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IEEE International Conference on Advances and Developments in Electrical and Electronics Engineering (ICADEE)

作者： Sekar, Kavaskar Kumar, Sendil S. Karthick, K. Panimalar Engn Coll Dept Elect & Elect Engn Chennai Tamil Nadu India New Prince Bhavani Coll Engn & Technol Dept Elect & Elect Engn Chennai Tamil Nadu India GMR Inst Technol Dept Elect & Elect Engn Tirupati Andhra Pradesh India

ISBN: (纸本)9781728192512

The challenge of Power Quality Disturbances (PQDs) is now admitted as a crucial characteristic of a power system network. For structured power quality, disturbance causes must be recognized and regulated. This is accomplished through detection and classification of different PQDs. This article suggests a methodology to detect and classify PQDs using machine learning algorithm. The features of the signals are extracted through a mathematical morphology filter. These features are input to train one of the machine learning algorithm called Decision Tree (DT) and builds DT model to test and classify PQDs. For the purpose of classification, ten different types of disturbances are considered in this work. The proposed method is demonstrated on a data which is generated through PQD model using MATLAB. The performance of the proposed approach is good in PQD detection with accuracy rate of 99.95%.

关键词： Power Quality Disturbances Mathematical Morphology Decision Tree machine learning algorithm

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Rainrate Estimation from FY-4A Cloud Top Temperature for Mesoscale Convective Systems by Using machine learning algorithm

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REMOTE SENSING 2021年第16期13卷 3273-3273页

作者： Lao, Ping Liu, Qi Ding, Yuhao Wang, Yu Li, Yuan Li, Meng Univ Sci & Technol China Sch Earth & Space Sci Hefei 230026 Peoples R China Univ Sci & Technol China Sch Environm Sci & Optoelect Technol Hefei 230026 Peoples R China Chinese Acad Sci Anhui Inst Opt & Fine Mech Key Lab Atmospher Opt HFIPS Hefei 230031 Peoples R China

Satellite rainrate estimation is a great challenge, especially in mesoscale convective systems (MCSs), which is mainly due to the absence of a direct physical connection between observable cloud parameters and surface rainrate. The machine learning technique was employed in this study to estimate rainrate in the MCS domain via using cloud top temperature (CTT) derived from a geostationary satellite. Five kinds of machine learning models were investigated, i.e., polynomial regression, support vector machine, decision tree, random forest, and multilayer perceptron, and the precipitation of Climate Prediction Center morphing technique (CMORPH) was used as the reference. A total of 31 CTT related features were designed to be the potential inputs for training an algorithm, and they were all proved to have a positive contribution in modulating the algorithm. Random forest (RF) shows the best performance among the five kinds of models. By combining the classification and regression schemes of the RF model, an RF-based hybrid algorithm was proposed first to discriminate the rainy pixel and then estimate its rainrate. For the MCS samples considered in this study, such an algorithm generates the best estimation, and its accuracy is definitely higher than the operational precipitation product of FY-4A. These results demonstrate the promising feasibility of applying a machine learning technique to solve the satellite precipitation retrieval problem.

关键词： rainrate estimation mesoscale convective system machine learning algorithm cloud top temperature FY-4A meteorological satellite

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Investigation of data distribution effect in Random Forest machine learning algorithm for WLCSP Reliability Prediction 15

Investigation of data distribution effect in Random Forest M...

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15th International Microsystems, Packaging, Assembly and Circuits Technology Conference (IMPACT)

作者： Chen, B. W. Tsai, T. H. Chiang, K. N. Natl Tsing Hua Univ Dept Power Mech Engn Adv Microsyst Packaging & Nanomech Res Lab Hsinchu 300 Taiwan

