scholarly journals Application of spectral analysis methods for data pre-processing of anomaly detection problem of vibration diagnostics in non-destructive testing

2021 ◽  
Vol 2127 (1) ◽  
pp. 012028
Author(s):  
N N Trufanov ◽  
D V Churikov ◽  
O V Kravchenko
Keyword(s):  
Machine Learning ◽  
Technological Process ◽  
Numerical Control ◽  
Primary Data ◽  
Training Methods ◽  
Destructive Testing ◽  
Vibration Diagnostics ◽  
Factors Affecting ◽  
Frequency Domains ◽  
Preliminary Preparation

Abstract The paper is devoted to the problem of primary data processing obtained in the vibration measurements during the processing of the workpiece on a milling machine with computer numerical control. An experimental setup is described and an algorithm for analysing vibration diagnostics signals using a mathematical machine learning tool is proposed. Special attention is paid to the study of the rigidity characteristics of the machine at different relative positions of its components. The analysis of the equipment design and factors affecting the ongoing process is carried out, as a result of which the received signal is processed and its characteristic fragments in the time and frequency domains are identified. The data is prepared for further use in solving the problem of detecting anomalies of the technological process, which implies predicting the progress of the technological process based on a mathematical model constructed using machine learning methods, and identifying deviations of the real technological process from the forecast. Preliminary preparation is carried out using the windowed Fourier transform. Various variants of windows in the transformation are considered, including those constructed using atomic functions. Calculations are performed using the Python 3.9 language, the main results are supported by graphs. The development of training methods for the considered models of neural networks is the subject of further research.

Analisis Partisipasi Petani dalam Asuransi Pertanian

2019 ◽  
Vol 2 (6) ◽  
pp. 793
Author(s):  
Mega Nabilla Ardiana ◽  
Ivanovich Agusta
Keyword(s):  
Program Implementation ◽  
Education Level ◽  
External Factors ◽  
Primary Data ◽  
Agricultural Insurance ◽  
Factors Affecting ◽  
Participation Level ◽  
Regression Test ◽  
Internal And External Factors ◽  
Program Data

Farmers participation is crucial for succeeding in the implementation of agricultural insurance in Indonesia. The purposes of this research are analyzing farmers' form and level of participation and also identifying some factors affecting farmer’s participation in agricultural insurance. The research was conducted in Curug Bitung Village, District of Nanggung, Bogor Regency involving 40 respondents. Primary data includes farmers characteristics, farmers participation form and farmers participation level as program beneficiaries. The data were processed using multivariable linear regression test. The results showed that the intensity of communication, age, education level, income level and length of stay did not significantly affect the participation level of farmers whose majority were at the level of no participation. Non-compliance occurs during program implementation. The form of participation shown by farmers in the program is varied.Keywords:  agricultural insurance, internal and external factors, farmers' participation ABSTRAK Partisipasi petani menjadi hal yang penting dalam rangka menyukseskan penerapan asuransi pertanian di Indonesia. Tujuan penelitian ini adalah menganalisis bentuk dan tingkat partisipasi petani dalam asuransi pertanian serta mengidentifikasi faktor-faktor yang memengaruhi partisipasi petani dalam program asuransi pertanian. Penelitian dilakukan di Desa Curug Bitung, Kecamatan Nanggung, Kabupaten Bogor dengan melibatkan 40 responden. Data primer meliputi karakteristik petani, bentuk partisipasi petani dan tingkat partisipasi petani penerima program. Data diolah menggunakan uji regresi linier multivariable. Hasil penelitian menunjukkan intensitas komunikasi, usia, tingkat pendidikan, tingkat pendapatan dan lama tinggal tidak berpengaruh secara signifikan terhadap tingkat partisipasi petani yang  mayoritas berada pada tingkatan tidak ada partisipasi. Ketidaksesuaian banyak terjadi selama penyelenggaraan program. Adapun bentuk partisipasi yang ditunjukkan petani dalam program bervariasi.Kata kunci: asuransi pertanian, faktor internal-eksternal, partisipasi petani

