Experiment Study and Process Parameters Analysis on Turbo Air Classifier for Talc Powder

2012 ◽  
Vol 446-449 ◽  
pp. 522-527
Author(s):  
Yuan Yu ◽  
Jia Xiang Liu ◽  
Li Ping Gao
Keyword(s):  
Process Parameters ◽  
Classification Performance ◽  
Parameter Analysis ◽  
Analysis Method ◽  
Sample Data ◽  
Parameters Analysis ◽  
Air Classifier ◽  
The One ◽  
Building Engineering

Talc powder is widely used in building engineering, especially preparation for coating, waterproof material and ceramics. With increasing demands for building material quality, the requirement for the particle fineness and particle-size distribution of talc power becomes higher than ever. A new method of process parameters analysis on turbo air classifier for talc powder is put forward in this paper. The effect of the two process parameters on a classification performance index is reflected visually through the 3-D drawing based on Matlab, so the one-dimensional process parameter analysis method is expanded to the two-dimensional process parameters analysis method. In the present study, a turbo air classifier is used as the classification system and fine talc powder is used as materials. The sample data is gathered through setting different process parameters. The experiment results show that process parameters analysis can be implemented quickly and visually. In actual production applications of turbo air classifier system, the user can select the suitable process parameters flexibly considering the production requirements according to the 3-D meshes based on Matlab. This method is also applicable for classification of other powder.

scholarly journals Study of cardiac arrhythmia classification based on Convolutional Neural Network

2020 ◽  
Vol 17 (2) ◽  
pp. 445-458
Author(s):  
Yonghui Dai ◽  
Bo Xu ◽  
Siyu Yan ◽  
Jing Xu
Keyword(s):  
Neural Network ◽  
Cardiovascular Diseases ◽  
Convolutional Neural Network ◽  
Classification Performance ◽  
Time Intervals ◽  
Ecg Signals ◽  
Layer Structures ◽  
Time Periods ◽  
The One

Cardiovascular disease is one of the diseases threatening the human health, and its diagnosis has always been a research hotspot in the medical field. In particular, the diagnosis technology based on ECG (electrocardiogram) signal as an effective method for studying cardiovascular diseases has attracted many scholars? attention. In this paper, Convolutional Neural Network (CNN) is used to study the feature classification of three kinds of ECG signals, which including sinus rhythm (SR), Ventricular Tachycardia (VT) and Ventricular Fibrillation (VF). Specifically, different convolution layer structures and different time intervals are used for ECG signal classification, such as the division of 2-layer and 4-layer convolution layers, the setting of four time periods (1s, 2s, 3s, 10s), etc. by performing the above classification conditions, the best classification results are obtained. The contribution of this paper is mainly in two aspects. On the one hand, the convolution neural network is used to classify the arrhythmia data, and different classification effects are obtained by setting different convolution layers. On the other hand, according to the data characteristics of three kinds of ECG signals, different time periods are designed to optimize the classification performance. The research results provide a reference for the classification of ECG signals and contribute to the research of cardiovascular diseases.

scholarly journals Tumor imaging diagnosis analysis based on improved KNN algorithm

2021 ◽  
Vol 2132 (1) ◽  
pp. 012018
Author(s):  
Cailing Wang ◽  
LeiChao Li ◽  
SuQiang He ◽  
Jing Zhang
Keyword(s):  
Tumor Imaging ◽  
Image Features ◽  
Parameter Analysis ◽  
Analysis Method ◽  
Abnormality Detection ◽  
Practical Applications ◽  
Classification Image ◽  
Image Characteristics ◽  
Training Samples

Abstract As a simple, effective and non-parameter analysis method, knn is widely used in text classification, image recognition, etc. [1]. However, this method requires a lot of calculations in practical applications, and the uneven distribution of training samples will directly lead to a decrease in the accuracy of tumor image classification. To solve this problem, we propose a method based on dynamic weighted KNN to improve the accuracy of classification, which is used to solve the problem of automatic prediction and classification of medical tumor images based on image features and automatic abnormality detection. According to the classification of tumor image characteristics, it can be divided into two categories: benign and malignant. This method can assist doctors in making medical diagnosis and analysis more accurately. The experimental results show that this method has certain advantages compared with the traditional KNN algorithm.

