Novel Method For Automatic Identification Research Articles

A Novel Method to Identify Radial Drainage Based on Morphological Features

The automatic detection and accurate characterization of drainage patterns are of primary importance for interpreting the regional geologic origin and the features of the regional geological structure. However, there are few studies regarding the identification of cross-basin drainage patterns. In addition, the automatic level of the current approaches needs to be further improved. As a typical type of cross-basin drainage pattern, radial drainage (RD) has unique spatial morphological features: diverging from the center to the surrounding area, developing in adjacent sections of multiple basins, and having a number of source nodes that is not smaller than the number of its outlet nodes. Based on these morphological features, this study aims to develop a novel method for automatic identification of RD using a feature-matching algorithm. The experiment in Mount Lu demonstrates that the proposed method was efficient in RD identification. In this study area, the RDs were identified, and there were no false or missed judgments, which was verified through experts. The proposed method not only helps to detect cross-basin drainage patterns, RD, and centripetal drainage but also has a unique advantage in identifying geographical scenes with complex spatial structures.

Identification of Flying Insects in the Spatial, Spectral, and Time Domains with Focus on Mosquito Imaging.

Insects constitute a very important part of the global ecosystem and include pollinators, disease vectors, and agricultural pests, all with pivotal influence on society. Monitoring and control of such insects has high priority, and automatic systems are highly desirable. While capture and analysis by biologists constitute the gold standard in insect identification, optical and laser techniques have the potential for high-speed detection and automatic identification based on shape, spectroscopic properties such as reflectance and fluorescence, as well as wing-beat frequency analysis. The present paper discusses these approaches, and in particular presents a novel method for automatic identification of mosquitos based on image analysis, as the insects enter a trap based on a combination of chemical and suction attraction. Details of the analysis procedure are presented, and selectivity is discussed. An accuracy of 93% is achieved by our proposed method from a data set containing 122 insect images (mosquitoes and bees). As a powerful and cost-effective method, we finally propose the combination of imaging and wing-beat frequency analysis in an integrated instrument.

A novel method for automatic identification of rock fracture signals in microseismic monitoring

Microseismic (MS) monitoring technology is able to offer 3-dimensional and real-time rupture characteristics of rocks by capturing and analyzing detectable elastic waves released from rock fracture, which has been widely applied in rock/mining engineering. The prerequisite of MS analysis is to identify MS signals with high efficiency and accuracy. However, the identification work is inevitably disturbed by vibration signals from the blasting activities involved in rock/mining engineering. Currently, the classification work of MS and blasting signals is mainly carried out by the on-site operators based on their own experience, which is time-consuming and subjectively sensitive. In this work, a new discriminant method is developed to automatically recognize these signals based on their time-frequency spectrum characteristics to offer more reliable data for MS monitoring analysis. Singular value decomposition is adopted to reduce data volume and obtain unique features. Random Forest (RF) is applied to achieve automatic recognition and get the most important features based on the variation of ‘out-of-bag’ error and Gini Index. These most important features can be used to optimize the RF classifier. The numerical experiments are conducted to verify our new method. The results prove that the method reaches 95.99%±0.1% accuracy in 100 repetitions for events collected from the field, and the optimized RF produces a more accurate result (97%) using fewer characteristics, which is higher than the methods reported in the previous literature. The proposed method diminishes the sensitivity of operators in the classification and promotes the accuracy and efficiency of the identification of MS signals data in the application of MS monitoring technology. Moreover, this strategy also offers a new strategy to process the acquired monitoring signals in other monitoring techniques.

A novel method for automatic identification of fovea location and its centre in colour retinal fundus images

A novel method for automatic identification of fovea location and its centre in colour retinal fundus images

A novel method for automatic identification of fovea location and its centre in colour retinal fundus images

The identification of fovea region plays a significant role in the detection of diabetic maculopathy. The presence of exudates within 500 micrometre from fovea centre requires laser treatment as it is sign of sight threatening. An ophthalmologist can decide over the nature of the treatment depending upon the position of the exudates from the fovea. Hence it is very important to identify fovea and its centre with high accuracy. In this work, we have proposed a novel algorithm for identification of fovea location and its centre in colour retinal fundus images. The novelty of this work aims at an approach where fovea location and its centre identification are done excluding blood vessels and OD. Initially, the grid is drawn over the retinal image, then the designed algorithm searches for region of interest (ROI) for the candidate fovea region by considering the four coordinate points. Finally, by using dark intensity property of fovea and morphological operations, fovea location and its centre is identified. The proposed method is simple, robust and it is tested on a publicly available MESSIDOR database and an accuracy of 97.37% is achieved.

Automatic identification of modal parameters for structures based on an uncertainty diagram and a convolutional neural network

A novel method for automatic identification of structural modal parameters is proposed, based on new developments in both uncertainty quantification for stochastic subspace identification and deep learning. An uncertainty diagram is first constructed to visualize uncertainty estimates, for clearly distinguishing spurious modes. Because the uncertainty of spurious modes is significantly larger than that of the real ones, a convolutional neural network (CNN) is adopted to automatically analyse the uncertainty diagram and efficiently determine the physical structural modes. The method is then applied to identify modal parameters for a six-degree-of-freedom spring–mass model, the Heritage Court Tower building in Canada, and the Ting Kau Bridge in Hong Kong. Results indicate for all three structures that the constructed CNN is effective for analysing the uncertainty diagram and can automatically and accurately obtain the real modes.

