Multilayer Feed-forward Artificial Neural Network Research Articles

Abstract 101: Predicting Hematoma Expansion Using An Artificial Neural Network: An Exploratory Analysis Of The Atach 2 Trial

Introduction: Hematoma expansion (HE) in patients with intracerebral hemorrhage (ICH) is a key predictor of poor prognosis and potentially amenable to treatment. Current clinical scoring models, radiological markers, and labs have limited ability to predict HE. This study aimed to create a more robust classification model to predict HE in patients with ICH using a deep learning algorithm, an artificial neural network (ANN). Methods: Data from the ATACH 2 trial (Antihypertensive Treatment of Acute Cerebral Hemorrhage) was utilized. Variables included in the models were chosen as per literature consensus on salient factors associated with HE and clinical expertise. HE was defined as increase in either >33% or 6mL in hematoma volume in the first 24 hours. A multi-layer feedforward ANN was trained using the back-propagation method to minimize the loss function with 5-fold cross validation. 80% of patients were used for training and 20% for testing. The ANN was compared to a logistic regression model (GLM). Given the imbalance in patients with HE, AUPRC (area under the precision-recall curve), recall and precision were calculated for the respective models. Results: Of the 963 patients in the study (mean age 62±13.0, 38.5% female), 31% had hematoma expansion. The median [interquartile range (IQR)] initial hematoma volume was 10.5mm 3 [5.16 - 20.25], median admission SBP [IQR] was 200 [184-217], median platelet count [IQR] was 213 [178-256], median INR [IQR] was 1.0 [0.9-1.0], and median admission GCS [IQR] was 15 [13-15]. The GLM model had AUPRC of 0.38 with recall of 0.60 and precision of 0.38 in the testing cohort. The ANN training model demonstrated AUPRC of 0.92 with recall of 0.72 with precision of 0.39 during testing. Initial ICH volume, platelets, INR, and GCS had the highest feature importance. Conclusion: We developed an ANN to predict HE in ICH patients performing with improved sensitivity and similar positive predictive value when compared to GLM. We support that ANN can capture complex associations between variables not attainable in classic regression models. This model may help identify patients at risk for HE who warrant careful monitoring and aggressive treatment upfront including those suitable for clinical trials for treatments.

Mapping the Abundance of Multipurpose Agroforestry Faidherbia albida Trees in Senegal

Multi-purpose Faidherbia albida trees represent a vital component of agroforestry parklands in West Africa as they provide resources (fodder for livestock, fruits and firewood) and support water lifting and nutrient recycling for cropping. Faidherbia albida trees are characterized by their inverse phenology, growing leaf flowers and pods during the dry season, thereby providing fodder and shedding leaves during the wet season, which minimizes competition with pastures and crops for resources. Multi-spectral and multi-temporal satellite systems and novel computational methods open new doors for classifying single trees and identifying species. This study used a Multi-Layer Perception feedforward artificial neural network to classify pixels covered by Faidherbia albida canopies from Sentinel-2 time series in Senegal, West Africa. To better discriminate the Faidherbia albida signal from the background, monthly images from vegetation indices were used to form relevant variables for the model. We found that NDI54/NDVI from the period covering onset of leaf senescence (February) until end of senescence (leaf-off in June) to be the most important, resulting in a high precision and recall rate of 0.91 and 0.85. We compared our result with a potential Faidherbia albida occurrence map derived by empirical modelling of the species ecology, which deviates notably from the actual species occurrence mapped by this study. We have shown that even small differences in dry season leaf phenology can be used to distinguish tree species. The Faidherbia albida distribution maps, as provided here, will be key in managing farmlands in drylands, helping to optimize economic and ecological services from both tree and crop products.

Stability of biochar derived from banana peel through pyrolysis as alternative source of nutrient in soil: feedforward neural network modelling study.

