| 1. |
- Abdallah, Mohammed, et al.
(author)
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Daily global solar radiation time series prediction using variational mode decomposition combined with multi-functional recurrent fuzzy neural network and quantile regression forests algorithm
- 2023
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In: Energy Reports. - 2352-4847. ; 10, s. 4198-4217
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Journal article (peer-reviewed)abstract
- Global solar radiation (GSR) prediction capability with a reliable model and high accuracy is crucial for comprehending hydrological and meteorological systems. It is vital for the production of renewable and clean energy. This research aims to evaluate the performance of combined variational mode decomposition (VMD) with a multi-functional recurrent fuzzy neural network (MFRFNN) and quantile regression forests (QRF) models for GSR prediction in daily scales. The hybrid VMD-MFRFNN and QRF models were compared with standalone MFRFNN, random forest (RF), extreme gradient boosting (XGB), and M5 tree (M5T) models across the Lund and Växjö meteorological stations in Sweden. The meteorological data from 2008 to 2017 were used to train the models, while the prediction accuracy was verified by using the data from 2018 to 2021 under five different input combinations. The various meteorological-based scenarios (including the input are air temperatures (Tmin, Tmax, T), wind speed (WS), relative humidity (RH), sunshine duration (SSH), and maximum possible sunshine duration (N)) were considered as input of predictor models. The current study resulted that the M5T model exhibited higher accuracy than RF and XGB models, while the QRF model showed equivalent performance with the M5T model at both study sites. The MFRFNN model outperformed QRF and M5T models across all input combinations at both study sites. The hybrid VMD-MFRFNN model showed the best performance when fewer input variables (Tmin, Tmax, T, WS at Lund station and Tmin, Tmax, T, WS, SSH, RH at Växjö station) were used for GSR prediction. We conclude that the MFRFNN model best predicts average daily GSR when combining all meteorological variables (Tmin, Tmax, T, WS, SSH, RH, N).
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| 2. |
- Chehreh Chelgani, Saeed, et al.
(author)
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CatBoost-SHAP for modeling industrial operational flotation variables – A “conscious lab” approach
- 2024
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In: Minerals Engineering. - : Elsevier. - 0892-6875 .- 1872-9444. ; 213
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Journal article (peer-reviewed)abstract
- Flotation separation is the most important upgrading critical raw material technique. Measuring interactions within flotation variables and modeling their metallurgical responses (grade and recovery) is quite challenging on the industrial scale. These challenges are because flotation separation includes several sub-micron processes, and their monitoring won't be possible for the processing plants. Since many flotation plants are still manually operating and maintaining, understanding interactions within operational variables and their effect on the metallurgical responses would be crucial. As a unique approach, this study used the “Conscious Lab” concept for modeling flotation responses of an industrial copper upgrading plant when Potassium Amyl Xanthate substituted the secondary collector (Sodium Ethyl Xanthate) in the process. The main aim is to understand and compare interactions before and after the collector substitution. For the first time, the conscious lab was constructed based on the most advanced explainable artificial intelligence model, Shapley Additive Explanations, and Catboost. Catboost- Shapley Additive Explanations could accurately model flotation responses (less than 2% error between actual and predicted values) and illustrate variations of complex interactions through the substitution. Through a comparative study, Catboost could generate more precise outcomes than other known artificial intelligence models (Random Forest, Support Vector Regression, Extreme Gradient Boosting, and Convolutional Neural Network). In general, substituting Sodium Ethyl Xanthate by Potassium Amyl Xanthate reduced process predictability, although Potassium Amyl Xanthate could slightly increase the copper recovery.
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| 3. |
- Fatahi, Rasoul, et al.
(author)
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Modeling of energy consumption factors for an industrial cement vertical roller mill by SHAP-XGBoost: a "conscious lab" approach
- 2022
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In: Scientific Reports. - : Springer Nature. - 2045-2322. ; 12:1
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Journal article (peer-reviewed)abstract
- Cement production is one of the most energy-intensive manufacturing industries, and the milling circuit of cement plants consumes around 4% of a year's global electrical energy production. It is well understood that modeling and digitalizing industrial-scale processes would help control production circuits better, improve efficiency, enhance personal training systems, and decrease plants' energy consumption. This tactical approach could be integrated using conscious lab (CL) as an innovative concept in the internet age. Surprisingly, no CL has been reported for the milling circuit of a cement plant. A robust CL interconnect datasets originated from monitoring operational variables in the plants and translating them to human basis information using explainable artificial intelligence (EAI) models. By initiating a CL for an industrial cement vertical roller mill (VRM), this study conducted a novel strategy to explore relationships between VRM monitored operational variables and their representative energy consumption factors (output temperature and motor power). Using SHapley Additive exPlanations (SHAP) as one of the most recent EAI models accurately helped fill the lack of information about correlations within VRM variables. SHAP analyses highlighted that working pressure and input gas rate with positive relationships are the key factors influencing energy consumption. eXtreme Gradient Boosting (XGBoost) as a powerful predictive tool could accurately model energy representative factors by R-square ever 0.80 in the testing phase. Comparison assessments indicated that SHAP-XGBoost could provide higher accuracy for VRM-CL structure than conventional modeling tools (Pearson correlation, Random Forest, and Support vector regression.
