scholarly journals A Blanket Accommodative Sleep Posture Classification System Using an Infrared Depth Camera: A Deep Learning Approach with Synthetic Augmentation of Blanket Conditions

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5553
Author(s):  
Andy Yiu-Chau Tam ◽  
Bryan Pak-Hei So ◽  
Tim Tin-Chun Chan ◽  
Alyssa Ka-Yan Cheung ◽  
Duo Wai-Chi Wong ◽  
...  
Keyword(s):  
Data Augmentation ◽  
Classification Systems ◽  
Coarse Grained ◽  
Depth Camera ◽  
Fine Grained ◽  
Network Layers ◽  
Original Dataset ◽  
Left And Right ◽  
Sleep Posture ◽  
Posture Classification

Surveillance of sleeping posture is essential for bed-ridden patients or individuals at-risk of falling out of bed. Existing sleep posture monitoring and classification systems may not be able to accommodate the covering of a blanket, which represents a barrier to conducting pragmatic studies. The objective of this study was to develop an unobtrusive sleep posture classification that could accommodate the use of a blanket. The system uses an infrared depth camera for data acquisition and a convolutional neural network to classify sleeping postures. We recruited 66 participants (40 men and 26 women) to perform seven major sleeping postures (supine, prone (head left and right), log (left and right) and fetal (left and right)) under four blanket conditions (thick, medium, thin, and no blanket). Data augmentation was conducted by affine transformation and data fusion, generating additional blanket conditions with the original dataset. Coarse-grained (four-posture) and fine-grained (seven-posture) classifiers were trained using two fully connected network layers. For the coarse classification, the log and fetal postures were merged into a side-lying class and the prone class (head left and right) was pooled. The results show a drop of overall F1-score by 8.2% when switching to the fine-grained classifier. In addition, compared to no blanket, a thick blanket reduced the overall F1-scores by 3.5% and 8.9% for the coarse- and fine-grained classifiers, respectively; meanwhile, the lowest performance was seen in classifying the log (right) posture under a thick blanket, with an F1-score of 72.0%. In conclusion, we developed a system that can classify seven types of common sleeping postures under blankets and achieved an F1-score of 88.9%.

scholarly journals Large-Scale Coarse-to-Fine Object Retrieval Ontology and Deep Local Multitask Learning

2019 ◽  
Vol 2019 ◽  
pp. 1-40
Author(s):  
Ngoc Q. Ly ◽  
Tuong K. Do ◽  
Binh X. Nguyen
Keyword(s):  
Network Architecture ◽  
Large Scale ◽  
Data Augmentation ◽  
Imbalanced Data ◽  
Recall Rate ◽  
Coarse Grained ◽  
Object Retrieval ◽  
Fine Grained ◽  
Object Ontology ◽  
Coarse To Fine

Object retrieval plays an increasingly important role in video surveillance, digital marketing, e-commerce, etc. It is facing challenges such as large-scale datasets, imbalanced data, viewpoint, cluster background, and fine-grained details (attributes). This paper has proposed a model to integrate object ontology, a local multitask deep neural network (local MDNN), and an imbalanced data solver to take advantages and overcome the shortcomings of deep learning network models to improve the performance of the large-scale object retrieval system from the coarse-grained level (categories) to the fine-grained level (attributes). Our proposed coarse-to-fine object retrieval (CFOR) system can be robust and resistant to the challenges listed above. To the best of our knowledge, the new main point of our CFOR system is the power of mutual support of object ontology, a local MDNN, and an imbalanced data solver in a unified system. Object ontology supports the exploitation of the inner-group correlations to improve the system performance in category classification, attribute classification, and conducting training flow and retrieval flow to save computational costs in the training stage and retrieval stage on large-scale datasets, respectively. A local MDNN supports linking object ontology to the raw data, and an imbalanced data solver based on Matthews’ correlation coefficient (MCC) addresses that the imbalance of data has contributed effectively to increasing the quality of object ontology realization without adjusting network architecture and data augmentation. In order to evaluate the performance of the CFOR system, we experimented on the DeepFashion dataset. This paper has shown that our local MDNN framework based on the pretrained NASNet architecture has achieved better performance (14.2% higher in recall rate) compared to single-task learning (STL) in the attribute learning task; it has also shown that our model with an imbalanced data solver has achieved better performance (5.14% higher in recall rate for fewer data attributes) compared to models that do not take this into account. Moreover, MAP@30 hovers 0.815 in retrieval on an average of 35 imbalanced fashion attributes.