Bantuan Hukum Cuma-Cuma Bagi Tersangka yang Tidak Mampu Pada Tingkat Penyidikan

2020 ◽  
Vol 19 (2) ◽  
Author(s):  
Rio Saputra ◽  
Mokhammad Najih
Keyword(s):  
Secondary Data ◽  
Primary Data ◽  
Legal Aid ◽  
Criminal Cases ◽  
Legal Research ◽  
Factors Affecting ◽  
Legal Structure ◽  
Procedure Code ◽  
Study Techniques ◽  
The Right

<p><em>Suspects have the right to obtain legal assistance, especially for suspects who are classified as economically disadvantaged in accordance with Article 56 of the Criminal Procedure Code (KUHAP). The facts show that there are many irregularities in the implementation of legal aid, therefore it is necessary to know about the implementation of free legal aid for suspects who are incapacitated at the level of investigation and the factors that become obstacles in the implementation of legal aid. This legal research is an empirical legal research and this research is descriptive in nature. The data used are primary data and secondary data. The techniques used to collect data were document study techniques and interview techniques. Inhibiting factors affecting the implementation of free legal aid for suspects who are unable at the level of investigation can be classified and differentiated into 3 factors, namely, legal substance, legal structure, and legal culture).</em></p><p><strong><em>Keywords: </em></strong><em>Legal Aid, Criminal Cases</em></p>

scholarly journals Intelligibility in English as a lingua franca – The interpreters’ perspective

2020 ◽  
Vol 9 (2) ◽  
pp. 173-193
Author(s):  
Karin Reithofer
Keyword(s):  
English Language ◽  
Language Skills ◽  
Background Knowledge ◽  
Evidence Based ◽  
Training Methods ◽  
Lingua Franca ◽  
Factors Affecting ◽  
Specialist Knowledge ◽  
Written Questions ◽  
Subordinate Role

AbstractThis article aims at examining the topic of ELF intelligibility from the interpreters’ perspective. Therefore, the focus is put on listener factors affecting intelligibility in settings typical for interpreting i.e. monologic settings. Data from various intelligibility studies are compared with results from a study that tested an ELF user’s intelligibility in a conference-like ELF setting and examined the influence of listener variables such as background knowledge, familiarity with ELF use or proficiency in English. In this study, an Italian speaker gave an impromptu speech in English to participants who subsequently were asked to answer written questions on the topic. The results showed that listeners with more experience in ELF settings reached the highest score in the test, while participants with specialist knowledge were unable to profit from it. The participants’ English language skills played a rather subordinate role. The findings of this study may prove useful for considerations in interpreter training and can contribute to the development of concrete, evidence-based training methods for interpreters in the interpreting sub-skill of comprehension.

scholarly journals Advances in Blast-Induced Impact Prediction—A Review of Machine Learning Applications

Minerals ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 601
Author(s):  
Nelson K. Dumakor-Dupey ◽  
Sampurna Arya ◽  
Ankit Jha
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Academic Research ◽  
Empirical Models ◽  
Rock Breakage ◽  
Environmental Implications ◽  
Learning Methods ◽  
Factors Affecting ◽  
Impact Prediction ◽  
Machine Learning Methods

Rock fragmentation in mining and construction industries is widely achieved using drilling and blasting technique. The technique remains the most effective and efficient means of breaking down rock mass into smaller pieces. However, apart from its intended purpose of rock breakage, throw, and heave, blasting operations generate adverse impacts, such as ground vibration, airblast, flyrock, fumes, and noise, that have significant operational and environmental implications on mining activities. Consequently, blast impact studies are conducted to determine an optimum blast design that can maximize the desirable impacts and minimize the undesirable ones. To achieve this objective, several blast impact estimation empirical models have been developed. However, despite being the industry benchmark, empirical model results are based on a limited number of factors affecting the outcomes of a blast. As a result, modern-day researchers are employing machine learning (ML) techniques for blast impact prediction. The ML approach can incorporate several factors affecting the outcomes of a blast, and therefore, it is preferred over empirical and other statistical methods. This paper reviews the various blast impacts and their prediction models with a focus on empirical and machine learning methods. The details of the prediction methods for various blast impacts—including their applications, advantages, and limitations—are discussed. The literature reveals that the machine learning methods are better predictors compared to the empirical models. However, we observed that presently these ML models are mainly applied in academic research.

