scholarly journals Diagnosis and Analysis of Transabdominal and Intracavitary Ultrasound in Gynecological Acute Abdomen

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Huali Yang ◽  
Renying Wang ◽  
Liangchao Zhao ◽  
Jinhua Ye ◽  
Nengping Li ◽  
...  
Keyword(s):  
Acute Abdomen ◽  
Statistical Data ◽  
Abdominal Ultrasound ◽  
Accuracy Rate ◽  
Acute Pelvic Inflammatory Disease ◽  
Diagnosis Method ◽  
Diagnosis Accuracy ◽  
Effective Diagnosis ◽  
Ultrasound Group ◽  
Corpus Luteum Cyst

In order to explore the effective diagnosis method of gynecological acute abdomen, this paper takes hospital gynecological acute abdomen patients as samples and selects gynecological acute abdomen patients admitted to the hospital to be included in this study. They are divided into transabdominal ultrasound group, intracavitary ultrasound group, and combined group. Moreover, this paper uses mathematical statistics to carry out sample statistics. The statistical data mainly include ectopic pregnancy, torsion of ovarian tumor pedicle, acute suppurative salpingitis, torsion of fallopian tube, hemorrhagic salpingitis, acute pelvic inflammatory disease, rupture of corpus luteum cyst, and diagnosis accuracy rate. In addition, this paper compares the diagnostic accuracy of the abdominal ultrasound group, the intracavitary ultrasound group, and the combined group. The experimental research shows that the combined ultrasound diagnosis method can effectively improve the accuracy of the diagnosis of gynecological acute abdomen.

Research on Electronic Equipment Fault Diagnosis Expert System Based on Embedded Linux

2013 ◽  
Vol 683 ◽  
pp. 837-840
Author(s):  
Jian Hu Zhang ◽  
Lei Lei ◽  
Jia Feng Li ◽  
Xin You Cui ◽  
Yong Wu
Keyword(s):  
Fault Diagnosis ◽  
Expert System ◽  
Embedded Linux ◽  
Electronic Equipment ◽  
Accuracy Rate ◽  
Embedded Linux System ◽  
Knowledge Updating ◽  
Diagnosis Method ◽  
Diagnosis Accuracy ◽  
Circuit Fault Diagnosis

This paper elaborate one circuit fault diagnosis method about electronic equipment circuit detection combined with expert system on ARM9 and embedded Linux platform and design CLIPS expert system using DSP combined with CPLD data acquisition, making full use of DSP high for-speed data processing capability and then passing the data to the Embedded Linux system operation. Expert system implemente a real-time fault diagnosis according to the the predefined fault diagnosis Knowledge. Compared with traditional testing equipment, the expert system has the advantage of knowledge updating conveniently, high fault diagnosis accuracy rate, etc.

scholarly journals Identification of Skin Lesions by Using Single-Step Multiframe Detector

2021 ◽  
Vol 10 (1) ◽  
pp. 144
Author(s):  
Yu-Ping Hsiao ◽  
Chih-Wei Chiu ◽  
Chih-Wei Lu ◽  
Hong Thai Nguyen ◽  
Yu Sheng Tseng ◽  
...  
Keyword(s):  
Ground Truth ◽  
Skin Lesions ◽  
Single Step ◽  
Single Shot ◽  
Accuracy Rate ◽  
Tissue Slices ◽  
Image Detection ◽  
Sensitivity Rate ◽  
Diagnosis Accuracy ◽  
Artificial Intelligence Algorithm

An artificial intelligence algorithm to detect mycosis fungoides (MF), psoriasis (PSO), and atopic dermatitis (AD) is demonstrated. Results showed that 10 s was consumed by the single shot multibox detector (SSD) model to analyze 292 test images, among which 273 images were correctly detected. Verification of ground truth samples of this research come from pathological tissue slices and OCT analysis. The SSD diagnosis accuracy rate was 93%. The sensitivity values of the SSD model in diagnosing the skin lesions according to the symptoms of PSO, AD, MF, and normal were 96%, 80%, 94%, and 95%, and the corresponding precision were 96%, 86%, 98%, and 90%. The highest sensitivity rate was found in MF probably because of the spread of cancer cells in the skin and relatively large lesions of MF. Many differences were found in the accuracy between AD and the other diseases. The collected AD images were all in the elbow or arm and other joints, the area with AD was small, and the features were not obvious. Hence, the proposed SSD could be used to identify the four diseases by using skin image detection, but the diagnosis of AD was relatively poor.