Pattern Classification Based on Self-Organizing Feature Mapping Neural Network

2013 ◽  
Vol 448-453 ◽  
pp. 3645-3649 ◽  
Author(s):  
Shuo Ding ◽  
Xiao Heng Chang ◽  
Qing Hui Wu
Keyword(s):  
Neural Network ◽  
Pattern Classification ◽  
Classification Performance ◽  
Adjustment Process ◽  
Data Sets ◽  
Two Dimensional ◽  
Sample Data ◽  
Sofm Network ◽  
Traditional Pattern

Traditional pattern classification methods are not always efficient because sample data sets are sometimes incomplete and there are exceptions and counter examples. In this paper, SOFM neural network is applied in pattern classification of two-dimensional vectors after analysis of its structure and algorithm. The method to establish SOFM network via MATLAB7.0 is introduced before the network is applied to classify two-dimensional vectors. The adjustment process of weight vectors together with classification performance of SOFM model are also tested in the condition of different number of training steps. The simulation results show that the classification approach based on SOFM model is effective because of its fast speed, high accuracy and strong generalization ability.

Pathology of acceleration programs in corporate accelerators of Iran

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Saeed Heshmati ◽  
Maysam Shafiee
Keyword(s):  
Thematic Analysis ◽  
Design Methodology ◽  
Analysis Method ◽  
Structured Interviews ◽  
Content Type ◽  
Team Members ◽  
Acceleration Programs ◽  
The One ◽  
Acceleration Structure

PurposeThis study was designed to detect the failures in Iranian accelerators. This paper attempts to identify these effects from the perspective of accelerator managers and founders of startups. The main goals of this article are as follows: (1) What are the failures of Iran's acceleration programs from the perspective of accelerator managers? (2) What are the failures of Iran's acceleration programs from the perspective of startup teams? (3) What are some of failures of the acceleration programs that both groups agree on?Design/methodology/approachIt has been attempted to conduct semi-structured interviews with managers of corporate accelerators on the one hand and startups accelerated in these accelerators on the other. The interviewees were selected using snowball method and consisted of 9 accelerator managers out of 7 accelerators and 15 startups based on 5 accelerators. The analysis of the information extracted from the interviews and coding of the failure identified in the accelerators was performed using the thematic analysis method. In order to assess the validity of this study, an entrepreneurial doctoral student was asked to codify the interviews individually to compare the extracted codes.FindingsFinally, 34 problems have been identified that are divided into four main themes related to mentorship, acceleration program, acceleration structure and infrastructure and internal startup team problems. Overall, the greatest agreement among the failures identified as wrong orientation by untrained mentors, the lack of complementary in ability and skills of team members, the lack of knowledge of mentors, the lack of acceleration managers in entrepreneurship and the lack of a proper leader in startup teams.Originality/valueThis study aimed to investigate the failures of corporate accelerators in Iran as a developing country, which is the first survey in Iran. We have many researches about the pathology and identify failures of accelerators, but in corporate accelerators, little research has been done. The authors have a classification of failures in corporate accelerators by using thematic analysis. In this study, accelerators' managers and founders of startups were interviewed and 34 failures were identified.

Security Test by using F T M and Data Allocation Strategies on Leakage Detection

2005 ◽  
Vol 4 (2) ◽  
pp. 393-400
Author(s):  
Pallavali Radha ◽  
G. Sireesha
Keyword(s):  
Third Party ◽  
Distributed Data ◽  
Data Allocation ◽  
Security Testing ◽  
Sensitive Data ◽  
Sample Data ◽  
The One ◽  
Security Test ◽  
Data Request ◽  
Allocation Strategies

The data distributors work is to give sensitive data to a set of presumably trusted third party agents.The data i.e., sent to these third parties are available on the unauthorized places like web and or some ones systems, due to data leakage. The distributor must know the way the data was leaked from one or more agents instead of as opposed to having been independently gathered by other means. Our new proposal on data allocation strategies will improve the probability of identifying leakages along with Security attacks typically result from unintended behaviors or invalid inputs.  Due to too many invalid inputs in the real world programs is labor intensive about security testing.The most desirable thing is to automate or partially automate security-testing process. In this paper we represented Predicate/ Transition nets approach for security tests automated generationby using formal threat models to detect the agents using allocation strategies without modifying the original data.The guilty agent is the one who leaks the distributed data. To detect guilty agents more effectively the idea is to distribute the data intelligently to agents based on sample data request and explicit data request. The fake object implementation algorithms will improve the distributor chance of detecting guilty agents.

