Moisture evolution, thermal properties and energy consumption of drying spent grain pellets from a blend of some cereals for small scale bio-energy utilization: Modelling and Experimental study

© 2022 Springer Nature Switzerland AG. This is the accepted manuscript version of an article which has been published in final form at https://doi.org/10.1007/s13399-022-02846-xA fixed bed convective dryer was used to assess the influence of drying temperature and geometry deformation on moisture and thermo-physical property evolution of solid fraction pellets (spent grain) from wet milling of cereal blends for bio-energy generation for small homes. The aim is to study the physical mechanism of drying the pellets that includes temperature and moisture behaviour, transport phenomena, the response rate to varying process conditions, drying time, and energy utilization which can be applied in the development of a fixed bed dryer for drying the pellets at a lower scale. The modified Cranck's diffusion model was used to study moisture loss by introducing shrinkage. The verification of the model gave the mean absolute error (MAE) for moisture content with shrinkage as 0.0366 - 0.1500 while for without shrinkage was 0.0729 - 0.1500 for 60- 80 oC. The effective moisture diffusivity with integrating shrinkage is lower than non- shrinkage though these values varied with drying time. Fitting the moisture ratio with the exponential drying curve equations shows that logarithmic equations were the best model for drying at 60 and 70 oC while Henderson and Pabis's model was better at 80 oC isothermal drying. Thermophysical analysis showed that the average specific heat capacity ranges from 5423.387 to 5198.197J/kgK while the thermal conductivity ranged from 0.115281to 0.136882W/mK at 60-80 oC. The energy and specific energy consumption ranged from 0.41 to 0.494 kWh and 108.39 to 119.29MJ/kg. The shrinkage ratios, effective diffusivity and energy and specific energy consumption were empirically presented as a function of moisture, temperature and or air velocity variations with a high degree of association.Peer reviewe

Relationship detection based on object semantic inference and attention mechanisms

Detecting relations among objects is a crucial task for image understanding. However, each relationship involves different objects pair combinations, and different objects pair combinations express diverse interactions. This makes the relationships, based just on visual features, a challenging task. In this paper, we propose a simple yet effective relationship detection model, which is based on object semantic inference and attention mechanisms. Our model is trained to detect relation triples, such as , . To overcome the high diversity of visual appearances, the semantic inference module and the visual features are combined to complement each others. We also introduce two different attention mechanisms for object feature refinement and phrase feature refinement. In order to derive a more detailed and comprehensive representation for each object, the object feature refinement module refines the representation of each object by querying over all the other objects in the image. The phrase feature refinement module is proposed in order to make the phrase feature more effective, and to automatically focus on relative parts, to improve the visual relationship detection task. We validate our model on Visual Genome Relationship dataset. Our proposed model achieves competitive results compared to the state-of-the-art method MOTIFNET

SC-CAN: Spectral Convolution and Channel Attention Network for wheat stress classification

Biotic and abiotic plant stress (e.g., frost, fungi, diseases) can significantly impact crop production. It is thus essential to detect such stress at an early stage before visual symptoms and damage become apparent. To this end, this paper proposes a novel deep learning method, called Spectral Convolution and Channel Attention Network (SC-CAN), which exploits the difference in spectral responses of healthy and stressed crops. The proposed SC-CAN method comprises two main modules: (i) a spectral convolution module, which consists of dilated causal convolutional layers stacked in a residual manner to capture the spectral features; (ii) a channel attention module, which consists of a global pooling layer and fully connected layers that compute inter-relationship between feature map channels before scaling them based on their importance level (attention score). Unlike standard convolution, which focuses on learning local features, the dilated convolution layers can learn both local and global features. These layers also have long receptive fields, making them suitable for capturing long dependency patterns in hyperspectral data. However, because not all feature maps produced by the dilated convolutional layers are important, we propose a channel attention module that weights the feature maps according to their importance level. We used SC-CAN to classify salt stress (i.e., abiotic stress) on four datasets (Chinese Spring (CS), Aegilops columnaris (co(CS)), Ae. speltoides auchery (sp(CS)), and Kharchia datasets) and Fusarium head blight disease (i.e., biotic stress) on Fusarium dataset. Reported experimental results show that the proposed method outperforms existing state-of-the-art techniques with an overall accuracy of 83.08%, 88.90%, 82.44%, 82.10%, and 82.78% on CS, co(CS), sp(CS), Kharchia, and Fusarium datasets, respectively

Machine learning risk prediction model for acute coronary syndrome and death from use of non-steroidal anti-inflammatory drugs in administrative data