ISBN: (纸本)9781728198514

In recent years, due to the consumer market become larger and larger, the electronic equipment improves every day, we pay more and more attention to the reliability of electronic packaging. Electronic packaging is a complex structure that protects IC devices and plays an important role in the semiconductor industry. Our research aims to evaluate the reliability of electronic packaging, and Thermal Cycling Testing (TCT) is one of the important tests to ensure packaging reliability. TCT is a good way to test electronic packaging reliability, but it takes a lot of time and cost to do this experiment. The time required to perform a TCT can be as long as several months or years. In order to effectively reduce the test time, we often use the Finite Element Method (FEM) instead of TCT. Although FEM takes less time than TCT, it still takes some time to use the FEM to get the simulation result. We build the model according to different parameters and fixed boundary conditions, and then apply the thermal cycle load on the model to obtain the prediction life value of the electronic package. Different researchers may lead to different results, even if they use the same model parameter. If we use a large amount of validated FEM data to build a database for machine learning(ML), then we can immediately evaluate the electronic package prediction life through machine learning methods. It not only saves the time to build the model and validation, but also avoid the simulation error. This research is to use the ML method to analyze the reliability of Wafer Level Chip Scale Packaging (WLCSP). ML can find potential rules of data sets through algorithms, establish mathematical models, and obtain prediction life of different WLCSP structures. For the same data set, different algorithms may get different results. So, choosing the suitable algorithm is the most important step in using machine learning algorithm. This research uses a Random Forest algorithm (RF) and Extremely Randomized Tre

关键词： Thermal Cycling Testing Wafer Level Chip Scale Packaging Finite Element Method machine learning algorithm Random Forest Extremely Randomized Trees

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On-line fault diagnosis of rolling bearing based on machine learning algorithm 5

On-line fault diagnosis of rolling bearing based on machine ...

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5th International Conference on Information Science, Computer Technology and Transportation (ISCTT)

作者： Sun, Jinmeng Yu, Zhongqing Wang, Haiya Qingdao Univ Sch Data Sci & Software Engn Qingdao Shandong Peoples R China

ISBN: (纸本)9781728185750

In order to realize the predictive maintenance of rolling bearings in industry, this paper proposes an online fault diagnosis method for rolling bearings based on three machine learning algorithms. The method mainly includes two steps: establishing a fault diagnosis model and online fault diagnosis. Firstly, preprocess the collected bearing vibration data, and then train and optimize the fault diagnosis model, and finally realize online fault diagnosis. The experimental results show that, compared with the traditional bearing fault diagnosis method, the online fault diagnosis of the bearing by the machine learning method is simpler and has a better diagnosis effect.

关键词： rolling bearing fault diagnosis machine learning algorithm predictive maintenance

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Electricity Corpus Construction based on Data Mining and machine learning algorithm 5

Electricity Corpus Construction based on Data Mining and Mac...

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IEEE 5th Information Technology and Mechatronics Engineering Conference (ITOEC)

作者： Zhao, Liujun Kong, Weizheng Wang, Chunming State Grid Energy Res Inst CO Ltd Beijing 102209 Peoples R China

ISBN: (纸本)9781728143231;9781728143224

With the exponential increase of the Internet information, it is more difficult to collect and distinguish the useful information online. Corpus plays an important role in information retrieval and recognition. This paper proposes a corpus construction method based on data mining and machine learning for the energy and power industry. Firstly, the information data of the power industry are obtained by web crawler technology. Then they are stored in a database in structured manner. In order to construct a useful power industry corpus, the Long Short-Term Memory - Conditional Random Fields (LSTM-CRF) model are adopted for word segmentation, the Inverse Document Frequency (IDF) feature and Accessor Variety feature are used for new word discovery. The final electricity corpus consists of words, paragraphs and chapters. Based on the information retrieval and knowledge map, the built electricity corpus is demonstrated that is benefit to energy and power research.

关键词： Electricity Corpus Text information Word segmentation Data mining machine learning algorithm

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Risk Stratification of Early-Stage Cervical Cancer with Intermediate-Risk Factors: Model Development and Validation Based on machine learning algorithm

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ONCOLOGIST 2021年第12期26卷 E2217-E2226页

作者： Chu, Ran Zhang, Yue Qiao, Xu Xie, Lin Chen, Wei Zhao, Ying Xu, Yintao Yuan, Zeng Liu, Xiaolin Yin, Aijun Wang, Zhiwen Zhang, Qing Yang, Xingsheng Su, Xuantao Kong, Beihua Song, Kun Shandong Univ Qilu Hosp Cheeloo Coll Med Dept Obstet & Gynecol Jinan Shandong Peoples R China Shandong Univ Sch Control Sci & Engn Jinan Shandong Peoples R China Jining 1 Peoples Hosp Dept Obstet & Gynecol Jining Shandong Peoples R China Shandong Univ Qilu Hosp Gynecol Oncol Key Lab Jinan Shandong Peoples R China

