Mixture Model Method Research Articles

Child-to-adult body mass index trajectories and the risk of subclinical renal damage in middle age.

Although it is well established that obesity is a risk factor for chronic kidney disease, the impact of distinct long-term body mass index (BMI) developmental patterns on renal function in later life is poorly understood. This study utilized data derived from the Hanzhong Adolescent Hypertension Cohort, a prospective cohort followed over 30 years. We used latent class growth mixture modeling method to identify the BMI trajectories of participants who had received BMI measurements at least three times from childhood (age: 6-15 years) to adulthood (age: 36-45 years). The modified Poisson regression model was used to identify potential associations between BMI trajectories and subclinical renal damage (SRD) in midlife. Within a total of 2162 individuals, we identified four distinct long-term BMI trajectories: stable normal (54.72%), moderately increasing overweight (32.42%), resolving (10.27%), and progressively increasing obese (2.59%). By the latest follow-up in 2017, a total of 257 (13.1%) individuals were diagnosed with SRD. Compared with the stable normal group, the moderately increasing overweight group and the progressively increasing obese group exhibited significantly a higher urinary albumin-to-creatinine ratio and a higher odd of existing SRD in 2017 (risk ratio [RR], 1.70 [95% confidence interval (CI), 1.33-2.19] and 4.35 [95% CI, 3.00-6.30], respectively). However, individuals who resolved their elevated BMI in early life had a similar risk for SRD as those who had never been obese or overweight (RR, 1.17 [95% CI, 0.77-1.79]). Child-to-adult BMI trajectories that worsen or persist at high levels were associated with an increased risk for SRD in midlife. Maintaining a normal BMI or reversing an elevated BMI in early life may be beneficial to renal function over the long term.

Extracting glacier information from remote sensing imageries by automatic threshold method of Gaussian mixture model

Extracting glacier information from remote sensing imageries by automatic threshold method of Gaussian mixture model

Presentation of Novel Hybrid Algorithm for Detection and Classification of Breast Cancer Using Growth Region Method and Probabilistic Neural Network.

Mammography is a significant screening test for early detection of breast cancer, which increases the patient's chances of complete recovery. In this paper, a clustering method is presented for the detection of breast cancer tumor locations and areas. To implement the clustering method, we used the growth region approach. This method detects similar pixels nearby. To find the best initial point for detection, it is essential to remove human interaction in clustering. Therefore, in this paper, the FCM-GA algorithm is used to find the best point for starting growth. Their results are compared with the manual selection method and Gaussian Mixture Model method for verification. The classification is performed to diagnose breast cancer type in two primary datasets of MIAS and BI-RADS using features of GLCM and probabilistic neural network (PNN). Results of clustering show that the presented FCM-GA method outperforms other methods. Moreover, the accuracy of the clustering method for FCM-GA is 94%, as the best approach used in this paper. Furthermore, the result shows that the PNN methods have high accuracy and sensitivity with the MIAS dataset.

EVALUATION OF TREE SPECIES CLASSIFICATION METHODS USING MULTI-TEMPORAL SATELLITE IMAGES

Abstract. Tree species classification is an important step towards forest monitoring and biodiversity conservation. This research study evaluates several multispectral image classification techniques for tree species over Ahwa village in Dang district, South Gujarat, India. Multispectral images consisting of 4 bands-R, G, B and NIR collected over 4 months was used. Object-based segmentation using mean shift, cluster-based using K-Means and Gaussian Mixture Model (GMM) and pixel-based methods have been analyzed. Additionally, a new method of classification has been described using the Dynamic Time Warping (DTW) algorithm. It outperformed supervised classification techniques with accuracy over 95%. The GMM+DTW model accurately reflected the actual species distribution found in the ground truth.

