improvement made by the proposed bore-epoxy approach. The revised models predicted the experimental shear strength of RC beams, strengthened with bore-epoxy, with a good level of accuracy with average mean absolute percentage error (MAPE) = 6.07 %, 14.71 %, normalized mean square error (NMSE) = 0.097, 0.16, and coefficient of determination R2 = 0.912, 0.974 for plates and sheets, respectively.

Efficient Model for Estimation of Dew Point Pressure in Gas Condensate Systems

Gas condensate reservoirs are a special class of gas reservoirs with temperatures which plot between the critical point temperature Tc and the cricondentherm Tcric on a Pressure–Temperature (P-T) phase diagram. The reservoirs’ produced streams are characterized by relatively, low C1 : C2 +ratio, implying compositions with significant fractions of high molecular weight (C2 +) hydrocarbons, called Natural Gas Liquids (NGLs). NGLs exist as gas at reservoir conditions of high pressures and temperatures but as liquids, called condensates (or distillates) at separator conditions, havinge higher market price than separator gas fractions. Condensation of NGLs can occur within reservoirs if reservoir pressures fall to the reservoir’s Dew Point Pressure (DPP) during production. Condensates formed within reservoirs are never produced owing to their less than critical saturation. Therefore, accurate estimation of DPP and ensuring production occurs above such estimates, is necessary for ensuring optimum productivity and profit. Correlations have proven valuable in providing quick DPP estimates, however, the several correlating parameters of common types, make them computationally intense, yet their associated errors remain immense. This study aims at presenting a simple correlation, with fewer correlating parameters that would guarantee better accuracy when compared to other popular models. A new correlation for DPP as a function of gas compositional analysis data and reservoir temperature was developed using method of multiple regression analysis. A total of 1,568 gas condensate datasets from 984 reservoirs obtained from literature with unrestricted geographic locations were used to develop the model. These data were divided in the ratio of 3:1:1 for training, cross validation and testing. The results obtained showed that the new correlation significantly outperforms other existing popular industry models and provides predictions with higher accuracy. This was verified in terms of highest correlation coefficient, lowest average absolute percentage error and lowest root mean square error

Modeling of an inductive displacement sensor based on 1DCNN-LSTM-AT

Abstract The input-output relationship of inductive displacement sensors is typically nonlinear, which demands numerous computational resources for precise calculation. Therefore, the traditional analytical methods for the inductive displacement sensors cannot meet the real-time high-precision measurement requirements. In response to the aforementioned issues, this paper proposes an optimized Long Short-Term Memory (LSTM) neural networks based on one-dimensional convolutional neural networks (1DCNN) to modeling the input-output relationship of the inductive displacement sensor. First, the measurement principle of the inductive displacement sensors was analyzed, and the analytical model of the sensor output was derived. And the influences of the key parameters of sensors on the relationship between the induced voltage and the displacement were studied. Then, the 1DCNN-LSTM-AT network for modelling the input-output relationship of the inductive displacement sensor was studied. The spatial features of historical induced electromotive force (EMF) data generated by the induction coil were initially extracted using the 1DCNN network. And these spatial features were then utilized as input to the LSTM neural network to capture the temporal features of the historical induced EMF data. Subsequently, the spatiotemporal features of the induced EMF data were fed into the regression prediction layer to compute the displacement measurement results corresponding to the current input. Moreover, the attention mechanism was used for the 1DCNN-LSTM model to enhance the prediction accuracy and stability of the model. Finally, the experimental results demonstrate that the proposed 1DCNN-LSTM-AT model achieves an average absolute percentage error of 3.1%, significantly outperforming traditional models such as LSTM (29.3%), CNN (4.7%), and ANN (4.3%). This paper provides a new method for modelling the nonlinear relationships of the inductive displacement sensor, and presents a fresh perspective for research in the data processing of nonlinear sensors.

Cell Temperature Determination based on IEC61215: Solar Photovoltaic Experimental Study of Tropical Malaysia

Most commercial photovoltaic (PV) module data sheets include Nominal Operating Cell Temperature (NOCT) values, which assist PV system designers in estimating module temperatures under real outdoor conditions. However, the typical NOCT values of 45°C to 47°C do not account for tropical climates. This study seeks to develop an adjusted NOCT mathematical model and determine revised NOCT values suited for tropical conditions. The new proposed NOCT model follows the international standard IEC61215 but incorporates updated tropical Standard Reference Environment (SRE) parameters: solar irradiance (SI) of 800 W/m², ambient temperature (AT) of 31°C, and wind speed (WS) of 1 m/s. This modified NOCT model demonstrates improved accuracy over the existing model, with an average reduction of 2% in percentage error, root mean square error, and mean average percentage error. Outdoor NOCT testing conducted in Shah Alam, Malaysia, uncovered much higher NOCT values for various PV modules technology: 55°C for monocrystalline, 57°C for polycrystalline, and 59°C for thin film. These results emphasize the inadequacy of current NOCT values in commercial PV module data sheets, which do not accurately reflect conditions in tropical climates. The revised NOCT values from this study offer crucial thermal reference data for PV system designers, integrators, and researchers working in tropical regions, particularly Malaysia.

IoT-Based Control, Monitoring, and Protection System for 3-Phase Induction Motors in Electric Motorcycles

This study investigates the application of the Internet of Things (IoT) for wireless control and monitoring of a 3-phase electric motor using a smartphone. The system integrates PZEM-004T, DS18B20, and Hall Effect sensors to collect data on voltage, current, temperature, and rotational speed using the NodeMCU ESP8266 microcontroller. Measurements are displayed on an LCD and transmitted to the Blynk server for smartphone access. A comparative method evaluates the accuracy of sensor readings against standard measuring instruments. Results obtained an average percentage error of 0.5% for the R phase voltage, 0.2% for the S phase, and 0.1% for the T phase. Current measurements reveal errors of 5% for the R phase, 10.3% for the S phase, and 11.7% for the T phase. The control system’s performance varies with internet speed, with an average delay of 0.99 seconds on a 4G network and 2.51 seconds on 3G. Additionally, the study evaluates three protection mechanisms, demonstrating that the motor stops within 4.03 seconds in the event of a phase failure, while overcurrent and overheating protections activate within 8.47 seconds and 3.64 seconds, respectively. Overall, the findings affirm the viability of IoT in motor monitoring and control, emphasizing accuracy and response times under varying conditions.

Approximate fundamental frequency formula for cantilevers with weakly non-uniform sections and verification with experiments and other studies in the literature

The vibrational frequencies of beams with uniform cross-sections are easily derived from the analytical solutions of the Euler-Bernoulli equation (EBE). However, for beams with non-uniform cross-sections, complex and time-consuming numerical solutions are typically required for each cross-sectional geometry. This paper presents a formula that accurately predicts the vibrational frequencies in the presence of weak heterogeneities, regardless of how the cross-sectional shape varies along the beam's length. Experimental verification and comparisons with literature confirm that the formula reliably predicts frequency shifts in cases where a thin heterogeneous film is coated on a uniform beam, thin heterogeneous layers are removed from a uniform beam, and when a moving point load affects the vibrational frequency. The average absolute percentage error between the experimentally measured and calculated frequencies was <0.17 %. As such, this formula serves as a practical tool for various engineering applications involving weakly heterogeneous beams.