Somatoform disorders: diseases of the civilization

2020 ◽  
pp. 25-30
Author(s):  
I. Kukhtevich
Keyword(s):  
Health Professionals ◽  
Somatoform Disorders ◽  
Neuropsychiatric Disorders ◽  
The Other ◽  
Significant Part ◽  
Organic Basis ◽  
Autonomic Disorders ◽  
The One ◽  
Somatic Diseases

Functional autonomic disorders occupy a significant part in the practice of neurologists and professionals of other specialties as well. However, there is no generally accepted classification of such disorders. In this paper the authors tried to show that functional autonomic pathology corresponds to the concept of somatoform disorders combining syndromes manifested by visceral, borderline psychopathological, neurological symptoms that do not have an organic basis. The relevance of the problem of somatoform disorders is that on the one hand many health professionals are not familiar enough with manifestations of borderline neuropsychiatric disorders, often forming functional autonomic disorders, and on the other hand they overestimate somatoform symptoms that are similar to somatic diseases.

A Study on the Auxiliary Diagnosis of Thyroid Disease Images Based on Multiple Dimensional Deep Learning Algorithms

2020 ◽  
Vol 16 (3) ◽  
pp. 199-205
Author(s):  
Yuejun Liu ◽  
Yifei Xu ◽  
Xiangzheng Meng ◽  
Xuguang Wang ◽  
Tianxu Bai
Keyword(s):  
Deep Learning ◽  
Learning Algorithms ◽  
Region Of Interest ◽  
Classification Performance ◽  
Thyroid Diseases ◽  
Great Success ◽  
Learning Models ◽  
Good Classification Performance ◽  
Spect Images

Background: Medical imaging plays an important role in the diagnosis of thyroid diseases. In the field of machine learning, multiple dimensional deep learning algorithms are widely used in image classification and recognition, and have achieved great success. Objective: The method based on multiple dimensional deep learning is employed for the auxiliary diagnosis of thyroid diseases based on SPECT images. The performances of different deep learning models are evaluated and compared. Methods: Thyroid SPECT images are collected with three types, they are hyperthyroidism, normal and hypothyroidism. In the pre-processing, the region of interest of thyroid is segmented and the amount of data sample is expanded. Four CNN models, including CNN, Inception, VGG16 and RNN, are used to evaluate deep learning methods. Results: Deep learning based methods have good classification performance, the accuracy is 92.9%-96.2%, AUC is 97.8%-99.6%. VGG16 model has the best performance, the accuracy is 96.2% and AUC is 99.6%. Especially, the VGG16 model with a changing learning rate works best. Conclusion: The standard CNN, Inception, VGG16, and RNN four deep learning models are efficient for the classification of thyroid diseases with SPECT images. The accuracy of the assisted diagnostic method based on deep learning is higher than that of other methods reported in the literature.

scholarly journals Modes of Interaction in Naturally Occurring Medical Encounters With General Practitioners: The “One in a Million” Study

2021 ◽  
pp. 104973232199379
Author(s):  
Olaug S. Lian ◽  
Sarah Nettleton ◽  
Åge Wifstad ◽  
Christopher Dowrick
Keyword(s):  
General Practitioners ◽  
Narrative Analysis ◽  
Mode Competition ◽  
General Applicability ◽  
Naturally Occurring ◽  
Wide Range ◽  
The One ◽  
Narrative Mode ◽  
Modes Of Interaction

In this article, we qualitatively explore the manner and style in which medical encounters between patients and general practitioners (GPs) are mutually conducted, as exhibited in situ in 10 consultations sourced from the One in a Million: Primary Care Consultations Archive in England. Our main objectives are to identify interactional modes, to develop a classification of these modes, and to uncover how modes emerge and shift both within and between consultations. Deploying an interactional perspective and a thematic and narrative analysis of consultation transcripts, we identified five distinctive interactional modes: question and answer (Q&A) mode, lecture mode, probabilistic mode, competition mode, and narrative mode. Most modes are GP-led. Mode shifts within consultations generally map on to the chronology of the medical encounter. Patient-led narrative modes are initiated by patients themselves, which demonstrates agency. Our classification of modes derives from complete naturally occurring consultations, covering a wide range of symptoms, and may have general applicability.

Sign in / Sign up

Export Citation Format

Share Document