Classification and Identification Properties of Soils

2015 ◽  
pp. 116-179
Keyword(s):  
Classification System ◽  
Soil Classification ◽  
Classification Systems ◽  
Coarse Grained ◽  
Index Properties ◽  
Fine Grained ◽  
State Highway ◽  
Highway Transportation ◽  
Density Index

Soil texture and soil structure are both unique properties of soil that have profound effects on their behavior. The index properties commonly used for coarse-grained soils are grain size distribution and relative density. Index properties of fine-grained soils include consistency and sensitivity. These properties of a soil indicate the type and conditions of the soil and provide a relationship to its structural properties such as strength, compressibility, permeability, swelling potential, etc. Brief descriptions of some of these properties are given in this chapter. Towards the end, the chapter shows how these properties can be used for the classification of soils. The Soil Classification Systems considered include the following: Geological and Pedological Classification Systems (Classification by Origin and by Pedology), Morphological Classification Systems (Classification by Appearance and Textural Soil Classification System [USDA]), and Classification by Use (American Association of State Highway Transportation Officials System [AASHTO] and Unified Soil Classification System [USCS]).

Correlation between dislocation, grain boundary and interface of duplex SS in stress corrosion cracking(SCC)

1990 ◽  
Vol 48 (4) ◽  
pp. 928-929
Author(s):  
Wang Zheng-fang ◽  
Z.F. Wang
Keyword(s):  
Stainless Steel ◽  
Thermal Cycle ◽  
Secondary Phase ◽  
Corrosion Cracking ◽  
Coarse Grained ◽  
Test Environment ◽  
Fine Grained ◽  
Evaluation Test ◽  
Welding Thermal Cycle ◽  
Micro Analysis

The main purpose of this study highlights on the evaluation of chloride SCC resistance of the material,duplex stainless steel,OOCr18Ni5Mo3Si2 (18-5Mo) and its welded coarse grained zone(CGZ).18-5Mo is a dual phases (A+F) stainless steel with yield strength:512N/mm2 .The proportion of secondary Phase(A phase) accounts for 30-35% of the total with fine grained and homogeneously distributed A and F phases(Fig.1).After being welded by a specific welding thermal cycle to the material,i.e. Tmax=1350°C and t8/5=20s,microstructure may change from fine grained morphology to coarse grained morphology and from homogeneously distributed of A phase to a concentration of A phase(Fig.2).Meanwhile,the proportion of A phase reduced from 35% to 5-10°o.For this reason it is known as welded coarse grained zone(CGZ).In association with difference of microstructure between base metal and welded CGZ,so chloride SCC resistance also differ from each other.Test procedures:Constant load tensile test(CLTT) were performed for recording Esce-t curve by which corrosion cracking growth can be described, tf,fractured time,can also be recorded by the test which is taken as a electrochemical behavior and mechanical property for SCC resistance evaluation. Test environment:143°C boiling 42%MgCl2 solution is used.Besides, micro analysis were conducted with light microscopy(LM),SEM,TEM,and Auger energy spectrum(AES) so as to reveal the correlation between the data generated by the CLTT results and micro analysis.

scholarly journals Explaining Neural Networks Using Attentive Knowledge Distillation

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1280
Author(s):  
Hyeonseok Lee ◽  
Sungchan Kim
Keyword(s):  
Neural Networks ◽  
Model Prediction ◽  
Saliency Map ◽  
Model Parameters ◽  
Learning Capability ◽  
Fine Grained ◽  
Network Layers ◽  
Saliency Maps ◽  
Novel Approach ◽  
Knowledge Distillation

Explaining the prediction of deep neural networks makes the networks more understandable and trusted, leading to their use in various mission critical tasks. Recent progress in the learning capability of networks has primarily been due to the enormous number of model parameters, so that it is usually hard to interpret their operations, as opposed to classical white-box models. For this purpose, generating saliency maps is a popular approach to identify the important input features used for the model prediction. Existing explanation methods typically only use the output of the last convolution layer of the model to generate a saliency map, lacking the information included in intermediate layers. Thus, the corresponding explanations are coarse and result in limited accuracy. Although the accuracy can be improved by iteratively developing a saliency map, this is too time-consuming and is thus impractical. To address these problems, we proposed a novel approach to explain the model prediction by developing an attentive surrogate network using the knowledge distillation. The surrogate network aims to generate a fine-grained saliency map corresponding to the model prediction using meaningful regional information presented over all network layers. Experiments demonstrated that the saliency maps are the result of spatially attentive features learned from the distillation. Thus, they are useful for fine-grained classification tasks. Moreover, the proposed method runs at the rate of 24.3 frames per second, which is much faster than the existing methods by orders of magnitude.