FACTORS AFFECTING URBAN AGRICULTURE DEVELOPMENT IN THAI NGUYEN PROVINCE, VIET NAM

2021 ◽  
Vol 9 (1) ◽  
pp. 222-233
Author(s):  
Vu Bach Diep ◽  
Dinh Hong Linh ◽  
Bui Thi Minh Hang
Keyword(s):  
Urban Agriculture ◽  
Urban Areas ◽  
Industrial Development ◽  
Agricultural Development ◽  
Agricultural Land ◽  
Primary Data ◽  
Factors Affecting ◽  
Infrastructure Policy ◽  
Process Of Urbanization ◽  
Agriculture Development

The process of urbanization is taking place fast and vigorously in large urban and peri-urban areas in Vietnam. According to national forecasts, the rate of urbanization nationwide will reach 39.3% by 2020 and 50-55% by 2035. Thai Nguyen is a province in the midland and mountainous region. The province is located at the northern gateway and bordered with Hanoi capital. In recent years, the agricultural land area of Thai Nguyen province has narrowed due to the urban-industrial development. Urban agriculture development is an inevitable direction, creating safe and high quality food products, protecting the ecological environment, and increasing people's income. Thai Nguyen is one of the provinces promoting sustainable urban agricultural development. Secondary and primary data sources are analyzed and synthesized by descriptive statistical methods. The article will analyze five groups of factors affecting urban agricultural development in Thai Nguyen province in the period 2015-2018, including Socio-economic; Natural conditions and infrastructure; Policy factors; Planning factors; Links and integration.

Factors Affecting on Entrepreneurial Intention of University Students (Case Study on Wollo University)

2021 ◽  
Author(s):  
Abdu Kamil
Keyword(s):  
Environmental Factors ◽  
University Students ◽  
Family Background ◽  
Secondary Data ◽  
Entrepreneurial Intention ◽  
Primary Data ◽  
Personal Factors ◽  
Factors Affecting ◽  
College Of Business

Abstract Background: Entrepreneurship is essential in creating, fulfilling and forming a healthy economy. This study is conducted to investigate Factor Affecting on Entrepreneurial Intention: The case study on Wollo University Students. Some studies have been done in this area but only a few were conducted in Ethiopia. This research aims to address the gap that exists due to the weakness of previous studies to verify the factors that affect entrepreneurial intention and provide more clarification on the topic. Methods: For the purpose of this study explanatory research design was employed. The researcher used stratified random sampling to classify all participants into seven colleges and one school of law. From each stratum proportionally by using purposive sampling to select 226 respondents with graduate students from college of business and economics for the desire of the study. Both primary and secondary data were collected. Primary data were collected through structured questionnaire from 210 students. Secondary data were collected from previous studies and used as reference. Results: The correlation and regression analysis has been applied to see the relationship and how independent variables influence entrepreneurial intention. From the analyses it is confirmed that demographic factors have statistically insignificant effect on entrepreneurial intention, while personal factors, environmental factors and family background have a statistically significant effect on entrepreneurial intention. Conclusions: Based on the findings it is concluded that demographic factor does not affect entrepreneurial intention while personal factors, environmental factors and family background affect entrepreneurial intention.

An Experimental Study of Spammer Detection on Chinese Microblogs

2020 ◽  
Vol 30 (11n12) ◽  
pp. 1759-1777
Author(s):  
Jialing Liang ◽  
Peiquan Jin ◽  
Lin Mu ◽  
Jie Zhao
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
User Behavior ◽  
Real Data ◽  
User Profile ◽  
Data Set ◽  
Sina Weibo ◽  
Factors Affecting ◽  
The Government ◽  
Hot Event