Background Adjuvant therapy for patients with cervical cancer (CC) with intermediate-risk factors remains controversial. The objectives of the present study are to assess the prognoses of patients with early-stage CC with pathological intermediate-risk factors and to provide a reference for adjuvant therapy choice. Materials and Methods This retrospective study included 481 patients with stage IB-IIA CC. Cox proportional hazards regression analysis, machine learning (ML) algorithms, Kaplan-Meier analysis, and the area under the receiver operating characteristic curve (AUC) were used to develop and validate prediction models for disease-free survival (DFS) and overall survival (OS). Results A total of 35 (7.3%) patients experienced recurrence, and 20 (4.2%) patients died. Two prediction models were built for DFS and OS using clinical information, including age, lymphovascular space invasion, stromal invasion, tumor size, and adjuvant treatment. Patients were divided into high-risk or low-risk groups according to the risk score cutoff value. The Kaplan-Meier analysis showed significant differences in DFS (p = .001) and OS (p = .011) between the two risk groups. In the traditional Sedlis criteria groups, there were no significant differences in DFS or OS (p > .05). In the ML-based validation, the best AUCs of DFS at 2 and 5 years were 0.69/0.69, and the best AUCs of OS at 2 and 5 years were 0.88/0.63. Conclusion Two prognostic assessment models were successfully established, and risk grouping stratified the prognostic risk of patients with CC with pathological intermediate-risk factors. Evaluation of long-term survival will be needed to corroborate these findings. Implications for Practice The Sedlis criteria are intermediate-risk factors used to guide postoperative adjuvant treatment in patients with cervical cancer. However, for patients meeting the Sedlis criteria, the choice of adjuvant therapy remains controversial. This study developed two prognostic models based

关键词： Cervical cancer Intermediate-risk factor Adjuvant therapy Sedlis criteria Prediction model machine learning algorithm

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Binary classification model based on machine learning algorithm for the DC serial arc detection in electric vehicle battery system

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IET POWER ELECTRONICS 2019年第1期12卷 112-119页

作者： Xia, Kun Guo, Haotian He, Sheng Yu, Wei Xu, Jingjun Dong, Hui Univ Shanghai Sci & Technol Elect Engn Dept 516 JunGong Rd Shanghai 200093 Peoples R China Hella Shanghai Elect Co Ltd R&D Ctr Shanghai 201201 Peoples R China

Direct current (DC) serial arc faults usually occur in the damaged insulation lines or line connections, which will cause serious accidents such as fires and explosions. With the rapid increase of electric vehicles, DC serial arc faults are more and more dangerous to battery system. Therefore, a binary classification model based on machine learning algorithm was proposed to detect DC serial arc faults effectively in this study. It was optimised according to the characteristic signals of the arc to be satisfied with different loads for higher detection accuracy and robustness. In the simulative experiments for the power system electric vehicle, while the loads changing to the motor, the resistor or the inverter, it will all reach a highly successful detection rate, respectively.

关键词： DC motors power engineering computing arcs (electric) learning (artificial intelligence) fault diagnosis pattern classification electric vehicles battery powered vehicles binary classification model machine learning algorithm DC serial arc detection electric vehicle battery system direct current serial arc faults damaged insulation lines line connections electric vehicles DC serial arc faults higher detection accuracy robustness power system electric vehicle

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Big data processing with Apache Spark in university institutions: spark streaming and machine learning algorithm

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INTERNATIONAL JOURNAL OF CONTINUING ENGINEERING EDUCATION AND LIFE-LONG learning 2019年第1-2期29卷 5-20页

作者： Boachie, Emmanuel Li, Chunlin Wuhan Univ Technol Sch Comp Sci & Technol Wuhan 430063 Hubei Peoples R China Kumasi Tech Univ Dept Comp Sci Box 854 Kumasi Ghana

Data processing is an effective tool for educational sector, which can improve admission selection procedures and decisions. Most research papers focus on computational and theoretical aspect of education though little effort have been put on technological aspect of applying data mining techniques on students admission process. We therefore design a simple spark streaming framework together with machine learning algorithm to guide admission processing. We implement the spark streaming model and the proposed machine learning algorithm in a selected university using its admissions' data. The focus is on the number of students that can be admitted and those that should be rejected to reduce time and cost. The case study we evaluated show the practical usefulness of Spark streaming and machine learning algorithm for data processing in a real-time to reduce time and cost. The experiment results also confirm meaningful graphical interpretation of data using spark streaming and machine learning algorithm for students' selection for admissions.