Biochar derived from banana peels can be used as an alternative nutrient in the soil that can promote crop growth while reducing fertiliser usage. Biochar stability has proportional relationship to biochar residence time in the soil and potassium is one of the vital nutrients needed for plant growth. This research aims at providing optimum pyrolysis operating conditions like temperature, residence time, and heating rate using banana peels as feedstock. An electrical tubular furnace was used to conduct the pyrolysis process to convert banana peels into biochar. The elemental compositions of biochar are potassium, oxygen (O), and carbon (C) content. The O:C ratio was used as the biochar stability indicator. Analysis of results showed that operating temperature has the most remarkable effect on biochar yield, biochar stability, and biochar's potassium content. In addition, a multilayer feedforward artificial neural network model was developed for the pyrolysis process. Eleven training algorithms were selected to model the multi-input multi-output neural network (MIMO). The most suitable training algorithm was identified through four performance criterions which are root mean square error (RMSE), mean absolute error (MSE), mean absolute percentage error (MAPE), and regression (R2). The results show that the Levenberg-Marquardt backpropagation training algorithm has the lowest error. From the chosen training algorithm, neural network was trained, and optimum operating parameters for banana peel were predicted at 490°C, 110min, and 11°C/min with a high yield of 47.78%, O/C ratio of 0.2393, and 14.04 wt. % of potassium.

Comparison of PV Power Generation Forecasting in a Residential Building using ANN and DNN

Due to the fast growth of energy consumption in buildings, it is crucial to ensure sustainability demands through the use of renewable energies. The solar energies have been standing out and, as a result, the forecast of photovoltaic (PV) production has received broad attention. However, the intermittent nature of the generated power brings some uncertainty. This paper presents two PV generation forecasting methodologies based on a multi-layer feed-forward Artificial Neural Networks (ANN) and a Deep Neural Networks (DNN) combined with a Convolution Neural Network layer and a Recurrent Neural Network layer. Both techniques were implemented based on a data set of a PV production from a panel installed at a residential building. The main objective of this paper is to analyze and compare the forecasting results precision of both techniques. The accuracy of both models was evaluated through the calculated errors. The comparative analysis between the two networks demonstrated that the ANN technique is capable of predicting the PV generation with low forecasting errors.

Prediction of ground subsidence due to underground mining through time using multilayer feed-forward artificial neural networks and back-propagation algorithm – case study at Mong Duong underground coal mine (Vietnam)

The paper is devoted to studying the possibility of using artificial neural networks (ANN) to estimate ground subsidence caused by underground mining. The experiments showed that the most suitable network structure is a network with three layers of perceptrons and four neurons in the hidden layer with the back propagation algorithm (BP) as a training algorithm. The subsidence observation data in the Mong Duong underground coal mine and other parameters, including: (1) the distance from the centre of the stope to the ground monitoring points; (2) the volume of mined-out space; (3) the positions of the ground points in the direction of the main cross-section of the trough; and (4) the time (presented by cycle number), were used as the input data for the ANN. The findings showed that the selected model was suitable for predicting subsidence along the main profile within the subsidence trough. The prediction accuracy depended on the number of cycles used for the network training as well as the time interval between the predicted cycle and the last cycle in the training dataset. When the number of monitoring cycles used for the network training was greater than eight, the largest values of RMS and MAE were less than 10 % compared to the actual maximum subsidence value for each cycle. If the network training was less than eight cycles, the results of prediction did not meet the accuracy requirements.

Enhanced Mineral Quantification and Uncertainty Analysis from Downhole Spectroscopy Logs Using Variational Autoencoders

This paper describes an innovative machine-learning application, based on variational autoencoder frameworks, to quantify the concentrations and associated uncertainties of common minerals in sedimentary formations using the measurement of atomic element concentrations from geochemical spectroscopy logs as inputs. The algorithm comprises an input(s), encoder, decoder, output(s), and a novel cost function to optimize the model coefficients during training. The input to the algorithm is a set of dry-weight concentrations of atomic elements with their associated uncertainty. The first output is a set of dry-weight fractions of 14 minerals, and the second output is a set of reconstructed dry-weight concentrations of the original elements. Both sets of outputs include estimates of uncertainty on their predictions. The encoder and decoder are multilayer feed-forward artificial neural networks (ANN), with their coefficients (weights) optimized during calibration (training). The cost function simultaneously minimizes error (accuracy metric) and variance (precision or robustness metric) on the mineral and reconstructed elemental outputs. Training of the weights is done using a set of several-thousand core samples with independent, high-fidelity elemental and mineral (quartz, potassium-feldspar, plagioclase-feldspar, illite, smectite, kaolinite, chlorite, mica, calcite, dolomite, ankerite, siderite, pyrite, and anhydrite) data. The algorithm provides notable advantages over existing methods to estimate formation lithology or mineralogy relying on simple linear, empirical, or nearest-neighbor functions. The ANN numerically capture the multidimensional and nonlinear geochemical relationship (mapping) between elements and minerals that is insufficiently described by prior methods. Training is iterative via backpropagation and samples from Gaussian distributions on each of the elemental inputs, rather than single values, for every sample at each iteration (epoch). These Gaussian distributions are chosen to specifically represent the unique statistical uncertainty of the dry-weight elements in the logging measurements. Sampling from Gaussian distributions during training reduces the potential for overfitting, provides robustness for log interpretations, and further enables a calibrated estimate of uncertainty on the mineral and reconstructed elemental outputs, all of which are lacking in prior methods. The framework of the algorithm is purposefully generalizable so that it can be adapted across geochemical spectroscopy tools. The algorithm reasonably approximates a “global-average” model that requires neither different calibrations nor expert parameterization or intervention for interpreting common oilfield sedimentary formations, although the framework is again purposefully generalizable so it can be optimized for local environments where desirable. The paper showcases a field application of the method for estimating mineral type and abundance in oilfield formations from wellbore-logging measurements.