Our aim was to investigate the usefulness of machine learning approaches on linked administrative health data at the population level in predicting older patients’ one-year risk of acute coronary syndrome and death following the use of non-steroidal anti-inflammatory drugs (NSAIDs). Patients from a Western Australian cardiovascular population who were supplied with NSAIDs between 1 Jan 2003 and 31 Dec 2004 were identified from Pharmaceutical Benefits Scheme data. Comorbidities from linked hospital admissions data and medication history were inputs. Admissions for acute coronary syndrome or death within one year from the first supply date were outputs. Machine learning classification methods were used to build models to predict ACS and death. Model performance was measured by the area under the receiver operating characteristic curve (AUC-ROC), sensitivity and specificity. There were 68,889 patients in the NSAIDs cohort with mean age 76 years and 54% were female. 1882 patients were admitted for acute coronary syndrome and 5405 patients died within one year after their first supply of NSAIDs. The multi-layer neural network, gradient boosting machine and support vector machine were applied to build various classification models. The gradient boosting machine achieved the best performance with an average AUC-ROC of 0.72 predicting ACS and 0.84 predicting death. Machine learning models applied to linked administrative data can potentially improve adverse outcome risk prediction. Further investigation of additional data and approaches are required to improve the performance for adverse outcome risk prediction

Attention in Convolutional LSTM for Gesture Recognition

Convolutional long short-term memory (LSTM) networks have been widely used for action/gesture recognition, and different attention mechanisms have also been embedded into the LSTM or the convolutional LSTM (ConvLSTM) networks. Based on the previous gesture recognition architectures which combine the threedimensional convolution neural network (3DCNN) and ConvLSTM, this paper explores the effects of attention mechanism in ConvLSTM. Several variants of ConvLSTM are evaluated: (a) Removing the convolutional structures of the three gates in ConvLSTM, (b) Applying the attention mechanism on the input of ConvLSTM, (c) Reconstructing the input and (d) output gates respectively with the modified channel-wise attention mechanism. The evaluation results demonstrate that the spatial convolutions in the three gates scarcely contribute to the spatiotemporal feature fusion, and the attention mechanisms embedded into the input and output gates cannot improve the feature fusion. In other words, ConvLSTM mainly contributes to the temporal fusion along with the recurrent steps to learn the long-term spatiotemporal features, when taking as input the spatial or spatiotemporal features. On this basis, a new variant of LSTM is derived, in which the convolutional structures are only embedded into the input-to-state transition of LSTM. The code of the LSTM variants is publicly available2

Geometric distortion measurement for shape coding: a contemporary review

Geometric distortion measurement and the associated metrics involved are integral to the rate-distortion (RD) shape coding framework, with importantly the efficacy of the metrics being strongly influenced by the underlying measurement strategy. This has been the catalyst for many different techniques with this paper presenting a comprehensive review of geometric distortion measurement, the diverse metrics applied and their impact on shape coding. The respective performance of these measuring strategies is analysed from both a RD and complexity perspective, with a recent distortion measurement technique based on arc-length-parameterisation being comparatively evaluated. Some contemporary research challenges are also investigated, including schemes to effectively quantify shape deformation

Pseudo-Pair Based Self-Similarity Learning for Unsupervised Person Re-Identification

Person re-identification (re-ID) is of great importance to video surveillance systems by estimating the similarity between a pair of cross-camera person shorts. Current methods for estimating such similarity require a large number of labeled samples for supervised training. In this paper, we present a pseudo-pair based self-similarity learning approach for unsupervised person re-ID without human annotations. Unlike conventional unsupervised re-ID methods that use pseudo labels based on global clustering, we construct patch surrogate classes as initial supervision, and propose to assign pseudo labels to images through the pairwise gradient-guided similarity separation. This can cluster images in pseudo pairs, and the pseudos can be updated during training. Based on pseudo pairs, we propose to improve the generalization of similarity function via a novel self-similarity learning:it learns local discriminative features from individual images via intra-similarity, and discovers the patch correspondence across images via inter-similarity. The intra-similarity learning is based on channel attention to detect diverse local features from an image. The inter-similarity learning employs a deformable convolution with a non-local block to align patches for cross-image similarity. Experimental results on several re-ID benchmark datasets demonstrate the superiority of the proposed method over the state-of-the-arts

Evolutionary feature learning for 3-D object recognition

3-D object recognition is a challenging task for many applications including autonomous robot navigation and scene understanding. Accurate recognition relies on the selection/learning of discriminative features that are in turn used to uniquely characterize the objects. This paper proposes a novel evolutionary feature learning (EFL) technique for 3-D object recognition. The proposed novel automatic feature learning approach can operate directly on 3-D raw data, alleviating the need for data pre-processing, human expertise and/or defining a large set of parameters. EFL offers smart search strategy to learn the best features in a huge feature space to achieve superior recognition performance. The proposed technique has been extensively evaluated for the task of 3-D object recognition on four popular data sets including Washington RGB-D (low resolution 3-D Video), CIN 2D3D, Willow 2D3D and ETH-80 object data set. Reported experimental results and evaluation against existing state-of-the-art methods (e.g., unsupervised dictionary learning and deep networks) show that the proposed EFL consistently achieves superior performance on all these data sets