• Asian Pacific Journal of Cancer Prevention
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• v.14 no.5
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• pp.2835-2838
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• 2013

Background: Prostate cancer features a substantial incidence and mortality burden, similarly to breast cancer, and it ranks among the top ten specific causes of death in males. Objective: To explore the situation of prostate cancer in a healthy population cohort in Eastern Nepal. Materials and Methods: This study was conducted in the Department of General Surgery at B. P. Koirala Institute of Health Sciences, Dharan, Nepal from July 2010 to June 2011. Males above 50 years visiting the Surgical Outpatient Department in BPKIHS were enrolled in the study and screening camps were organized in four Teaching District Hospitals of BPKIHS, all in Eastern Nepal. Digital rectal examination (DRE) was conducted by trained professionals after collecting blood for assessment of serum prostatic specific antigen (PSA). Trucut biopsies were performed for all individuals with abnormal PSA/DRE findings. Results: A total of 1,521 males more than 50 years of age were assessed and screened after meeting the inclusion criteria. The vast majority of individuals, 1,452 (96.2%), had PSA ${\leq}4.0$ ng/ml. Abnormal PSA (>4 ng/ml) was found in 58 (3.8%). Abnormal DRE was found in 26 (1.72%). DRE and PSA were both abnormal in 26 (1.72%) individuals. On the basis of raised PSA or abnormal DRE 58 (3.84%) individuals were subjected to digitally guided trucut biopsy. Biopsy report revealed benign prostatic hyperplasia in 47 (3.11%) and adenocarcinoma prostate in 11 (0.73%). The specificity of DRE was 66.0%with a sensitivity of 90.9% and a positive predictive value of 38.5%. The sensitivity of PSA more than 4ng/ml in detecting carcinoma prostate was 100% and the positive predictive value for serum PSA was 19.0% Conclusions: The overall cancer detection rate in this study was 0.73% and those detected were locally advanced. Larger community-based studies are highly warranted specially among high-risk groups.

• Journal of Yeungnam Medical Science
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• v.2 no.1
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• pp.77-80
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• 1985

The R-R interval analyzer was developed to measure the autonomic nervous system function using microcomputer. The system based on 8 bit microcomputer including bandpass filter, R-wave detector and clock generator in order to obtain the mean value, standard deviation, total time, CV value, maximum value and minimum value in the specific view point of R-R interval variation. The pattern of R-R interval change after resting, voluntary standing and deep breathing can be analysed in normal subjects and diabetics with autonomic nervous dysfunction. The amplitude of the R-R interval variation showed sensitive pattern for normal subjects at resting, standing and deep breathing. On the contrary, the periodicities of amplitude for abnormal subjects with autonomic nervous dysfunction showed dull pattern. It was suggested that R-R interval analyzer is a good detection method for dysfunction of autonomic nervous system.

• IEMEK Journal of Embedded Systems and Applications
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• v.18 no.3
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• pp.117-124
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• 2023

In the machine vision inspection equipment, diameter measurement is important process in inspection of cylindrical object. However, machine vision inspection equipment requires complex algorithm processing such as camera distortion correction and perspective distortion correction, and the increase in processing time and cost required for precise diameter measurement. In this paper, we proposed the algorithm for diameter measurement of cylindrical object using the laser displacement sensor. In order to fit circle for given four input outer points, grid search algorithms using root-mean-square error and mean-absolute error are applied and compared. To solve the limitations of the grid search algorithm, we finally apply the singular-value-decomposition based circle fitting algorithm. In order to compare the performance of the algorithms, we generated the pseudo data of the outer points of the cylindrical object and applied each algorithm. As a result of the experiment, the grid search using root-mean-square error confirmed stable measurement results, but it was confirmed that real-time processing was difficult as the execution time was 10.8059 second. The execution time of mean-absolute error algorithm was greatly improved as 0.3639 second, but there was no weight according to the distance, so the result of algorithm is abnormal. On the other hand, the singular-value-decomposition method was not affected by the grid and could not only obtain precise detection results, but also confirmed a very good execution time of 0.6 millisecond.