A Hybrid Approach to Classify Power Quality Problems in Distribution Systems

Electrical power systems are expected to transmit continuously nominal rated sinusoidal voltage and current to consumers. However, the widespread use of power electronics has brought power quality problems. This study performs classification of power quality disturbances using an artificial neural network (ANN). The most appropriate ANN structure was determined using the Box-Behnken experimental design method. Nine types of disturbance (no fault, voltage sag, voltage, swell, flicker, harmonics, transient, DC component, electromagnetic interference, instant interruption) were investigated in computer simulations. The feature vectors used in the identification of the different types of disturbances were produced using the discrete wavelet transform and principal component analysis. Our results show that the optimized feed forward multilayer ANN structure successfully distinguishes power quality disturbances in simulation data and was also able to identify these disturbances in real time data from substations.

The use of an artificial neural network for predicting the gloss of thermally densified wood veneers

In this study, an artificial neural network (ANN) model was developed to predict the gloss of thermally densified wood veneers. A custom application created with MATLAB codes was employed for the development of the multilayer feed-forward ANN model. The wood species, temperature, pressure, measurement direction, and angle of incidence were considered as the model inputs, while the gloss was the output of the ANN model. Model performance was evaluated by using the mean absolute percentage error (MAPE), the root mean square error (RMSE), and the coefficient of determination (R²). It was observed that the ANN model yielded very satisfactory results with acceptable deviations. The MAPE, RMSE, and R2 values of the testing period of the ANN model were found as 8.556%, 1.245, and 0.9814, respectively. Consequently, this study could be useful for the wood industry to predict the gloss with less number of tiring experimental activities.

Degradation of Bisphenol A by ozonation in a rotating packed bed: Modeling by response surface methodology and artificial neural network

The ozonation process of Bisphenol A (BPA) in a rotating packed bed (RPB) was modeled by response surface methodology (RSM) and artificial neural network (ANN). Experiments were performed according to the Box-Behnken design, and the interactive effects of various parameters including ozone concentration, pH, rotation speed of RPB and liquid flow rate on BPA degradation efficiency were investigated. Ozone concentration and pH had the most significant interactive effects on BPA degradation efficiency while rotation speed of RPB had no significant interactive effects with other variables. A second order polynomial equation was obtained to predict BPA degradation efficiency. Also, a multi-layered feed-forward ANN model was constructed based on the data of RSM experiments. Six neurons in hidden layer had the highest correlation coefficient (RANN = 0.99158). A comparison between RSM and ANN models suggested that both can accurately predict BPA degradation efficiency (RRSM = 0.99559). The highest BPA degradation efficiency (99.52 %) was achieved under the conditions of ozone concentration of 20 mg L−1, pH of 11, liquid flow rate of 10 L h−1 and rotation speed of RPB of 800 rpm, which was well predicted by the RSM model (99.54 %) and the ANN model (99.82 %). However, the RSM model was slightly better than the ANN model owing to its higher determination coefficient (R2RSM = 0.9912, R2ANN = 0.9827) and lower mean square error (MSERSM = 0.0001684, MSEANN = 0.0003305).

