scholarly journals Multi-Time-Scale Features for Accurate Respiratory Sound Classification

2020 ◽  
Vol 10 (23) ◽  
pp. 8606
Author(s):  
Alfonso Monaco ◽  
Nicola Amoroso ◽  
Loredana Bellantuono ◽  
Ester Pantaleo ◽  
Sabina Tangaro ◽  
...  
Keyword(s):  
Time Scale ◽  
Health Informatics ◽  
Large Scale ◽  
The Body ◽  
Support Vector ◽  
Computer Assisted ◽  
Respiratory Sound ◽  
Short Term ◽  
Sound Classification

The COVID-19 pandemic has amplified the urgency of the developments in computer-assisted medicine and, in particular, the need for automated tools supporting the clinical diagnosis and assessment of respiratory symptoms. This need was already clear to the scientific community, which launched an international challenge in 2017 at the International Conference on Biomedical Health Informatics (ICBHI) for the implementation of accurate algorithms for the classification of respiratory sound. In this work, we present a framework for respiratory sound classification based on two different kinds of features: (i) short-term features which summarize sound properties on a time scale of tenths of a second and (ii) long-term features which assess sounds properties on a time scale of seconds. Using the publicly available dataset provided by ICBHI, we cross-validated the classification performance of a neural network model over 6895 respiratory cycles and 126 subjects. The proposed model reached an accuracy of 85%±3% and an precision of 80%±8%, which compare well with the body of literature. The robustness of the predictions was assessed by comparison with state-of-the-art machine learning tools, such as the support vector machine, Random Forest and deep neural networks. The model presented here is therefore suitable for large-scale applications and for adoption in clinical practice. Finally, an interesting observation is that both short-term and long-term features are necessary for accurate classification, which could be the subject of future studies related to its clinical interpretation.

scholarly journals Two Stage Continuous Gesture Recognition Based on Deep Learning

Electronics ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 534
Author(s):  
Huogen Wang
Keyword(s):  
Gesture Recognition ◽  
Large Scale ◽  
Short Term Memory ◽  
Short Term ◽  
Hand Motion ◽  
Spatiotemporal Features ◽  
Spatiotemporal Information ◽  
Video Frames ◽  
Depth Sequences

The paper proposes an effective continuous gesture recognition method, which includes two modules: segmentation and recognition. In the segmentation module, the video frames are divided into gesture frames and transitional frames by using the information of hand motion and appearance, and continuous gesture sequences are segmented into isolated sequences. In the recognition module, our method exploits the spatiotemporal information embedded in RGB and depth sequences. For the RGB modality, our method adopts Convolutional Long Short-Term Memory Networks to learn long-term spatiotemporal features from short-term spatiotemporal features obtained from a 3D convolutional neural network. For the depth modality, our method converts a sequence into Dynamic Images and Motion Dynamic Images through weighted rank pooling and feed them into Convolutional Neural Networks, respectively. Our method has been evaluated on both ChaLearn LAP Large-scale Continuous Gesture Dataset and Montalbano Gesture Dataset and achieved state-of-the-art performance.

scholarly journals Short-Term Master-Slave Forecast Method for Distributed Photovoltaic Plants Based on the Spatial Correlation

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Jia Ning ◽  
Guanghao Lu ◽  
Sipeng Hao ◽  
Aidong Zeng ◽  
Hualei Wang
Keyword(s):  
Power Systems ◽  
Spatial Correlation ◽  
Data Storage ◽  
Power Plants ◽  
Large Scale ◽  
Clustering Algorithm ◽  
Storage Condition ◽  
Support Vector ◽  
Forecast Method ◽  
Short Term

With the large-scale integration of distributed photovoltaic (DPV) power plants, the uncertainty of photovoltaic generation is intensively influencing the secure operation of power systems. Improving the forecast capability of DPV plants has become an urgent problem to solve. However, most of the DPV plants are not able to make generation forecast on their own due to the constraints of the investment cost, data storage condition, and the influence of microscope environment. Therefore, this paper proposes a master-slave forecast method to predict the power of target plants without forecast ability based on the power of DPV plants with comprehensive forecast system and the spatial correlation between these two kinds of plants. First, a characteristics pattern library of DPV plants is established with K-means clustering algorithm considering the time difference. Next, the pattern most spatially correlated to the target plant is determined through online matching. The corresponding spatial correlation mapping relationship is obtained by numerical fitting using least squares support vector machine (LS-SVM), and the short-term generation forecast for target plants is achieved with the forecast of reference plants and mapping relationship. Simulation results demonstrate that the proposed method could improve the overall forecast accuracy by more than 52% for univariate prediction and by more than 22% for multivariate prediction and obtain short-term generation forecast for DPV or newly built DPV plants with low investment.