scholarly journals Balanced Sparsity for Efficient DNN Inference on GPU

2019 ◽  
Vol 33 ◽  
pp. 5676-5683 ◽  
Author(s):  
Zhuliang Yao ◽  
Shijie Cao ◽  
Wencong Xiao ◽  
Chen Zhang ◽  
Lanshun Nie
Keyword(s):  
Deep Neural Networks ◽  
General Purpose ◽  
Coarse Grained ◽  
Efficient Computation ◽  
Model Accuracy ◽  
Sparse Model ◽  
Model Inference ◽  
Fine Grained ◽  
Practical Inference ◽  
Speed Up

In trained deep neural networks, unstructured pruning can reduce redundant weights to lower storage cost. However, it requires the customization of hardwares to speed up practical inference. Another trend accelerates sparse model inference on general-purpose hardwares by adopting coarse-grained sparsity to prune or regularize consecutive weights for efficient computation. But this method often sacrifices model accuracy. In this paper, we propose a novel fine-grained sparsity approach, Balanced Sparsity, to achieve high model accuracy with commercial hardwares efficiently. Our approach adapts to high parallelism property of GPU, showing incredible potential for sparsity in the widely deployment of deep learning services. Experiment results show that Balanced Sparsity achieves up to 3.1x practical speedup for model inference on GPU, while retains the same high model accuracy as finegrained sparsity.

scholarly journals Submarine lava deltas of the 2018 eruption of Kīlauea volcano

2021 ◽  
Vol 83 (4) ◽  
Author(s):  
S. Adam Soule ◽  
Michael Zoeller ◽  
Carolyn Parcheta
Keyword(s):  
Grain Size ◽  
Pore Pressure ◽  
Mechanistic Model ◽  
Large Fraction ◽  
Volcanic Hazards ◽  
Lava Flows ◽  
Coarse Grained ◽  
Fine Grained ◽  
Fine Grain ◽  
Delta Formation

AbstractHawaiian and other ocean island lava flows that reach the coastline can deposit significant volumes of lava in submarine deltas. The catastrophic collapse of these deltas represents one of the most significant, but least predictable, volcanic hazards at ocean islands. The volume of lava deposited below sea level in delta-forming eruptions and the mechanisms of delta construction and destruction are rarely documented. Here, we report on bathymetric surveys and ROV observations following the Kīlauea 2018 eruption that, along with a comparison to the deltas formed at Pu‘u ‘Ō‘ō over the past decade, provide new insight into delta formation. Bathymetric differencing reveals that the 2018 deltas contain more than half of the total volume of lava erupted. In addition, we find that the 2018 deltas are comprised largely of coarse-grained volcanic breccias and intact lava flows, which contrast with those at Pu‘u ‘Ō‘ō that contain a large fraction of fine-grained hyaloclastite. We attribute this difference to less efficient fragmentation of the 2018 ‘a‘ā flows leading to fragmentation by collapse rather than hydrovolcanic explosion. We suggest a mechanistic model where the characteristic grain size influences the form and stability of the delta with fine grain size deltas (Pu‘u ‘Ō‘ō) experiencing larger landslides with greater run-out supported by increased pore pressure and with coarse grain size deltas (Kīlauea 2018) experiencing smaller landslides that quickly stop as the pore pressure rapidly dissipates. This difference, if validated for other lava deltas, would provide a means to assess potential delta stability in future eruptions.

scholarly journals A cooperative DDoS attack detection scheme based on entropy and ensemble learning in SDN

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Shanshan Yu ◽  
Jicheng Zhang ◽  
Ju Liu ◽  
Xiaoqing Zhang ◽  
Yafeng Li ◽  
...  
Keyword(s):  
Ensemble Learning ◽  
Denial Of Service ◽  
Attack Detection ◽  
Coarse Grained ◽  
Communication Overhead ◽  
Detection Scheme ◽  
Fine Grained ◽  
Ddos Attack ◽  
Network Status ◽  
Ddos Attack Detection