With the development of Web 2.0, social media such as Twitter and Sina Weibo have become an essential platform for disseminating hot events. Simultaneously, due to the free policy of microblogging services, users can post user-generated content freely on microblogging platforms. Accordingly, more and more hot events on microblogging platforms have been labeled as spammers. Spammers will not only hurt the healthy development of social media but also introduce many economic and social problems. Therefore, the government and enterprises must distinguish whether a hot event on microblogging platforms is a spammer or is a naturally-developing event. In this paper, we focus on the hot event list on Sina Weibo and collect the relevant microblogs of each hot event to study the detecting methods of spammers. Notably, we develop an integral feature set consisting of user profile, user behavior, and user relationships to reflect various factors affecting the detection of spammers. Then, we employ typical machine learning methods to conduct extensive experiments on detecting spammers. We use a real data set crawled from the most prominent Chinese microblogging platform, Sina Weibo, and evaluate the performance of 10 machine learning models with five sampling methods. The results in terms of various metrics show that the Random Forest model and the over-sampling method achieve the best accuracy in detecting spammers and non-spammers.

Implementation of Machine Learning Algorithms for Prediction of Fluidelastic Instability in Tube Arrays

2021 ◽  
Vol 143 (2) ◽  
Author(s):  
Joaquin E. Moran ◽  
Yasser Selima
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Learning Algorithm ◽  
Machine Learning Algorithms ◽  
Two Phase ◽  
Factors Affecting ◽  
Logistic Regression Models ◽  
Number Of Factors ◽  
Tube Arrays ◽  
Fluidelastic Instability

Abstract Fluidelastic instability (FEI) in tube arrays has been studied extensively experimentally and theoretically for the last 50 years, due to its potential to cause significant damage in short periods. Incidents similar to those observed at San Onofre Nuclear Generating Station indicate that the problem is not yet fully understood, probably due to the large number of factors affecting the phenomenon. In this study, a new approach for the analysis and interpretation of FEI data using machine learning (ML) algorithms is explored. FEI data for both single and two-phase flows have been collected from the literature and utilized for training a machine learning algorithm in order to either provide estimates of the reduced velocity (single and two-phase) or indicate if the bundle is stable or unstable under certain conditions (two-phase). The analysis included the use of logistic regression as a classification algorithm for two-phase flow problems to determine if specific conditions produce a stable or unstable response. The results of this study provide some insight into the capability and potential of logistic regression models to analyze FEI if appropriate quantities of experimental data are available.

scholarly journals Factors contributing towards research productivity in higher education

2020 ◽  
Vol 9 (1) ◽  
pp. 203
Author(s):  
Caroline Henry ◽  
Nor Azura Md Ghani ◽  
Umi Marshida Abd Hamid ◽  
Ahmad Naqiyuddin Bakar
Keyword(s):  
Higher Education ◽  
Research Productivity ◽  
Academic Staff ◽  
Student Perception ◽  
Primary Data ◽  
Factors Affecting ◽  
Age Cohort ◽  
Role Of Principal ◽  
Behavioural Factors ◽  
Conducting Research

<span>Research Productivity (RP) is the key element in the establishment of ranking and rating system in the Higher Education (HE) sector. Despite of the many initiatives taken to enliven the research culture among academic staff, there are still constraints and resistance towards conducting research. Therefore, this study attempts to identify the factors affecting RP and develop an appropriate model to determine the RP of an academic staff in Universiti Teknologi MARA (UiTM). In this study, 5 research related indicators were used in the determination of RP. Since the population size of UiTM is large, the primary data was collected by using questionnaire survey and stratified random sampling. The variables that were found to be significant in determining RP of an academic staff were age cohort, highest qualification, cluster and track emphasis. Satisfaction towards annual KPI, UiTM current policy and monthly income were also found to influence the RP of an academic staff. In addition, perceiving the role of principal investigator as a chore and burden and supervising and graduating a PhD student perception as burden and pleasure were also found to be affecting RP. Using these variables, Logistic Regression Model was used to determine the RP of an academic staff in UiTM. In conclusion, personal, environmental and behavioural factors were found to have influence on the RP among academic staff of UiTM. Therefore, generally it is possible to maximize the RP of academic staff by identifying the factors influencing RP followed by strategic management and proper monitoring system.</span>

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