scholarly journals Lalabet Financial Analysis: Study of Islamic Economics at Micro Level

2021 ◽  
Vol 4 (1) ◽  
pp. 233-244
Author(s):  
Ainorrofiqie Ainorrofiqie ◽  
Umrotul Khasanah ◽  
Akhmad Djalaluddin
Keyword(s):  
Financial Management ◽  
Financial Analysis ◽  
Financial Statements ◽  
The Body ◽  
Short Term ◽  
The Family ◽  
Micro Level ◽  
The Impact ◽  
The Village

This research aims to explore the model of financial management tradition Lalabet in the village of Babbalan District Batuan Sumenep. This study is based on the fact that occurred in the community about the implementation of traditions carried out by the heirs to family members who died. Interpretative qualitative research is used and an in-depth understanding of a problem that occurs is emphasized more. Based on the results of this study, the financial management tradition Lalabet can be done based on accounting equations. The accounts contained in the accounting equation is not used in its entirety and are reported as are generally financial statements. In this case, the source of funds in carrying out Lalabet tradition is sourced from personal money, money and donations from the family, money from Muslimat, debt, and money or goods from Lalabet's proceeds. The impact is the onset of debt both short-term and long-term. While the expenditure is in the form of costs in taking care of the body, costs for tahlilan (petto'arean), pa'polo, nyatos, nyataon, nyaebu, mangaji, ngin-tangin, nyalenin mayyid, and ajege makam (kep-sekep).

scholarly journals The Immune System Can Hear Noise

2021 ◽  
Vol 11 ◽  
Author(s):  
Andi Zhang ◽  
Tianyuan Zou ◽  
Dongye Guo ◽  
Quan Wang ◽  
Yilin Shen ◽  
...  
Keyword(s):  
Immune System ◽  
Adverse Effects ◽  
Immune Function ◽  
Noise Exposure ◽  
The Body ◽  
Intensity Noise ◽  
Short Term ◽  
Non Hodgkin Lymphoma ◽  
Exposure During Pregnancy

As a stressor widely existing in daily life, noise can cause great alterations to the immune system and result in many physical and mental disorders, including noise-induced deafness, sleep disorders, cardiovascular diseases, endocrine diseases and other problems. The immune system plays a major role in maintaining homeostasis by recognizing and removing harmful substances in the body. Many studies have shown that noise may play vital roles in the occurrence and development of some immune diseases. In humans, both innate immunity and specific immunity can be influenced by noise, and different exposure durations and intensities of noise may exert various effects on the immune system. Short-term or low-intensity noise can enhance immune function, while long-term or high-intensity noise suppresses it. Noise can lead to the occurrence of noise-induced hearing loss (NIHL) through the production of autoantibodies such as anti-Hsp70 and anti-Hsp60 and exert adverse effects related to other immune-related diseases such as some autoimmune diseases and non-Hodgkin lymphoma. The neuroendocrine system, mainly including the hypothalamic-pituitary-adrenal (HPA) axis and the sympathetic-adrenal-medullary (SAM) system, is involved in the mechanisms of immune-related diseases induced by noise and gut microbiota dysfunction. In addition, noise exposure during pregnancy may be harmful to the immune system of the fetus. On the other hand, some studies have shown that music can improve immune function and alleviate the adverse effects caused by noise.

scholarly journals Body Joints and Trajectory Guided 3D Deep Convolutional Descriptors for Human Activity Identification

2019 ◽  
Vol 8 (12) ◽  
pp. 1016-1021
Keyword(s):  
Optical Flow ◽  
Human Activity ◽  
Large Scale ◽  
Recognition Accuracy ◽  
Computational Cost ◽  
The Body ◽  
Support Vector ◽  
3D Network ◽  
Body Joints ◽  
Body Joint

Human Activity Identification (HAI) in videos is one of the trendiest research fields in the computer visualization. Among various HAI techniques, Joints-pooled 3D-Deep convolutional Descriptors (JDD) have achieved effective performance by learning the body joint and capturing the spatiotemporal characteristics concurrently. However, the time consumption for estimating the locale of body joints by using large-scale dataset and computational cost of skeleton estimation algorithm were high. The recognition accuracy using traditional approaches need to be improved by considering both body joints and trajectory points together. Therefore, the key goal of this work is to improve the recognition accuracy using an optical flow integrated with a two-stream bilinear model, namely Joints and Trajectory-pooled 3D-Deep convolutional Descriptors (JTDD). In this model, an optical flow/trajectory point between video frames is also extracted at the body joint positions as input to the proposed JTDD. For this reason, two-streams of Convolutional 3D network (C3D) multiplied with the bilinear product is used for extracting the features, generating the joint descriptors for video sequences and capturing the spatiotemporal features. Then, the whole network is trained end-to-end based on the two-stream bilinear C3D model to obtain the video descriptors. Further, these video descriptors are classified by linear Support Vector Machine (SVM) to recognize human activities. Based on both body joints and trajectory points, action recognition is achieved efficiently. Finally, the recognition accuracy of the JTDD model and JDD model are compared.