AbstractIn order to solve the problem of distributed denial of service (DDoS) attack detection in software-defined network, we proposed a cooperative DDoS attack detection scheme based on entropy and ensemble learning. This method sets up a coarse-grained preliminary detection module based on entropy in the edge switch to monitor the network status in real time and report to the controller if any abnormality is found. Simultaneously, a fine-grained precise attack detection module is designed in the controller, and a ensemble learning-based algorithm is utilized to further identify abnormal traffic accurately. In this framework, the idle computing capability of edge switches is fully utilized with the design idea of edge computing to offload part of the detection task from the control plane to the data plane innovatively. Simulation results of two common DDoS attack methods, ICMP and SYN, show that the system can effectively detect DDoS attacks and greatly reduce the southbound communication overhead and the burden of the controller as well as the detection delay of the attacks.

Rheology of hydrocarbon gels

1950 ◽  
Vol 200 (1061) ◽  
pp. 183-188 ◽  
Keyword(s):  
Mechanical Properties ◽  
Experimental Measurement ◽  
Theoretical Basis ◽  
Continuous Media ◽  
Point Of View ◽  
Coarse Grained ◽  
Testing Instrument ◽  
Fine Grained ◽  
Mineral Oils ◽  
Space And Time

Hydrocarbon gels contain a number of materials, such as rubber, greases, saponified mineral oils, etc., of great interest for various engineering purposes. Specific requirements in mechanical properties have been met by producing gels in appropriately chosen patterns of constituent components of visible, colloidal, molecular and atomic sizes, ranging from coarse-grained aggregates, represented by sponges, foams, emulsions, etc.; to fine-grained and apparently homogeneous ones, represented by optically clear compounds. The engineer who has to deal with the whole range of such materials will adopt a macroscopic point of view, based on an apparent continuity of all the material structures and of the distributions in space and time of the displacements and forces occurring under mechanical actions. It has been possible to determine these distributions in the framework of a comprehensive scheme in which the fundamental principles of the mechanics of continuous media provide the theoretical basis, and a testing instrument of new design, termed Rheogoniometer, the means of experimental measurement (Weissenberg 1931, 1934, 1946, 1947, 1948).

Microstructural Refinement in a Commercial Aluminum Alloy Processed by ECAP Using a Die Channel Angle of 110 Degrees

2015 ◽  
Vol 1114 ◽  
pp. 3-8
Author(s):  
Nicolae Şerban ◽  
Doina Răducanu ◽  
Nicolae Ghiban ◽  
Vasile Dănuţ Cojocaru
Keyword(s):  
Grain Refinement ◽  
Cross Section ◽  
High Strength ◽  
Secondary Phase ◽  
Coarse Grained ◽  
Metallographic Analysis ◽  
Second Phase ◽  
Channel Angle ◽  
Ambient Temperatures ◽  
Fine Grained

The properties of ultra-fine grained materials are superior to those of corresponding conventional coarse grained materials, being significantly improved as a result of grain refinement. Equal channel angular pressing (ECAP) is an efficient method for modifying the microstructure by refining grain size via severe plastic deformation (SPD) in producing ultra-fine grained materials (UFG) and nanomaterials (NM). The grain sizes produced by ECAP processing are typically in the submicrometer range and this leads to high strength at ambient temperatures. ECAP is performed by pressing test samples through a die containing two channels, equal in cross-section and intersecting at a certain angle. The billet experiences simple shear deformation at the intersection, without any precipitous change in the cross-section area because the die prevents lateral expansion and therefore the billet can be pressed more than once and it can be rotated around its pressing axis during subsequent passes. After ECAP significant grain refinement occurs together with dislocation strengthening, resulting in a considerable enhancement in the strength of the alloys. A commercial AlMgSi alloy (AA6063) was investigated in this study. The specimens were processed for a number of passes up to nine, using a die channel angle of 110°, applying the ECAP route BC. After ECAP, samples were cut from each specimen and prepared for metallographic analysis. The microstructure of the ECAP-ed and as-received material was investigated using optical (OLYMPUS – BX60M) and SEM microscopy (TESCAN VEGA II – XMU). It was determined that for the as-received material the microstructure shows a rough appearance, with large grains of dendritic or seaweed aspect and with a secondary phase at grain boundaries (continuous casting structure). For the ECAP processed samples, the microstructure shows a finished aspect, with refined, elongated grains, also with crumbled and uniformly distributed second phase particles after a typical ECAP texture.

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