Application of SVM Classifier and Fractal Feature in Circuit Fault Diagnosis

2012 ◽  
Vol 490-495 ◽  
pp. 942-945
Author(s):  
Jing Kui Mao ◽  
Xian Bai Mao
Keyword(s):  
Fault Diagnosis ◽  
Analog Circuits ◽  
Fractal Theory ◽  
Svm Classifier ◽  
Band Pass Filter ◽  
Pass Filter ◽  
Diagnosis Method ◽  
Diagnosis Accuracy ◽  
Band Pass ◽  
Circuit Fault Diagnosis

Combining SVM and fractal theory, a novel fault diagnosis method for analog circuits based on SVM using fractal dimension is developed in this paper. Simulation results of diagnosing the Sallen-Key band pass filter circuit have confirmed that the proposed approach increases the fault diagnosis accuracy, thereby it may be considered as an alternative for the analog fault diagnosis.

scholarly journals Scan-Chain-Fault Diagnosis Using Regressions in Cryptographic Chips for Wireless Sensor Networks

Sensors ◽  
2020 ◽  
Vol 20 (17) ◽  
pp. 4771
Author(s):  
Hyunyul Lim ◽  
Minho Cheong ◽  
Sungho Kang
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Large Scale ◽  
Vlsi Circuits ◽  
Wireless Sensor ◽  
Large Scale Integration ◽  
Diagnosis Method ◽  
Diagnosis Accuracy ◽  
Scan Chain ◽  
Scale Integration

Scan structures, which are widely used in cryptographic circuits for wireless sensor networks applications, are essential for testing very-large-scale integration (VLSI) circuits. Faults in cryptographic circuits can be effectively screened out by improving testability and test coverage using a scan structure. Additionally, scan testing contributes to yield improvement by identifying fault locations. However, faults in circuits cannot be tested when a fault occurs in the scan structure. Moreover, various defects occurring early in the manufacturing process are expressed as faults of scan chains. Therefore, scan-chain diagnosis is crucial. However, it is difficult to obtain a sufficiently high diagnosis resolution and accuracy through the conventional scan-chain diagnosis. Therefore, this article proposes a novel scan-chain diagnosis method using regression and fan-in and fan-out filters that require shorter training and diagnosis times than existing scan-chain diagnoses do. The fan-in and fan-out filters, generated using a circuit logic structure, can highlight important features and remove unnecessary features from raw failure vectors, thereby converting the raw failure vectors to fan-in and fan-out vectors without compromising the diagnosis accuracy. Experimental results confirm that the proposed scan-chain-diagnosis method can efficiently provide higher resolutions and accuracies with shorter training and diagnosis times.

scholarly journals Study on a Fault Identification Method of the Hydraulic Pump Based on a Combination of Voiceprint Characteristics and Extreme Learning Machine

Processes ◽  
2019 ◽  
Vol 7 (12) ◽  
pp. 894 ◽  
Author(s):  
Wanlu Jiang ◽  
Zhenbao Li ◽  
Jingjing Li ◽  
Yong Zhu ◽  
Peiyao Zhang
Keyword(s):  
Fault Diagnosis ◽  
Recognition Accuracy ◽  
Test Sample ◽  
Training Sample ◽  
Accuracy Rate ◽  
Diagnosis Method ◽  
Learning Machine ◽  
Hidden Layer ◽  
Axial Piston ◽  
Sample Set

Aiming at addressing the problem that the faults in axial piston pumps are complex and difficult to effectively diagnose, an axial piston pump fault diagnosis method that is based on the combination of Mel-frequency cepstrum coefficients (MFCC) and the extreme learning machine (ELM) is proposed. Firstly, a sound sensor is used to realize contactless sound signal acquisition of the axial piston pump. The wavelet packet default threshold denoises the original acquired sound signals. Afterwards, windowing and framing are added to the de-noised sound signals. The MFCC voiceprint characteristics of the processed sound signals are extracted. The voiceprint characteristics are divided into a training sample set and test sample set. ELM models with different numbers of neurons in the hidden layers are established for training and testing. The relationship between the number of neurons in the hidden layer and the recognition accuracy rate is obtained. The ELM model with the optimal number of hidden layer neurons is established and trained with the training sample set. The trained ELM model is applied to the test sample set for fault diagnosis. The fault diagnosis results are obtained. The fault diagnosis results of the ELM model are compared with those of the back propagation (BP) neural network and the support vector machine. The results show that the fault diagnosis method that is proposed in this paper has a higher recognition accuracy rate, shorter training and diagnosis times, and better application prospect.

Diagnostic Value of Ultrasound Imaging in Obstetrics and Gynecology Acute Abdomen

2021 ◽  
Vol 11 (2) ◽  
pp. 469-477
Author(s):  
Sicong Li ◽  
Liangzhi Xu
Keyword(s):  
Acute Abdomen ◽  
Ultrasound Imaging ◽  
Diagnostic Value ◽  
Obstetrics And Gynecology ◽  
Imaging Diagnosis ◽  
Clinical Value ◽  
Obstetrics And Gynaecology ◽  
Imaging Characteristics ◽  
Objective Evidence ◽  
Acute Pelvic Inflammatory Disease