Real Time Control Application of the Robotic Arm Using Neural Network Based Inverse Kinematics Solution

Robotic arms are widely used in many industrial applications at present. The control of robotic arms involves position coordination Cartesian space by a forward/inverse kinematics solution method. The inverse kinematics is difficult for real-time control applications, computational requirements are intensive and the run-time is high. The traditional solution methods used geometric, algebraic, and numerical iterative techniques are inadequate and slow in the inverse kinematics solution. Recently, alternative solution methods based on artificial intelligence techniques have been developed to solve the inverse kinematics problem. In this study, a multi-layered feed-forward Artificial Neural Network model was developed to solve the inverse kinematics of the 5 degrees of freedom robotic arm. Using the Proportional-Integral control algorithm combined with this Artificial Neural Network model, the real-time position control of the robotic arm was accomplished. The obtained results were compared with the PI control supported by an analytical inverse kinematics solution in real-time. The results showed that the PI control combined with Artificial Neural Network has superior tracking ability, smaller control error, and better absolute fit to the reference.

Application of Artificial Neural Network for Prediction of Local Void Fraction in Vertical Subcooled Boiling Flow

This paper presents the feasibility study of potential application of multi-layer feed-forward Artificial Neural Networks (ANN) to predict local void fraction of subcooled boiling flows in vertical upward annular channel. A total of 638 experimental data points performed at KAERI and reported in literature was selected for training and testing ANN model. The seven basic parameters are chosen to be input variables and then the optimal structure of ANN which consist of two hidden layers with 131 neurons was determined based on traditional Trial-and-Error method after balancing the trade-off between the performance and training time. Results showed that the ANN model is capable to accurately predict the local void fraction with R2 value of 0.99931 for training data, R2 value of 0.99483 for testing data and R2 value of 0.99828 for all data. Also, it proved that the ANN training will be more effective with an extensive experimental database.

Using artificial neural network and non‐destructive test for crack detection in concrete surrounding the embedded steel reinforcement

AbstractBond between steel and concrete is one of the key aspects of structural design and its performance evaluation. In the past much research work has been focused on understanding bond deterioration owing to corrosion of reinforcement, however, there exists no nondestructive method to access the bond condition. In this regard, the presented experimental research work details the development of a nondestructive testing method to estimate the crack condition of concrete surrounding the steel reinforced by using ultrasonic pulse velocity test. In addition, a multilayer feedforward back propagation perceptron artificial neural network (ANN) is developed in order to avoid simplification assumptions for developing models to predict the cracking, owing to the nonlinear complex stress distribution at the steel‐concrete interface. The ANN is used to predict the crack width and to conduct sensitivity analysis of the various factors influencing the bond deterioration. A high accuracy level is achieved between the predicted and the experimental values with R2 of 0.97 and the most influential parameter is highlighted.

Ground plane design configuration estimation of 4.9 GHz reconfigurable monopole antenna for desired radiation features using artificial neural network

This paper presents a system based on artificial neural network (ANN) that predicts ground plane design for desired radiation properties of a monopole antenna with operation band of 4.8 to 5 GHz. The operating frequency can be adapted to any other frequency regimes. Initially, a 180 × 180 mm2 ground plane, which is composed of a copper layer, is designed and integrated to a radiative pole that creates monopole antenna configuration. The ground plane is divided into 18 rows and 18 columns as 18 × 18 matrix so that each unit cell has a square shape having 10 mm side length. Moreover, 152 different ground plane configurations are created by using logic 1 s and 0 s. Multi-layered feed forward ANN is used along with Scale Conjugate Gradient learning algorithm to design ground plane of the monopole antenna. Simulated 152 random ground plane arrays and obtained radiation patterns are used to train ANN for the ground plane design. If a user wants to manipulate radiation, artificial neural network gives the optimum ground plane design for the desired radiation direction and gain with 91.03% accuracy. Finally, one test antenna is fabricated and experimentally measured to support the results of the simulated one. The proposed ANN model approach can be easily used for antenna applications in the antenna industry.