• Asian Pacific Journal of Cancer Prevention
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• v.14 no.5
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• pp.2979-2982
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• 2013

Cervical cancer is a commonly-encountered malignant tumor in women. Cervical screening is particularly important due to early symptoms being deficient in specificity. The main purpose of the study is to assess the application value of cervical thinprep cytologic test (TCT) and human papillomavirus (HPV) detection in screening for cervical cancer and precancerous lesions. In the study, cervical TCT and HPV detection were simultaneously performed on 12,500 patients selected in a gynecological clinic. Three hundred patients with positive results demonstrated by cervical TCT and/or HPV detection underwent cervical tissue biopsy under colposcopy, and pathological results were considered as the gold standard. The results revealed that 200 out of 12,500 patients were abnormal by TCT, in which 30 cases pertained to equivocal atypical squamous cells (ASCUS), 80 cases to low squamous intraepithelial lesion (LSIL), 70 cases to high squamous intraepithelial lesion (HSIL) and 20 cases to squamous cell carcinoma (SCC). With increasing pathological grade of cervical biopsy, however, TCT positive rates did not rise. Two hundred and eighty out of 12,500 patients were detected as positive for HPV infection, in which 50 cases were chronic cervicitis and squamous metaplasia, 70 cases cervical intraepithelial neoplasia (CIN) I, 60 cases CIN II, 70 cases CIN III and 30 cases invasive cervical carcinoma. Two hundred and thirty patients with high-risk HPV infection were detected. With increase in pathological grade, the positive rate of high-risk HPV also rose. The detection rates of HPV detection to CIN III and invasive cervical carcinoma as well as the total detection rate of lesions were significantly higher than that of TCT. Hence, HPV detection is a better method for screening of cervical cancer at present.

• Smart Structures and Systems
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• v.29 no.6
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• pp.757-766
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• 2022

To train deep learning algorithms, a sufficient number of data are required. However, in most engineering systems, the acquisition of fault data is difficult or sometimes not feasible, while normal data are secured. The dearth of data is one of the major challenges to developing deep learning models, and fault diagnosis in particular cannot be made in the absence of fault data. With this context, this paper proposes an anomaly detection methodology for rotating machines using only normal data with self-labeling. Since only normal data are used for anomaly detection, a self-labeling method is used to generate a new labeled dataset. The overall procedure includes the following three steps: (1) transformation of normal data to self-labeled data based on a pretext task, (2) training the convolutional neural networks (CNN), and (3) anomaly detection using defined anomaly score based on the softmax output of the trained CNN. The softmax value of the abnormal sample shows different behavior from the normal softmax values. To verify the proposed method, four case studies were conducted, on the Case Western Reserve University (CWRU) bearing dataset, IEEE PHM 2012 data challenge dataset, PHMAP 2021 data challenge dataset, and laboratory bearing testbed; and the results were compared to those of existing machine learning and deep learning methods. The results showed that the proposed algorithm could detect faults in the bearing testbed and compressor with over 99.7% accuracy. In particular, it was possible to detect not only bearing faults but also structural faults such as unbalance and belt looseness with very high accuracy. Compared with the existing GAN, the autoencoder-based anomaly detection algorithm, the proposed method showed high anomaly detection performance.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.12
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• pp.1786-1793
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• 2022

Cyber-attacks such as smishing and hacking mail exploiting COVID-19, political and social issues, have recently been continuous. Machine learning and deep learning technology research are conducted to prevent any damage due to cyber-attacks inducing malicious links to breach personal data. It has been concluded as a lack of basis to judge the attacks to be malicious in previous studies since the features of data set were excessively simple. In this paper, nine main features of three types, "URL Days", "URL Word", and "URL Abnormal", were proposed in addition to lexical features of URL which have been reflected in previous research. F1-Score and accuracy index were measured through four different types of machine learning algorithms. An improvement of 0.9% in a result and the highest value, 98.5%, were examined in F1-Score and accuracy through comparatively analyzing an existing research. These outcomes proved the main features contribute to elevating the values in both accuracy and performance.

