Algorithm Development
Recently Published Documents





Inventions ◽  
2022 ◽  
Vol 7 (1) ◽  
pp. 12
Qi Zhang ◽  
Wenhui Pei

The digital signal processing (DSP) processor-in-the-loop tests based on automatic code generation technology are studied. Firstly, the idea of model-based design is introduced, and the principle and method of embedded code automatic generation technology are analyzed by taking the automatic code generation of the DSP control algorithm for pulse width modulation (PWM) output as an example. Then, the control system model is established on MATLAB/Simulink. After verifying the model through simulation, the target board platform is established with DSP as the core processor, and the automatically generated code is tested by the processor-in-the-loop (PIL). The results show that the technology greatly shortens the development cycle of the project, improves the robustness and consistency of the control code, and can be widely used in the complex algorithm development process of the controller, from intelligent design and modeling to implementation.

2022 ◽  
Vol 2022 ◽  
pp. 1-13
Jiamin Zhang ◽  
Jiarui Zhang

Trains can be optimally spread over the period of the cyclic timetable. By integrating sequencing issue with headway time together, this paper studies the structure optimization of mixed-speed train traffic for a cyclic timetable. Firstly, by taking it as a job-shop problem with sequence-dependent setup times on one machine, in the type of infinite capacity resource with headway (ICR + H), the problem is transformed to alternative graph (AG) and then recast to the mixed-speed train traffic planning (MSTTP) model. For the multiobjective in MSTTP, three indicators are optimized, i.e., heterogeneity, cycle time, and buffer time, which correspond to diversity of train service toward passenger, capacity consumption of rail network, and stability of train operation, respectively. Secondly, the random-key genetic algorithm (RKGA) is proposed to tackle the sequence and headway simultaneously. Finally, RKGA is coded with visual studio C# and the proposed method is validated with a case study. The rail system considered is a line section encompassing a territory of 180 km with 15 mixed-speed trains in each cycle of the timetable. Results indicate the comprehensively balanced train plan for all stakeholders from random variations of train sequence and headway time. Both the quantitative proportion of heterogeneity/homogeneity (e.g., 2.5) about the optimized distribution of the mixed train traffic and the link between train headway time and the sequence for each traffic scenario are found. All the findings can be used to arrange the mixed-speed train traffic more scientifically.

2022 ◽  
Xiaoling Tong ◽  
Edward Luke ◽  
Eric M. Collins

A. I. Dolgushina ◽  
G. M. Khusainova ◽  
O. B. Nesmeyanova ◽  
N. V. Kirsh ◽  
O. V. Solovieva ◽  

Aim. An algorithm development for joint pain differential diagnosis in patients with inflammatory bowel disorders (IBD) and its validation in clinical practice.Materials and methods. A total of 349 IBD patients hospitalised for gastroenterological complaints at the Chelyabinsk Regional Clinical Hospital during 2017–2020 have been examined.Results. Upon survey, 97 (27.8%) IBD patients complained of joint pain. Ulcerative colitis (UC) predominated (79 patients; 81.4%), Crohn’s disease (CD) had a 18.6% incidence. In survey, 27% UC and 32.1% CD patients reported joint pain (p = 0.26). Among IBD patients, 52.6% had mechanical, and 47.4% — inflammatory pain. The inflammatory back pain (IBP) rate in survey cohort was 23.7%. Use of a diagnostic algorithm allowed concomitant rheumatic disease detection in 7 (7.2%) patients from the IBD–joint pain cohort: 2 patients were diagnosed with psoriatic spondyloarthritis, 2 — rheumatoid arthritis, 1 — gout and 2 — with ankylosing spondylitis. IBD-associated arthritis was diagnosed in 41 (42.3%) cases, osteoarthritis — in 38 (39.2%) IBD patients with joint pain, arthralgia with no objective inflammation, impaired joint function or lesions in X-ray and/or ultrasound — in 13 (13.4%) patients.Conclusion. Joint pain complaints are common in IBD patients and require a multispecialty rheumatologists-involving approach to proceed with differential diagnosis and opting for treatment tactics. A clinically verified algorithm coupled with laboratory tests and instrumental imaging facilitates diagnosis and optimal therapy selection in IBD patients with complaints of joint pain. 

2021 ◽  
Vol 11 (1) ◽  
pp. 192
Cheng-Yu Lin ◽  
Yi-Wen Wang ◽  
Febryan Setiawan ◽  
Nguyen Thi Hoang Trang ◽  
Che-Wei Lin

Background: Heart rate variability (HRV) and electrocardiogram (ECG)-derived respiration (EDR) have been used to detect sleep apnea (SA) for decades. The present study proposes an SA-detection algorithm using a machine-learning framework and bag-of-features (BoF) derived from an ECG spectrogram. Methods: This study was verified using overnight ECG recordings from 83 subjects with an average apnea–hypopnea index (AHI) 29.63 (/h) derived from the Physionet Apnea-ECG and National Cheng Kung University Hospital Sleep Center database. The study used signal preprocessing to filter noise and artifacts, ECG time–frequency transformation using continuous wavelet transform (CWT), BoF feature generation, machine-learning classification using support vector machine (SVM), ensemble learning (EL), k-nearest neighbor (KNN) classification, and cross-validation. The time length of the spectrogram was set as 10 and 60 s to examine the required minimum spectrogram window time length to achieve satisfactory accuracy. Specific frequency bands of 0.1–50, 8–50, 0.8–10, and 0–0.8 Hz were also extracted to generate the BoF to determine the band frequency best suited for SA detection. Results: The five-fold cross-validation accuracy using the BoF derived from the ECG spectrogram with 10 and 60 s time windows were 90.5% and 91.4% for the 0.1–50 Hz and 8–50 Hz frequency bands, respectively. Conclusion: An SA-detection algorithm utilizing BoF and a machine-learning framework was successfully developed in this study with satisfactory classification accuracy and high temporal resolution.