scholarly journals Renewing the Body of Christ: Sharing of Ministries Abroad (SOMA) USA and Transnational Charismatic Anglicanism, 1978–1998

2017 ◽  
Vol 51 (4) ◽  
pp. 1243-1266
Author(s):  
JOHN MAIDEN
Keyword(s):  
The Body ◽  
Body Of Christ ◽  
Short Term ◽  
Global Mission ◽  
National Body ◽  
Global North ◽  
Late Twentieth ◽  
Global Church ◽  
Late Twentieth Century

Sharing of Ministries Abroad (SOMA) was formed in the late 1970s as an international organization for the cultivation of charismatic renewal amongst leaderships within the global Anglican Communion. This article explores the ethos and activities of its American national body. It argues that its short term, cross-cultural missions increasingly displayed mutuality and long-term partnership rather than one-directional American influence, and thus reflected a developing shift in the understanding and practice of global mission in the late twentieth century. The organiztion shaped awareness of the global Church amongst some US Episcopalians and constructed an influential transnational network within charismatic Anglicanism. Furthermore, SOMA's network was one context for the emergence of global North–South conservative solidarity in the politics of the Anglican Communion.

scholarly journals Challenges in modelling isoprene and monoterpene emission dynamics of Arctic plants: a case study from a subarctic tundra heath

2016 ◽  
Vol 13 (24) ◽  
pp. 6651-6667 ◽  
Author(s):  
Jing Tang ◽  
Guy Schurgers ◽  
Hanna Valolahti ◽  
Patrick Faubert ◽  
Päivi Tiiva ◽  
...  
Keyword(s):  
Response Curve ◽  
Large Scale ◽  
Ecosystem Model ◽  
The Arctic ◽  
Short Term ◽  
Response Curves ◽  
Arctic Plants ◽  
Scale Modelling

Abstract. The Arctic is warming at twice the global average speed, and the warming-induced increases in biogenic volatile organic compounds (BVOCs) emissions from Arctic plants are expected to be drastic. The current global models' estimations of minimal BVOC emissions from the Arctic are based on very few observations and have been challenged increasingly by field data. This study applied a dynamic ecosystem model, LPJ-GUESS, as a platform to investigate short-term and long-term BVOC emission responses to Arctic climate warming. Field observations in a subarctic tundra heath with long-term (13-year) warming treatments were extensively used for parameterizing and evaluating BVOC-related processes (photosynthesis, emission responses to temperature and vegetation composition). We propose an adjusted temperature (T) response curve for Arctic plants with much stronger T sensitivity than the commonly used algorithms for large-scale modelling. The simulated emission responses to 2 °C warming between the adjusted and original T response curves were evaluated against the observed warming responses (WRs) at short-term scales. Moreover, the model responses to warming by 4 and 8 °C were also investigated as a sensitivity test. The model showed reasonable agreement to the observed vegetation CO2 fluxes in the main growing season as well as day-to-day variability of isoprene and monoterpene emissions. The observed relatively high WRs were better captured by the adjusted T response curve than by the common one. During 1999–2012, the modelled annual mean isoprene and monoterpene emissions were 20 and 8 mg C m−2 yr−1, with an increase by 55 and 57 % for 2 °C summertime warming, respectively. Warming by 4 and 8 °C for the same period further elevated isoprene emission for all years, but the impacts on monoterpene emissions levelled off during the last few years. At hour-day scale, the WRs seem to be strongly impacted by canopy air T, while at the day–year scale, the WRs are a combined effect of plant functional type (PFT) dynamics and instantaneous BVOC responses to warming. The identified challenges in estimating Arctic BVOC emissions are (1) correct leaf T estimation, (2) PFT parameterization accounting for plant emission features as well as physiological responses to warming, and (3) representation of long-term vegetation changes in the past and the future.

