• Asian Pacific Journal of Cancer Prevention
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• v.16 no.6
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• pp.2361-2368
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• 2015

Objective: To evaluate the diagnostic accuracy of contrast-enhanced ultrasonography (CEUS) in differentiating between benign and malignant enlarged lymph nodes using meta-analysis. Materials and Methods: Pubmed, Embase, SCI and Cochrane databases were searched for studies (up to September 1, 2014) reporting the diagnostic performance of CEUS in discriminating between benign and malignant lymph nodes. Inclusion criteria were: prospective study; histopathology as the reference standard; and sufficient data to construct $2{\times}2$ contingency tables. Methodological quality was assessed using Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2). Patient clinical characteristics, sensitivity and specificity were extracted. The summary receiver operating characteristic curve was used to examine the accuracy of CEUS. A meta-analysis was performed to evaluate the clinical utility in identification of benign and malignant lymph nodes. Sensitivity analysis was performed after omitting outliers identified in a bivariate boxplot and publication bias was assessed with Egger testing. Results: The pooled sensitivity, specificity and AUROC were 0.92 (95%CI, 0.85-0.96), 0.91 (95%CI, 0.82-0.95) and 0.97 (95%CI, 0.95-0.98), respectively. After omitting 3 outlier studies, heterogeneity decreased. Sensitivity analysis demonstrated no disproportionate influences of individual studies. Publication bias was not significant. Conclusions: CEUS is a promising diagnostic modality in differentiating between benign and malignant lymph nodes and can potentially reduce unnecessary fine-needle aspiration biopsies of benign nodes.

• Journal of Astronomy and Space Sciences
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• v.19 no.2
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• pp.163-172
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• 2002

This article investigates the problem of data preprocessing for the precision orbit determination (POD) of low earth orbit satellite using GPS .aw data. Several data preprocessing algorithms have been developed to edit the GPS data automatically such that outlier deletion, cycle slip identification and correction, and time tag error correction. The GPS data are precisely edited for the accuracy of POD. Some methods of data preprocessing are restricted to the rate of the collections of the pseudorange and carrier phase measurements. This study considers the preprocessing efficiency varied with the rate, the quality of receiver and the altitude of the satellite's orbit. We also propose the proper methods in accordance with the rate for single frequency and dual frequency receivers.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.47 no.6
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• pp.33-42
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• 2010

This research propose a practical guidance system considering ocean currents in real sea operation. Optimality of generated path is not an issue in this paper. Way-points from start point to possible goal positions are selected by experienced human supervisors considering major ocean current axis. This paper also describes the implementation of a precise underwater navigation solution using multi-sensor fusion technique based on USBL, GPS, DVL and AHRS measurements in detail. To implement the precise, accurate and frequent underwater navigation solution, three strategies are chosen. The first one is the heading alignment angle identification to enhance the performance of standalone dead-reckoning algorithm. The second one is that absolute position is fused timely to prevent accumulation of integration error, where the absolute position can be selected between USBL and GPS considering sensor status. The third one is introduction of effective outlier rejection algorithm. The performance of the developed algorithm is verified with experimental data of mine disposal vehicle and deep-sea ROV.

• Smart Structures and Systems
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• v.29 no.1
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• pp.77-91
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• 2022

Various monitoring systems have been implemented in civil infrastructure to ensure structural safety and integrity. In long-term monitoring, these systems generate a large amount of data, where anomalies are not unusual and can pose unique challenges for structural health monitoring applications, such as system identification and damage detection. Therefore, developing efficient techniques is quite essential to recognize the anomalies in monitoring data. In this study, several machine learning techniques are explored and implemented to detect and classify various types of data anomalies. A field dataset, which consists of one month long acceleration data obtained from a long-span cable-stayed bridge in China, is employed to examine the machine learning techniques for automated data anomaly detection. These techniques include the statistic-based pattern recognition network, spectrogram-based convolutional neural network, image-based time history convolutional neural network, image-based time-frequency hybrid convolution neural network (GoogLeNet), and proposed ensemble neural network model. The ensemble model deliberately combines different machine learning models to enhance anomaly classification performance. The results show that all these techniques can successfully detect and classify six types of data anomalies (i.e., missing, minor, outlier, square, trend, drift). Moreover, both image-based time history convolutional neural network and GoogLeNet are further investigated for the capability of autonomous online anomaly classification and found to effectively classify anomalies with decent performance. As seen in comparison with accuracy, the proposed ensemble neural network model outperforms the other three machine learning techniques. This study also evaluates the proposed ensemble neural network model to a blind test dataset. As found in the results, this ensemble model is effective for data anomaly detection and applicable for the signal characteristics changing over time.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.57-73
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• 2021