Bagging for Gaussian mixture regression in robot learning from demonstration

Robot learning from demonstration (LfD) emerges as a promising solution to transfer human motion to the robot. However, because of the open-loop between the learner and task constraints, the precision of the reproduction at the desired task constraints cannot always be guaranteed and the model is not robust to changes of the training data. This paper proposes a closed-loop framework of LfD based on the bagging method of Gaussian Mixture Model and Gaussian Mixture Regression (GMM/GMR) to obtain a robust learner of LfD with high precision reproduction. The original demonstration data are divided into several sub-training data, from which multiple Gaussian mixture models are developed and combined through weighted average to provide predictions. A closed-loop is built between the reproduction of the combined learner and task constraints, and the weights that satisfy task constraints are estimated in the closed-loop. The prediction uncertainty of the models is automatically eliminated by the closed-loop, therefore, the low robustness of the LfD model to the training date is overcome. In experiments, tasks of the position and velocity are both constrained in dual closed-loop. It is shown that the proposed method can significantly meet the task constraints without increasing the complexity of the algorithm.

Hybridizing Machine Learning Methods and Finite Mixture Models for Estimating Heterogeneous Treatment Effects in Latent Classes

There has been increasing interest in exploring heterogeneous treatment effects using machine learning (ML) methods such as causal forests, Bayesian additive regression trees, and targeted maximum likelihood estimation. However, there is little work on applying these methods to estimate treatment effects in latent classes defined by well-established finite mixture/latent class models. This article proposes a hybrid method, a combination of finite mixture modeling and ML methods from causal inference to discover effect heterogeneity in latent classes. Our simulation study reveals that hybrid ML methods produced more precise and accurate estimates of treatment effects in latent classes. We also use hybrid ML methods to estimate the differential effects of private lessons across latent classes from Trends in International Mathematics and Science Study data.

Multimodal process monitoring based on transition-constrained Gaussian mixture model

Reliable process monitoring is important for ensuring process safety and product quality. A production process is generally characterized by multiple operation modes, and monitoring these multimodal processes is challenging. Most multimodal monitoring methods rely on the assumption that the modes are independent of each other, which may not be appropriate for practical application. This study proposes a transition-constrained Gaussian mixture model method for efficient multimodal process monitoring. This technique can reduce falsely and frequently occurring mode transitions by considering the time series information in the mode identification of historical and online data. This process enables the identified modes to reflect the stability of actual working conditions, improve mode identification accuracy, and enhance monitoring reliability in cases of mode overlap. Case studies on a numerical simulation example and simulation of the penicillin fermentation process are provided to verify the effectiveness of the proposed approach in multimodal process monitoring with mode overlap.

Optimized frame detection technique in vehicle accident using deep learning

 Video processing becomes one of the most popular and needed steps in machine leering. Todays, Cameras are installed in many places for many reasons including government services. One of the most applications for this concern is traffic police services. One of the main problems of using videos in machine learning application is the duration of the video; which is consuming time, paperwork and space in processing. This leads to increase the computation cost through a high number of frames. This paper proposes an algorithm to optimize videos duration using a Gaussian mixture model (GMM) method for real accident video. The Histogram of Gradient (HoG) has been used to extract the features of the video frames, a scratch CNN has been designed and conducted on two common datasets; Stanford Dogs Dataset (SDD) and Vehicle Make and Model Recognition Dataset (VMMRdb) in addition to a local dataset that created for this research. The experimental work is done in two ways, the first is after applying GMM, the finding revealed that the number of frames in the dataset was decreased by nearly 51%. The second is comparing the accuracy and complexity of these datasets has been done. Whereas the experimental results of accuracy illustrated for the proposed CNN, 85% on the local dataset, 85% on SDD Dataset and 86% on VMMRdb Dataset. However, applying GoogleNet and AlexNet on the same datasets achieved 82%, 79%, 80%, 83%, 81%, 83% respectively.