Common types of gynecological acute abdomen include ovarian cyst pedicle torsion, ectopic pregnancy, luteal rupture, and acute pelvic inflammatory disease. Make accurate diagnosis and surgical treatment of acute abdomen patients in obstetrics and gynecology in time, otherwise it will cause life danger or loss of organ function, therefore, accurate and timely diagnosis and treatment of gynecological acute abdomen is very important for the prognosis of patients. Ultrasound imaging has important clinical value for the diagnosis of acute abdomen in obstetrics and gynecology. Ultrasound imaging has the advantages of simple examination, rapid reporting, and no pain in the subject, which is one of the best methods for diagnosing acute abdomen in obstetrics and gynecology. This study analysed and summarized the imaging principles of ultrasound imaging in acute obstetrics and gynecology and the imaging characteristics of various acute abdomen. A retrospective analysis of patients with acute obstetrics and gynaecology performed in our hospital from December 2017 to June 2019 was performed. The diagnostic analysis of ultrasound imaging in acute obstetrics and gynaecology was performed. The results showed that the ultrasound imaging diagnosis results of 202 obstetric and gynecological acute abdomen patients were compared with the results of surgery and pathological diagnosis. Among the 182 cases with correct ultrasound imaging diagnosis, the coincidence rate was 90.1%, and 20 cases were misdiagnosed, accounting for 9.9%. The research of this study shows that the ultrasound examination technique for patients with acute obstetrics and gynaecology is simple, fast, non-invasive, and has high accuracy. Ultrasound imaging can provide reliable objective evidence for the diagnosis and differential diagnosis of most acute abdominal diseases, in order to improve the diagnosis rate and reduce the misdiagnosis rate.

Full Information Fusion and its Application in Fault Diagnosis for Rotary Machinery

2011 ◽  
Vol 66-68 ◽  
pp. 1315-1319 ◽  
Author(s):  
Xin Min Dong ◽  
Jie Han ◽  
Wang Shen Hao
Keyword(s):  
Fault Diagnosis ◽  
Information Fusion ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Engineering Practice ◽  
Full Information ◽  
Single Section ◽  
Rotary Machinery ◽  
Diagnosis Method ◽  
Diagnosis Accuracy

The rotor motion and the information fusion of single section were discussed; the fault diagnosis method for rotary machinery based on the full information fusion of two sections was put forward, and the back propagation neural network model was established. Engineering practice indicated that the fault diagnosis accuracy based on the information fusion of two sections was higher than that based on the information fusion of single section.

scholarly journals End-to-end diagnosis of cloud systems against intermittent faults

2020 ◽  
pp. 40-40
Author(s):  
Chao Wang ◽  
Zhongchuan Fu ◽  
Yanyan Huo
Keyword(s):  
Cloud Computing ◽  
Fault Diagnosis ◽  
Computing Environment ◽  
Bp Network ◽  
Cloud Computing Environment ◽  
Intermittent Faults ◽  
Diagnosis Method ◽  
Diagnosis Accuracy ◽  
End To End ◽  
Detection Mechanisms

The diagnosis of intermittent faults is challenging because of their random manifestation due to intricate mechanisms. Conventional diagnosis methods are no longer effective for these faults, especially for hierachical environment, such as cloud computing. This paper proposes a fault diagnosis method that can effectively identify and locate intermittent faults originating from (but not limited to) processors in the cloud computing environment. The method is end-to-end in that it does not rely on artificial feature extraction for applied scenarios, making it more generalizable than conventional neural network-based methods. It can be implemented with no additional fault detection mechanisms, and is realized by software with almost zero hardware cost. The proposed method shows a higher fault diagnosis accuracy than BP network, reaching 97.98% with low latency.

scholarly journals Multidetector computerized tomography in acute abdomen

2019 ◽  
Vol 6 (7) ◽  
pp. 2353
Author(s):  
Suhail Rafiq ◽  
Inayat Ellahi ◽  
Shafqat Shabir ◽  
Sheikh Shahnawaz
Keyword(s):  
Abdominal Pain ◽  
Acute Abdomen ◽  
Correct Diagnosis ◽  
High Sensitivity ◽  
Morbidity Rate ◽  
Accuracy Rate ◽  
Mortality And Morbidity ◽  
Pain Abdomen ◽  
Common Causes ◽  
Sensitivity Specificity

Background: Acute abdominal pain is a common chief complaint in patients examined reporting to emergency department. The sensitivity of CT is 96% in acute abdomen. In order to decrease the mortality and morbidity rate, an efficient and correct diagnosis should be given for these patients. When investigations, like USG examinations are inconclusive, in such cases, multi-detector computer tomography is a widely accepted primary investigation of choice in patients coming with intense abdominal pain. The aim of the study was to evaluate the accuracy of MDCT in diagnosis of acute abdomen; document the sensitivity and specificity of MDCT; the incidence of different pathologies presenting as acute abdomen.Methods: Prospective study on 64 subjects with acute pain abdomen was subjected to MDCT in GMC Srinagar. The duration of this study was from January to May 2019.Results: About 36 patients were females and 28 were males. Youngest patient had an age of 7 years to eldest patient having age of 79 years. Most common causes of acute abdomen were acute pancreatitis in 21.8, acute appendicitis in 15.6% and bowel obstruction in12.5%. In our study the sensitivity, specificity and positive and negative predictable values of MDCT were 95.0%, 75%%, 98.3% and 60% respectively.Conclusions: We conclude that MDCT has high sensitivity and accuracy rate. In inconclusive cases, MDCT is recommended to arrive at a definitive diagnosis. The results obtained in the study were comparable to pioneer studies conducted worldwide.

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