Identification of epileptic seizures in EEG signals using time-scale decomposition (ITD), discrete wavelet transform (DWT), phase space reconstruction (PSR) and neural networks

Traditionally, detection of epileptic seizures based on the visual inspection of neurologists is tedious, laborious and subjective. To overcome such disadvantages, numerous seizure detection techniques involving signal processing and machine learning tools have been developed. However, there still remain the problems of automatic detection with high efficiency and accuracy in distinguishing normal, interictal and ictal electroencephalogram (EEG) signals. In this study we propose a novel method for automatic identification of epileptic seizures in singe-channel EEG signals based upon time-scale decomposition (ITD), discrete wavelet transform (DWT), phase space reconstruction (PSR) and neural networks. First, EEG signals are decomposed into a series of proper rotation components (PRCs) and a baseline signal by using the ITD method. The first two PRCs of the EEG signals are extracted, which contain most of the EEG signals’ energy and are considered to be the predominant PRCs. Second, four levels DWT is employed to decompose the predominant PRCs into different frequency bands, in which third-order Daubechies (db3) wavelet function is selected for analysis. Third, phase space of the PRCs is reconstructed based on db3, in which the properties associated with the nonlinear EEG system dynamics are preserved. Three-dimensional (3D) PSR together with Euclidean distance (ED) has been utilized to derive features, which demonstrate significant difference in EEG system dynamics between normal, interictal and ictal EEG signals. Fourth, neural networks are then used to model, identify and classify EEG system dynamics between normal (healthy), interictal and ictal EEG signals. Finally, experiments are carried out on the University of Bonn’s widely used and publicly available epilepsy dataset to assess the effectiveness of the proposed method. By using the 10-fold cross-validation style, the achieved average classification accuracy for eleven cases is reported to be 98.15%. Compared with many state-of-the-art methods, the results demonstrate superior performance and the proposed method can serve as a potential candidate for the automatic detection of seizure EEG signals in the clinical application.

A Novel Method for Automatic Identification of Respiratory Disease from Acoustic Recordings.

This paper evaluates the use of breath sound recordings to automatically determine the respiratory health status of a subject. A number of features were investigated and Wilcoxon Rank Sum statistical test was used to determine the significance of the extracted features. The significant features were then passed to a feature selection algorithm based on mutual information, to determine the combination of features that provided minimal redundancy and maximum relevance. The algorithm was tested on a publicly accessible respiratory sounds database. With the testing dataset, the trained classifier achieved accuracy of 87.1%, sensitivity of 86.8% and specificity of 93.6%. These are promising results showing the possibility of determining the presence or absence of respiratory disease using breath sounds recordings.

Classification of Electronic Devices and Software Processes via Unintentional Electronic Emissions With Neural Decoding Algorithms

Electronic and electromechanical devices continuously emit electromagnetic (EM) signals while in use. These EM emissions (EMEs) contain unique spectral characteristics that can be leveraged for a variety of purposes, including identification of the device's unique EM “fingerprint,” and characterization of software processes running on the device. In this study, we implemented a novel method for automatic identification and characterization of these EMEs inspired by a classification/decoding scheme used to extract neural correlates of brain state from magnetoencephalographic data. Utilizing a sparse bilinear formulation of logistic regression as our “neural” decoder, we extracted device- and software-specific spectrospatial patterns from five identical Arduino Uno prototyping boards as they cycled through five program states. In the device fingerprinting task, we were able to discriminate all five boards from each other with near-perfect accuracy using this method. For software characterization, within a single device, we detected all five programs with 90% accuracy, and across devices, we were able to identify 3/5 programs with 99% accuracy, and achieving 77% accuracy for the other two. Overall, this neural-decoding approach performed well in all scenarios tested, and the corresponding EME “maps” it provides quantify the differences between device and software-specific EMEs in an easily interpretable way.

A Novel Method for Automatic Identification of Breathing State

Sputum deposition blocks the airways of patients and leads to blood oxygen desaturation. Medical staff must periodically check the breathing state of intubated patients. This process increases staff workload. In this paper, we describe a system designed to acquire respiratory sounds from intubated subjects, extract the audio features, and classify these sounds to detect the presence of sputum. Our method uses 13 features extracted from the time-frequency spectrum of the respiratory sounds. To test our system, 220 respiratory sound samples were collected. Half of the samples were collected from patients with sputum present, and the remainder were collected from patients with no sputum present. Testing was performed based on ten-fold cross-validation. In the ten-fold cross-validation experiment, the logistic classifier identified breath sounds with sputum present with a sensitivity of 93.36% and a specificity of 93.36%. The feature extraction and classification methods are useful and reliable for sputum detection. This approach differs from waveform research and can provide a better visualization of sputum conditions. The proposed system can be used in the ICU to inform medical staff when sputum is present in a patient’s trachea.