Modeling of phenol removal from water by NiFe2O4 nanocomposite using response surface methodology and artificial neural network techniques

This study demonstrates the synthesis, characterization, and modeling of nickel ferrite nanocomposite, NiFe2O4 (NFC) as an adsorbent for the phenol contaminated aqueous environment. The characterization of the prepared NFC was performed with X-ray diffraction spectroscopy (XRD), scanning electron microscopy (SEM), transmission electron microscopy (TEM) and vibrating sample magnetometer (VSM) techniques. The optimization and modeling of phenol removal using NFC was done through central composite design (CCD) and effective parameters of CCD were measured as input variables including the amount of NFC, pH, contact time and initial phenol concentration. The predicted results showed that the adsorption process using NFC as adsorbent had the maximum phenol removal (~99%) under predicted optimal conditions (pH = 7.67, NFC dosage = 0.15 g at room temperature), which also corresponded to the experimental values. In addition, a multilayer feed-forward artificial neural network (ANN) model was used to obtain a speculative phenol removal model. The network was trained for six replications after selection of the best neuron number for hidden layer. The value of MSE trained network was found to be 6.01718e-3 along with regression coefficient (R2 = 0.9934) that indicated satisfactory relationship. Isothermal modeling of phenol adsorption onto NFC was performed using well-known Temkin, Freundlich and Langmuir models and it was clear from the higher R2 value of 0.961 that the Langmuir model was significantly followed by experimental data. The maximum Langmuir adsorption capacity was found to be 274.72 mg/g at the optimal conditions. The obtained results prove that NFC could be an effective adsorbent for elimination of phenol contaminant from aqueous environment.

Prediction of instantaneous real-world emissions from diesel light-duty vehicles based on an integrated artificial neural network and vehicle dynamics model

This paper presents a road vehicle emission model that integrates an artificial neural network (ANN) model with a vehicle dynamics model to predict the instantaneous carbon dioxide (CO2), nitrogen oxides (NOx) and total hydrocarbon (THC) emissions of diesel light-duty vehicles. Real-world measurement data were used to train a multi-layer feed-forward ANN model. The optimal combination of the various experimental variables was selected as the ANN input through a parametric study considering both practicality and accuracy. For CO2 prediction, two variables (engine speed and engine torque) are enough to develop an accurate ANN model. In order to achieve satisfactory accuracy for CO and NOx prediction, more variables were used for ANN training. The trained ANN model was used to predict road vehicle emissions by integrating the vehicle dynamics model, which was used as a supplementary tool to produce ANN input data. The integrated model is practical because it requires relatively simple data for input such as vehicle specifications, velocity, and road gradient. In the accuracy validation, the proposed model showed satisfactory prediction accuracy for road vehicle emissions.

Applying artificial neural networks to predict the enhanced thermal conductivity of a phase change material with dispersed oxide nanoparticles

The weak thermal conductance of a phase change material (PCM) can be intensified by dispersing nanostructured materials called nano-PCM. Accurate thermal conductivity (TC) prediction of nano-PCM is essential to evaluate heat transport during phase change processes, namely, melting and solidification. The present study develops an artificial neural network (ANN) to forecast the TC of n-octadecane as a PCM with dispersed oxide nanoparticles. A total of 122 experimental datasets from existing literature with a wide range of temperatures (5-60°C), nanoparticles (CuO, Al2O3, TiO2, and mesoporous SiO2), nanoparticle mass fractions (0.5-12 wt%) are compiled to train a multi-layered feed-forward ANN with Levenberg-Marquardt back-propagation algorithm. An optimal architecture of the neural network is acquired by varying the number of network hidden layers, the number of neurons in each layer, and the transfer function of layers. The minimum mean square error (MSE) of 1.3512 × 10−5 is obtained for the best developed ANN. Results show that average absolute deviation (AAD) of 0.002458, mean absolute percentage error (MAPE) of 0.8260%, and correlation coefficient (R) of 0.999964948 are achieved for training data. Moreover, MAPE, AAD, and R values are, respectively, 0.9478, 0.002167, and 0.9999715861 for testing data. The maximum percentage errors of ANN computed values are 2.31%, and 0.812% for liquid and solid phases, respectively. This indicates that the ANN model accurately predicts the enhanced TC of nano-PCM across various oxide nanoparticles, temperatures, and nanoparticle loadings.