关键词： spark streaming big data processing machine learning algorithm

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A machine-learning algorithm integrating baseline serum proteomic signatures predicts exercise responsiveness in overweight males with prediabetes

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CELL REPORTS MEDICINE 2023年第2期4卷 100944-100944页

作者： Diaz-Canestro, Candela Chen, Jiarui Liu, Yan Han, Hao Wang, Yao Honore, Eric Lee, Chi-Ho Lam, Karen S. L. Tse, Michael Andrew Xu, Aimin Univ Hong Kong State Key Lab Pharmaceut Biotechnol Hong Kong Peoples R China Univ Hong Kong Dept Med Hong Kong Peoples R China Univ Cote Azur CNRS INSERM Inst Pharmacol Mol & CellulaireLabex ICST Valbonne France Univ Hong Kong Ctr Sports & Exercise Hong Kong Peoples R China Univ Hong Kong Dept Pharmacol & Pharm Hong Kong Peoples R China

The molecular transducers conferring the benefits of chronic exercise in diabetes prevention remain to be comprehensively investigated. Herein, serum proteomic profiling of 688 inflammatory and metabolic biomarkers in 36 medication-naive overweight and obese men with prediabetes reveals hundreds of exer-cise-responsive proteins modulated by 12-week high-intensity interval exercise training, including regula-tors of metabolism, cardiovascular system, inflammation, and apoptosis. Strong associations are found between proteins involved in gas tro-intestinal mucosal immunity and metabolic outcomes. Exercise -induced changes in trefoil factor 2 (TFF2) are associated with changes in insulin resistance and fasting in-sulin, whereas baseline levels of the pancreatic secretory granule membrane major glycoprotein GP2 are related to changes in fasting glucose and glucose tolerance. A hybrid set of 23 proteins including TFF2 are differentially altered in exercise responders and non-responders. Furthermore, a machine-learning al-gorithm integrating baseline proteomic signatures accurately predicts individualized metabolic respon-siveness to exercise training.

关键词： proteomics exercise metabolic outcomes prediabetes insulin resistance personalized medicine machine learning algorithm

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Single resistive sensor for selective detection of multiple VOCs employing SnO2 hollowspheres and machine learning algorithm: A proof of concept

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SENSORS AND ACTUATORS B-CHEMICAL 2020年 321卷 128484-128484页

作者： Acharyya, Snehanjan Jana, Biswabandhu Nag, Sudip Saha, Goutam Guha, Prasanta Kumar Indian Inst Technol Kharagpur Adv Technol Dev Ctr Kharagpur 721302 W Bengal India Indian Inst Technol Kharagpur Dept Elect & Elect Commun Engn Kharagpur 721302 W Bengal India

Selective detection of harmful gasses and volatile organic compounds (VOCs) in the ambient has become a major challenge. Primarily, semiconducting metal-oxide based gas sensors sense various gases simultaneously, hence their selectivity is poor. This paper presents a single chemiresistive metal-oxide gas sensor for identification of multiple VOCs accurately by employing highly sensitive microstructure and machine learning tools. Tin oxide (SnO2) hollowspheres were taken as sensing material that were prepared through optimized hydrothermal route. Different characterizations were carried out to confirm the formation of desired morphology and structural features. The sensor device was fabricated by controlled drop cast technique over gold based interdigitated electrodes. The sensor showed remarkable response towards the target VOCs with high sensitivity and fast recovery time. Incorporation of machine learning algorithm on the obtained sensor data provided accurate identification of all the VOCs (best performance shown by random forest). In addition, the quantitative prediction of gas concentration was performed for each target gas using regression model. In comparison to e-noses (having array of sensors with different sensing material), a single chemiresistive metal-oxide sensor with proper machine learning tool is simple, economic, compact and easy to fabricate.

关键词： Indoor air quality Volatile organic compound Selectivity Chemiresistive gas sensor Tin oxide hollowspheres machine learning algorithm

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