Determinants of rental strategy: short-term vs long-term rental strategy

2020 ◽  
Vol 13 (12) ◽  
pp. 3873-3894
Author(s):  
Sina Shokoohyar ◽  
Ahmad Sobhani ◽  
Anae Sobhani
Keyword(s):  
Nearest Neighbor ◽  
Performance Metrics ◽  
Sharing Economy ◽  
Real Estate Market ◽  
Support Vector ◽  
Attractive Alternative ◽  
K Nearest Neighbor ◽  
Short Term ◽  
Content Type

Purpose Short-term rental option enabled via accommodation sharing platforms is an attractive alternative to conventional long-term rental. The purpose of this study is to compare rental strategies (short-term vs long-term) and explore the main determinants for strategy selection. Design/methodology/approach Using logistic regression, this study predicts the rental strategy with the highest rate of return for a given property in the City of Philadelphia. The modeling result is then compared with the applied machine learning methods, including random forest, k-nearest neighbor, support vector machine, naïve Bayes and neural networks. The best model is finally selected based on different performance metrics that determine the prediction strength of underlying models. Findings By analyzing 2,163 properties, the results show that properties with more bedrooms, closer to the historic attractions, in neighborhoods with lower minority rates and higher nightlife vibe are more likely to have a higher return if they are rented out through short-term rental contract. Additionally, the property location is found out to have a significant impact on the selection of the rental strategy, which emphasizes the widely known term of “location, location, location” in the real estate market. Originality/value The findings of this study contribute to the literature by determining the neighborhood and property characteristics that make a property more suitable for the short-term rental vs the long-term one. This contribution is extremely important as it facilitates differentiating the short-term rentals from the long-term rentals and would help better understanding the supply-side in the sharing economy-based accommodation market.

scholarly journals Cost Index Predictions for Construction Engineering Based on LSTM Neural Networks

2020 ◽  
Vol 2020 ◽  
pp. 1-14 ◽  
Author(s):  
Jiacheng Dong ◽  
Yuan Chen ◽  
Gang Guan
Keyword(s):  
Neural Network ◽  
Support Vector ◽  
Short Term ◽  
Cost Index ◽  
Long Distance ◽  
Research Knowledge ◽  
Construction Engineering ◽  
Engineering Cost ◽  
Optimization Mechanism

In recent years, the cost index predictions of construction engineering projects are becoming important research topics in the field of construction management. Previous methods have limitations in reasonably reflecting the timeliness of engineering cost indexes. The recurrent neural network (RNN) belongs to a time series network, and the purpose of timeliness transfer calculation is achieved through the weight sharing of time steps. The long-term and short-term memory neural network (LSTM NN) solves the RNN limitations of the gradient vanishing and the inability to address long-term dependence under the premise of having the above advantages. The present study proposed a new framework based on LSTM, so as to explore the applicability and optimization mechanism of the algorithm in the field of cost indexes prediction. A survey was conducted in Shenzhen, China, where a total of 143 data samples were collected based on the index set for the corresponding time interval from May 2007 to March 2019. A prediction framework based on the LSTM model, which was trained by using these collected data, was established for the purpose of cost index predictions and test. The testing results showed that the proposed LSTM framework had obvious advantages in prediction because of the ability of processing high-dimensional feature vectors and the capability of selectively recording historical information. Compared with other advanced cost prediction methods, such as Support Vector Machine (SVM), this framework has advantages such as being able to capture long-distance dependent information and can provide short-term predictions of engineering cost indexes both effectively and accurately. This research extended current algorithm tools that can be used to forecast cost indexes and evaluated the optimization mechanism of the algorithm in order to improve the efficiency and accuracy of prediction, which have not been explored in current research knowledge.

Contrasting tactical and strategic dynamics on a Clausewitz landscape

2019 ◽  
Vol 17 (2) ◽  
pp. 143-153
Author(s):  
Rodrick Wallace
Keyword(s):  
Statistical Models ◽  
Limit Theorems ◽  
Large Scale ◽  
Evolutionary Process ◽  
Short Term ◽  
Local Resources ◽  
Human Organization ◽  
The World ◽  
Short Time

Statistical models based on the asymptotic limit theorems of control and information theories allow formal examination of the essential differences between short-time “tactical” confrontations and a long-term “strategic” conflict dominated by evolutionary process. The world of extended coevolutionary conflict is not the world of sequential “muddling through.” The existential strategic challenge is to take cognitive control of a long-term dynamic in which one may, in fact, be “losing” most short-term confrontations. Winning individual battles can be a relatively direct, if not simple or easy, matter of sufficient local resources, training, and resolve. Winning extended conflicts is not direct, and requires management of subtle coevolutionary phenomena subject to a dismaying punctuated equilibrium more familiar from evolutionary theory than military doctrine. Directed evolution has given us the agricultural base needed for large-scale human organization. Directed coevolution of the inevitable conflicts between the various segments of that organization may be needed for its long-term persistence.

Sign in / Sign up

Export Citation Format

Share Document