Leveraging artificial intelligence for more accurate and reliable predictions of anchors shear breakout capacity in thin concrete members

Understanding the shear behavior of post-installed anchors embedded in thin concrete members is considered complicated due to the many parameters involved including embedment depth, edge distance, concrete strength, anchor diameter, and member thickness. This study utilizes the Artificial Neural Network (ANN) concept to predict the capacity and behavior of adhesive and screw anchors with full-thickness embedment in thin concrete members and compare it with the adopted design model in ACI 318 standard. First, A multilayered feed-forward ANN model trained with Bayesian Regularization training algorithm is developed and compared against the proposed model based on the concrete capacity design method (CCD). Then, a parametric study is presented to evaluate the contribution of each variable to the shear capacity and ensure generalization. The study shows that ANN model provides a higher level of accuracy in predicting the shear capacity than CCD model by reducing the coefficient of variation from 18% to 11%. In addition, the parametric study shows that the CCD model does not capture the nonlinear effect of the variables on the capacity accurately and does not include the interaction effect between variables. For instance, CCD shows that the member thickness has a nearly linear effect on the capacity while ANN shows higher nonlinearity. In addition, the study presents a reliability analysis to assess the shift in safety level by adopting the ANN model. The ANN provided an average increase in reliability index by 32%. This study is considered the first study to utilize ANN to anchors embedded in thin concrete members with full embedment depth.

Intelligent Electrospun Thermochromic Composite Nanofibers for Temperature Measurements

Thermochromic nanofibers (NFs) have emerged as an important class of nanostructured materials and have found a broad range of technologic applications. Herein, an optical fiber temperature sensor composed of coating electrospun thermochromic NFs on the end-face of fiber is reported. A composite coating consisting of an aqueous dispersion of a thermochromic material (cobalt-doped zinc phosphate or also called cobalt-doped hopeite -CoHo), in polyglycerol dendrimer (PGD), was directly electrospun onto the end-face surface of multimode optical fiber for the fabrication of temperature optical fiber sensor. Morphological features and optical properties of the resulting electrospun CoHo:PGD NFs were assessed. The diameter of the electrospun CoHo:PGDNFs deposited on the end-face of optical fiber was 500 ± 14 nm. The spectroscopic absorption of the electrospun CoHo:PGDNFs at 630 nm is measured to determine their relationship with temperature. The results under variable temperature from 25 to 250 °C indicate good linearity and show the potential of using CoHo:PGDNFs sensing applications for control in industrial processes that require a steady or changing temperature. To extract the chromaticity profile of the CoHo:PGDNFs and their temperature dependence, a multilayer feed-forward artificial neural network (ANN) was used. ANN has been successfully employed to extract temperature information from the chromatic changes of CoHo:PGDNFs.

A novel comparative analysis between the experimental and numeric methods on viscosity of zirconium oxide nanofluid: Developing optimal artificial neural network and new mathematical model

In this study, the viscosity of five different ZrO2/Water nanofluids of 0.0125%, 0.025%, 0.05%, 0.1% and 0.2% prepared by the two-step method were experimentally investigated. Using the experimental data obtained, a multi-layer perceptron feed-forward back-propagation artificial neural network and a new mathematical correlation have been developed in order to predict the viscosity of ZrO2/Water nanofluid. Experimental results revealed that viscosity decreases with increasing temperature and increases with increasing concentration. The outputs obtained from the developed artificial neural network and the new correlation were compared with the experimental results and analyzed. The results show that the developed artificial neural network can predict the viscosity of ZrO2/Water nanofluid with an average error rate of −0.11%. The new mathematical model developed has been able to calculate the viscosity of ZrO2/Water nanofluid with an error rate of −0.74%.

Intelligent Optical Temperature Sensor based on Polyglycerol Dendrimer Microspheres Encapsulating Hopeites

Optical thermometry is a growing technological field which exploits the ability of certain materials to change their optical properties with temperature. In this work, poly(glycerol dendrimer) microspheres (PGLyD) encapsulating cobalt-doped hopeite (CoHo) was synthesized for its potential use as optical thermometer. The structure of the CoHo:PGLyD was studied using scanning electron microscopy (SEM), infrared spectroscopy (ATR-FTIR), X-ray diffraction (XRD) and thermogravimetric analysis (TGA). On the basis of SEM images, the CoHo:PGLyD exhibited a spherical shape in which around 80% of the microspheres were within 0.82 µm. A multilayer feed-forward artificial neural network (MLP-ANN) was used to extract the chromaticity profile dependence of the CoHo:PGLyD with temperature. A color change was observed for the CoHo:PGLyD, going from dark blue (490-550 nm) to pale pink (650 nm) when the temperature changed from 20-200 °C, respectively. These results suggest that CoHo:PGLyD is a promising material for temperature sensing applications.