Maintenance and prevention of failure through anomaly detection of ICT infrastructure is becoming important. System monitoring data is multidimensional time series data. When we deal with multidimensional time series data, we have difficulty in considering both characteristics of multidimensional data and characteristics of time series data. When dealing with multidimensional data, correlation between variables should be considered. Existing methods such as probability and linear base, distance base, etc. are degraded due to limitations called the curse of dimensions. In addition, time series data is preprocessed by applying sliding window technique and time series decomposition for self-correlation analysis. These techniques are the cause of increasing the dimension of data, so it is necessary to supplement them. The anomaly detection field is an old research field, and statistical methods and regression analysis were used in the early days. Currently, there are active studies to apply machine learning and artificial neural network technology to this field. Statistically based methods are difficult to apply when data is non-homogeneous, and do not detect local outliers well. The regression analysis method compares the predictive value and the actual value after learning the regression formula based on the parametric statistics and it detects abnormality. Anomaly detection using regression analysis has the disadvantage that the performance is lowered when the model is not solid and the noise or outliers of the data are included. There is a restriction that learning data with noise or outliers should be used. The autoencoder using artificial neural networks is learned to output as similar as possible to input data. It has many advantages compared to existing probability and linear model, cluster analysis, and map learning. It can be applied to data that does not satisfy probability distribution or linear assumption. In addition, it is possible to learn non-mapping without label data for teaching. However, there is a limitation of local outlier identification of multidimensional data in anomaly detection, and there is a problem that the dimension of data is greatly increased due to the characteristics of time series data. In this study, we propose a CMAE (Conditional Multimodal Autoencoder) that enhances the performance of anomaly detection by considering local outliers and time series characteristics. First, we applied Multimodal Autoencoder (MAE) to improve the limitations of local outlier identification of multidimensional data. Multimodals are commonly used to learn different types of inputs, such as voice and image. The different modal shares the bottleneck effect of Autoencoder and it learns correlation. In addition, CAE (Conditional Autoencoder) was used to learn the characteristics of time series data effectively without increasing the dimension of data. In general, conditional input mainly uses category variables, but in this study, time was used as a condition to learn periodicity. The CMAE model proposed in this paper was verified by comparing with the Unimodal Autoencoder (UAE) and Multi-modal Autoencoder (MAE). The restoration performance of Autoencoder for 41 variables was confirmed in the proposed model and the comparison model. The restoration performance is different by variables, and the restoration is normally well operated because the loss value is small for Memory, Disk, and Network modals in all three Autoencoder models. The process modal did not show a significant difference in all three models, and the CPU modal showed excellent performance in CMAE. ROC curve was prepared for the evaluation of anomaly detection performance in the proposed model and the comparison model, and AUC, accuracy, precision, recall, and F1-score were compared. In all indicators, the performance was shown in the order of CMAE, MAE, and AE. Especially, the reproduction rate was 0.9828 for CMAE, which can be confirmed to detect almost most of the abnormalities. The accuracy of the model was also improved and 87.12%, and the F1-score was 0.8883, which is considered to be suitable for anomaly detection. In practical aspect, the proposed model has an additional advantage in addition to performance improvement. The use of techniques such as time series decomposition and sliding windows has the disadvantage of managing unnecessary procedures; and their dimensional increase can cause a decrease in the computational speed in inference.The proposed model has characteristics that are easy to apply to practical tasks such as inference speed and model management.

• Asian-Australasian Journal of Animal Sciences
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• v.30 no.7
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• pp.912-919
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• 2017

Objective: Identification of the candidate genes that play key roles in phenotypic variations can provide new information about evolution and positive selection. Interleukin (IL)-32 is involved in many biological processes, however, its role for the immune response against various diseases in mammals is poorly understood. Therefore, the current investigation was performed for the better understanding of the molecular evolution and the positive selection of single nucleotide polymorphisms in IL-32 gene. Methods: By using fixation index ($F_{ST}$) based method, IL-32 (9375) gene was found to be outlier and under significant positive selection with the provisional combined allocation of mean heterozygosity and $F_{ST}$. Using nucleotide sequences of 11 mammalian species from National Center for Biotechnology Information database, the evolutionary selection of IL-32 gene was determined using Maximum likelihood model method, through four models (M1a, M2a, M7, and M8) in Codeml program of phylogenetic analysis by maximum liklihood. Results: IL-32 is detected under positive selection using the $F_{ST}$ simulations method. The phylogenetic tree revealed that goat IL-32 was in close resemblance with sheep IL-32. The coding nucleotide sequences were compared among 11 species and it was found that the goat IL-32 gene shared identity with sheep (96.54%), bison (91.97%), camel (58.39%), cat (56.59%), buffalo (56.50%), human (56.13%), dog (50.97%), horse (54.04%), and rabbit (53.41%) respectively. Conclusion: This study provides evidence for IL-32 gene as under significant positive selection in goat.