Oleksandr Dudin ◽  
Ozar Mintser ◽  
Oksana Sulaieva ◽  

Introduction. Over the past few decades, thanks to advances in algorithm development, the introduction of available computing power, and the management of large data sets, machine learning methods have become active in various fields of life. Among them, deep learning possesses a special place, which is used in many spheres of health care and is an integral part and prerequisite for the development of digital pathology. Objectives. The purpose of the review was to gather the data on existing image analysis technologies and machine learning tools developed for the whole-slide digital images in pathology. Methods: Analysis of the literature on machine learning methods used in pathology, staps of automated image analysis, types of neural networks, their application and capabilities in digital pathology was performed. Results. To date, a wide range of deep learning strategies have been developed, which are actively used in digital pathology, and demonstrated excellent diagnostic accuracy. In addition to diagnostic solutions, the integration of artificial intelligence into the practice of pathomorphological laboratory provides new tools for assessing the prognosis and prediction of sensitivity to different treatments. Conclusions: The synergy of artificial intelligence and digital pathology is a key tool to improve the accuracy of diagnostics, prognostication and personalized medicine facilitation

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Jie Gao

In order to overcome the problems of low error capture accuracy and long response time of traditional spoken French error correction algorithms, this study designed a French spoken error correction algorithm based on machine learning. Based on the construction of the French spoken pronunciation signal model, the algorithm analyzes the spectral features of French spoken pronunciation and then selects and classifies the features and captures the abnormal pronunciation signals. Based on this, the machine learning network architecture and the training process of the machine learning network are designed, and the operation structure of the algorithm, the algorithm program, the algorithm development environment, and the identification of oral errors are designed to complete the correction of oral French errors. Experimental results show that the proposed algorithm has high error capture accuracy and short response time, which prove its high efficiency and timeliness.

2021 ◽  
Vol 11 ◽  
Harry Subramanian ◽  
Rahul Dey ◽  
Waverly Rose Brim ◽  
Niklas Tillmanns ◽  
Gabriel Cassinelli Petersen ◽  

PurposeMachine learning has been applied to the diagnostic imaging of gliomas to augment classification, prognostication, segmentation, and treatment planning. A systematic literature review was performed to identify how machine learning has been applied to identify gliomas in datasets which include non-glioma images thereby simulating normal clinical practice.Materials and MethodsFour databases were searched by a medical librarian and confirmed by a second librarian for all articles published prior to February 1, 2021: Ovid Embase, Ovid MEDLINE, Cochrane trials (CENTRAL), and Web of Science-Core Collection. The search strategy included both keywords and controlled vocabulary combining the terms for: artificial intelligence, machine learning, deep learning, radiomics, magnetic resonance imaging, glioma, as well as related terms. The review was conducted in stepwise fashion with abstract screening, full text screening, and data extraction. Quality of reporting was assessed using TRIPOD criteria.ResultsA total of 11,727 candidate articles were identified, of which 12 articles were included in the final analysis. Studies investigated the differentiation of normal from abnormal images in datasets which include gliomas (7 articles) and the differentiation of glioma images from non-glioma or normal images (5 articles). Single institution datasets were most common (5 articles) followed by BRATS (3 articles). The median sample size was 280 patients. Algorithm testing strategies consisted of five-fold cross validation (5 articles), and the use of exclusive sets of images within the same dataset for training and for testing (7 articles). Neural networks were the most common type of algorithm (10 articles). The accuracy of algorithms ranged from 0.75 to 1.00 (median 0.96, 10 articles). Quality of reporting assessment utilizing TRIPOD criteria yielded a mean individual TRIPOD ratio of 0.50 (standard deviation 0.14, range 0.37 to 0.85).ConclusionSystematic review investigating the identification of gliomas in datasets which include non-glioma images demonstrated multiple limitations hindering the application of these algorithms to clinical practice. These included limited datasets, a lack of generalizable algorithm training and testing strategies, and poor quality of reporting. The development of more robust and heterogeneous datasets is needed for algorithm development. Future studies would benefit from using external datasets for algorithm testing as well as placing increased attention on quality of reporting standards.Systematic Review, International Prospective Register of Systematic Reviews (PROSPERO 2020 CRD42020209938).

2021 ◽  
pp. 146808742110642
Sree Harsha Rayasam ◽  
Weijin Qiu ◽  
Ted Rimstidt ◽  
Gregory M Shaver ◽  
Daniel G Van Alstine ◽  

Accurate modeling and control of the gas exchange process in a modern turbocharged spark-ignited engine is critical for the control and analysis of different control strategies. This paper develops a simple physics-based, five-state engine model for a large four-stroke spark-ignited turbocharged engine fueled by natural gas that is used in variable speed applications. The control-oriented model is amenable for control algorithm development and includes the impacts of modulation to any combination of four actuators: throttle valve, bypass valve, fuel rate, and wastegate valve. The control problem requires tracking engine speed to provide propulsive power, differential pressure across the throttle valve to prevent compressor surge, air-to-fuel ratio to restrict engine emissions. Two validation strategies, open-loop and closed-loop, are used to validate the accuracy of both nonlinear and linear versions of the control-oriented model. The control models are able to capture the engine dynamics within 5%–10% error at most of the engine operating points. Finally, the relative gain array (RGA) is applied to the linearized engine model to understand the degree of interactions between plant inputs and outputs as a function of frequency for various operating points. Results of the RGA analysis show that the preferred input-output pairing changes depending on the linear plant model as well as frequency. Therefore, a coordinated controller is ideal to tackle the control problem in question.

Sign in / Sign up

Export Citation Format

Share Document