Convolutional Neural Network for the Detection of End-Diastole and End-Systole Frames in Free-Breathing Cardiac Magnetic Resonance Imaging.

Free-breathing cardiac magnetic resonance (CMR) imaging has short examination time with high reproducibility. Detection of the end-diastole and the end-systole frames of the free-breathing cardiac magnetic resonance, supplemented by visual identification, is time consuming and laborious. We propose a novel method for automatic identification of both the end-diastole and the end-systole frames, in the free-breathing CMR imaging. The proposed technique utilizes the convolutional neural network to locate the left ventricle and to obtain the end-diastole and the end-systole frames from the respiratory motion signal. The proposed procedure works successfully on our free-breathing CMR data, and the results demonstrate a high degree of accuracy and stability. Convolutional neural network improves the postprocessing efficiency greatly and facilitates the clinical application of the free-breathing CMR imaging.

Automatic identification of transmission sections based on complex network theory

Taking transmission sections as the monitoring objects of power system security and stability level can largely improve the efficiency of analysis and control in power system operation. However, existing approaches for identifying transmission sections mainly depend on years of experience, which is not suitable for complicated and variable power systems with large scales. Thus, a novel method for automatic identification of transmission sections using complex network theory is proposed. The proposed method presents the fundamental conditions of transmission sections and identifies them from three levels: transmission lines, key transmission links and partition sections. First, based on the small-world characteristics of power grid, the index of transmission betweenness is presented to identify key transmission links from transmission lines. Then clustering algorithm of complex network is used to divide the power grid and to obtain partition sections from the key transmission links. Finally, the combinations of partition sections are selected and ranked as the transmission sections. Numerical results with CEPRI-36 system and a provincial system are provided to demonstrate the effectiveness and adaptability of the proposed method.

A Novel Method for Automatic Identification of Motion Artifact Beats in ECG Recordings

This paper presents a novel method for automatic identification of motion artifact beats in ECG recordings. The proposed method is based on the ECG complexes clustering, fuzzy logic and multi-parameters decision. Firstly, eight simulated datasets with different signal-to-noise ratio (SNR) were built for identification experiments. Results show that the identification sensitivity of our method is sensitive to SNR levels and acts like a low-pass filter that matches the cardiologists' recognition, while the Norm FP rate and PVB FP rate keep significantly low regardless of SNR. Furthermore, a simulated dataset including random durations of motion activities superimposed segments and two clinical datasets acquired from two different commercial recorders were adopted for the evaluation of accuracy and robustness. The overall identification results on these datasets were: sensitivity >94.69%, Norm FP rate <0.60% and PVB FP rate <2.65%. All the results were obtained without any manual threshold adjustment according to the priori information, thus dissolving the drawbacks of previous published methods. Additionally, the total cost time of our method applied to 24 h recordings is less than 1 s, which is extremely suitable in the situation of magnanimity data in long-term ECG recordings.

Combining Evidence, Specificity, and Proximity towards the Normalization of Gene Ontology Terms in Text

Structured information provided by manual annotation of proteins with Gene Ontology concepts represents a high-quality reliable data source for the research community. However, a limited scope of proteins is annotated due to the amount of human resources required to fully annotate each individual gene product from the literature. We introduce a novel method for automatic identification of GO terms in natural language text. The method takes into consideration several features: (1) the evidence for a GO term given by the words occurring in text, (2) the proximity between the words, and (3) the specificity of the GO terms based on their information content. The method has been evaluated on the BioCreAtIvE corpus and has been compared to current state of the art methods. The precision reached 0.34 at a recall of 0.34 for the identified terms at rank 1. In our analysis, we observe that the identification of GO terms in the "cellular component" subbranch of GO is more accurate than for terms from the other two subbranches. This observation is explained by the average number of words forming the terminology over the different subbranches.

형태기반 보간법과 양자화 기법을 이용한 CT 영상에서의 간 영역과 간암 추출 및 분석

In Korea, undoubtedly, the cancer is one of the most common reasons of death, and hepatoma is the second highest fatal cancer regardless of the gender only next to the stomach cancer In the middle and prime-aged between 40 and 60 years, the incidence of hepatoma is the highest in the world, and the death rate due to hepatoma is the highest among OECD countries. In this paper, we propose a novel method for automatic identification of hepatoma from a contrast enhanced CT images, which is used in an expert system that helps medical specialists. First, consecutive contrail enhanced CT images are photographed by every 5mm from the upper part of the chest, and using position information on the rib, we classify the internal area including only internal organs and the external one that consists of the rib, subcutaneous fat layers, and the background from the CT images. Then, the region of the liver is extracted from the classified internal area by using information on the intensity, the distribution of brightness, and using the regions extracted from consecutive images, we restore information on the 5 mm space occurred between the consecutive two slides tty applying a shape-based interpolation method. Lastly, using the characteristics such as the brightness and the morphology, we are able to extract the regions of hepatoma. The expert system based on our method is sufficiently competitive when it is compared with the diagnoses by specialists in the diagnostic radiology.