• Journal of the Korean Data and Information Science Society
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• v.20 no.2
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• pp.387-399
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• 2009

Mobile wireless networks continue to be plagued by theft of identify and intrusion. Both problems can be addressed in two different ways, either by misuse detection or anomaly-based detection. In this paper, we propose a dissimilarity-based anomaly detection method which can effectively identify abnormal behavior such as mobility patterns of mobile wireless networks. In the proposed algorithm, a normal profile is constructed from normal mobility patterns of mobile nodes in mobile wireless networks. From the constructed normal profile, a dissimilarity is computed by a weighted dissimilarity measure. If the value of the weighted dissimilarity measure is greater than the dissimilarity threshold that is a system parameter, an alert message is occurred. The performance of the proposed method is evaluated through a simulation. From the result of the simulation, we know that the proposed method is superior to the performance of other anomaly detection methods using dissimilarity measures.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.10
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• pp.3989-4006
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• 2020

The intelligent agriculture monitoring is based on the perception and analysis of environmental data, which enables the monitoring of the production environment and the control of environmental regulation equipment. As the scale of the application continues to expand, a large amount of data will be generated from the perception layer and uploaded to the cloud service, which will bring challenges of insufficient bandwidth and processing capacity. A fog-based offline and real-time hybrid data analysis architecture was proposed in this paper, which combines offline and real-time analysis to enable real-time data processing on resource-constrained IoT devices. Furthermore, we propose a data process-ing algorithm based on the incremental principal component analysis, which can achieve data dimensionality reduction and update of principal components. We also introduce the concept of Squared Prediction Error (SPE) value and realize the abnormal detection of data through the combination of SPE value and data fusion algorithm. To ensure the accuracy and effectiveness of the algorithm, we design a regular-SPE hybrid model update strategy, which enables the principal component to be updated on demand when data anomalies are found. In addition, this strategy can significantly reduce resource consumption growth due to the data analysis architectures. Practical datasets-based simulations have confirmed that the proposed algorithm can perform data fusion and exception processing in real-time on resource-constrained devices; Our model update strategy can reduce the overall system resource consumption while ensuring the accuracy of the algorithm.

• IEMEK Journal of Embedded Systems and Applications
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• v.10 no.5
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• pp.297-305
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• 2015

AFLS (Adaptive front lighting System) is being applied to improve safety in driving automotive at night. Safe embedded system for controlling head-lamp has to be tightly designed by considering safety requirement of hardware-dependent software, which is embedded in automotive ECU(Electronic Control Unit) hardware under severe environmental noise. In this paper, we propose an adaptive headlight controller with newly-designed symmetric angle sensor compensator, which is integrated with ECU-based adaptive front light system. The proposed system, on which additional backup hardware and emergency control algorithm are integrated, effectively detects abnormal situation and restore safe status of controlling the light-angle in AFLS operations by comparing result in symmetric angle sensor. The controlled angle value is traced into internal memory in runtime and will be continuously compared with the pre-defined lookup table (LUT) with symmetric angle value, which is used in normal operation. The watch-dog concept, which is based on using angle sensor and control-value tracer, enables quick response to restore safe light-controlling state by performing the backup sequence in emergency situation.

• Journal of the Korean Institute of Intelligent Systems
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• v.26 no.3
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• pp.246-252
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• 2016

Since thermal power plant (TPP) equipment is operated under very high pressure and temperature, failures of the equipment give rise to severe losses of life and property. To prevent the losses, fault detection method is, therefore, absolutely necessary to identify abnormal operating conditions of the equipment in advance. In this paper, we present Mahalanobis distance (MD) based fault detection method for steam boiler tube in TPP. In the MD-based method, it is supposed that abnormal data samples are far away from normal samples. Using multivariate samples collected from normal target system, mean vector and covariance matrix are calculated and threshold value of MD is decided. In a test phase, after calculating the MDs between the mean vector and test samples, alarm signals occur if the MDs exceed the predefined threshold. To demonstrate the performance, a failure case due to boiler tube leakage in 200MW TPP is employed. The experimental results show that the presented method can perform early detection of boiler tube leakage successfully.