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| 4. |
- Fatahi, Rasoul, et al.
(author)
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Modeling operational cement rotary kiln variables with explainable artificial intelligence methods–a “conscious lab” development
- 2023
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In: Particulate Science and Technology. - : Taylor & Francis. - 0272-6351 .- 1548-0046. ; 41:5, s. 715-724
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Journal article (peer-reviewed)abstract
- Digitalizing cement production plants to improve operation parameters’ control might reduce energy consumption and increase process sustainabilities. Cement production plants are one of the extremest CO2 emissions, and the rotary kiln is a cement plant’s most energy-consuming and energy-wasting unit. Thus, enhancing its operation assessments adsorb attention. Since many factors would affect the clinker production quality and rotary kiln efficiency, controlling those variables is beyond operator capabilities. Constructing a conscious-lab “CL” (developing an explainable artificial intelligence “EAI” model based on the industrial operating dataset) can potentially tackle those critical issues, reduce laboratory costs, save time, improve process maintenance and help for better training operators. As a novel approach, this investigation examined extreme gradient boosting (XGBoost) coupled with SHAP (SHapley Additive exPlanations) “SHAP-XGBoost” for the modeling and prediction of the rotary kiln factors (feed rate and induced draft fan current) based on over 3,000 records collected from the Ilam cement plant. SHAP illustrated the relationships between each record and variables with the rotary kiln factors, demonstrated their correlation magnitude, and ranked them based on their importance. XGBoost accurately (R-square 0.96) could predict the rotary kiln factors where results showed higher exactness than typical EAI models.
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| 5. |
- Ghourchian, Hamid, et al.
(author)
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On the Capacity of a Class of Signal-Dependent Noise Channels
- 2018
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In: IEEE Transactions on Information Theory. - : IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. - 0018-9448 .- 1557-9654. ; 64:12, s. 7828-7846
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Journal article (peer-reviewed)abstract
- In some applications, the variance of additive measurement noise depends on the signal that we aim to measure. For instance, additive signal-dependent Gaussian noise (ASDGN) channel models are used in molecular and optical communication. Herein, we provide lower and upper bounds on the capacity of additive signal-dependent noise (ASDN) channels. The first lower bound is based on an extension of majorization inequalities, and the second lower bound utilizes the properties of the differential entropy. The lower bounds are valid for arbitrary ASDN channels. The upper bound is based on a previous idea of the authors ("symmetric relative entropy") and is applied to the ASDGN channels. These bounds indicate that in the ASDN channels (unlike the classical additive white Gaussian noise channels), the capacity does not necessarily become larger by reducing the noise variance function. We also provide sufficient conditions under which the capacity becomes infinite. This is complemented by some conditions implying that the capacity is finite, and a unique capacity achieving measure exists (in the sense of the output measure).
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| 6. |
- Sedghi, Maryam, et al.
(author)
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Motor neuron diseases caused by a novel VRK1 variant - A genotype/phenotype study
- 2019
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In: Annals of Clinical and Translational Neurology. - : John Wiley & Sons. - 2328-9503. ; 6:11, s. 2197-2204
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Journal article (peer-reviewed)abstract
- Background Motor neuron disorders involving upper and lower neurons are a genetically and clinically heterogenous group of rare neuromuscular disorders with overlap among spinal muscular atrophies (SMAs) and amyotrophic lateral sclerosis (ALS). Classical SMA caused by recessive mutations in SMN1 is one of the most common genetic causes of mortality in infants. It is characterized by degeneration of anterior horn cells in the spinal cord, leading to progressive muscle weakness and atrophy. Non-SMN1-related spinal muscular atrophies are caused by variants in a number of genes, including VRK1, encoding the vaccinia-related kinase 1 (VRK1). VRK1 variants have been segregated with motor neuron diseases including SMA phenotypes or hereditary complex motor and sensory axonal neuropathy (HMSN), with or without pontocerebellar hypoplasia or microcephaly. Results Here, we report an association of a novel homozygous splice variant in VRK1 (c.1159 + 1G>A) with childhood-onset SMA or juvenile lower motor disease with brisk tendon reflexes without pontocerebellar hypoplasia and normal intellectual ability in a family with five affected individuals. We show that the VRK1 splice variant in patients causes decreased splicing efficiency and a mRNA frameshift that escapes the nonsense-mediated decay machinery and results in a premature termination codon. Conclusions Our findings unveil the impact of the variant on the VRK1 transcript and further support the implication of VRK1 in the pathogenesis of lower motor neuron diseases.
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