Fault diagnosis of VRF air-conditioning system based on improved Gaussian mixture model with PCA approach

Abstract The timely fault diagnosis of HVAC systems is important for building energy saving, equipment maintenance and indoor comfort. The Gaussian mixture model method has rarely been studied in the fault diagnosis application of HVAC systems. Therefore, a novel fault diagnosis strategy is proposed based on the Gaussian mixture model (GMM) method for the variable refrigerant flow air-conditioning system. To reduce excessive input variables resulting in large model complexity and long running time, the principal component analysis approach (PCA) is used to perform data dimensionality reduction. Therefore, the fault diagnosis model combining the Gaussian mixture model and principal component analysis is established, which is evaluated using the four types of faults of the variable refrigerant flow system. These faults include refrigerant undercharge, refrigerant overcharge, outdoor unit fouling and four-way reversing valve faults. Experiments are carried out under three heating conditions. Results show that the PCA-GMM approach can effectively reduce the running time. Especially for the VVV type model, the running time is reduced from 176.78 s to 15.18 s. Meanwhile, established PCA-GMMs still have good fault diagnosis correct rates when the input data dimension is reduced. Especially, some PCA-GMMs have fault diagnosis correct rates of over 99% when the number of principal components exceeds 7.

Classification of Walking Cars and Pedestrians for the use of Automatic Incident Detection as an Effort to Reduce Risk of Accidents on the Highway

Traffic problems become very important to minimize the number of accidents. Recorded in 2017, the death toll from accidents reached 703 people. While in 2018 503 people died or fell by 28%. This figure is considered to be the third largest killer, under coronary heart disease and tuberculosis / tuberculosis. Among several causes of accidents such as against the flow of traffic, stops on the road, pedestrians and speeds that are too low compared to other vehicles. Even though the traffic signs are already installed. The low level of awareness of road users will increase the number of accidents. A detection system for potential accidents is needed to reduce the risk and can be used for the investigation process if an accident occurs. The application of a traffic accident prediction system will be a solution to provide a warning of potential accidents. Early detection of incidents is very important to limit consequences such as delays for other road users, lower costs, less time commitment to emergency services, as well as to prevent accidents. Video processing obtained from CCTV installed at intersections, highways, bridges and tunnels will detect pedestrians and oncoming cars automatically. Detection is done by processing each video frame to determine the foreground by the Gaussian mixture models method of each video frame.

Dangerous Behavior Recognition of Autonomous Vehicles at Intersection Based on Gaussian Mixture Model

Urban road intersections are a typical complex traffic scene, and they are also prone to road traffic accidents. Aimed at the safety problems of road intersections, this paper proposes a model of vehicle dangerous behavior recognition based on Gaussian mixture model method, which provides an empirical basis for autonomous vehicle behavior decision at intersections. By analyzing the characteristics and rules of driving behavior at intersections, we select parameters like PET as input parameters of the model, and then construct a simulation model based on Beijing’s Weigongcun intersection, which is used to obtain the dataset in intersection scene. The obtained data was subject to Kalman filtering and normalized as data input of a Gaussian mixture model. Dangerous scenes and general scenes at the intersection scene were screened out. The screening results were analyzed and evaluated, and proved to be reliable. The model could provide an empirical basis for the decision-making design of autonomous vehicles at intersection.

Improved well log classification using semisupervised Gaussian mixture models and a new hyper-parameter selection strategy

Well log classification, the process of mapping well log measurements to lithofacies identified from core samples, is a common procedure in the oil and gas industry. Manually assigning lithofacies to the wire-line log measurements without core can be time consuming, and can also introduce a bias. Supervised machine learning algorithms are commonly used to automate this process, but they are prone to overfitting when the training data are scarce, which is common for well log classification problems. Semisupervised machine learning algorithms are designed for classification problems with minimal training data, and we adopt a semisupervised Gaussian mixture model (ssGMM) method to solve this problem. The dataset we consider for our study is from a machine learning competition held in 2016 and we simulate a semisupervised scenario by assuming only one out of the ten wells is the labeled data. We apply ssGMM to this well log dataset and compare its performance to the supervised method that was the winner of this competition, XGBoost. To try and improve the performance of both ssGMM and XGBoost, we also introduce a new hyper-parameter selection strategy that simultaneously uses the mean and standard deviation cross-validation scores, compared to the default procedure that only utilizes the mean cross-validation scores. Our results indicate that ssGMM is able to slightly outperform XGBoost in our semisupervised context, which supports the suggestion that semisupervised algorithms are more appropriate in low training data situations. We also show that our new hyper-parameter selection technique selects hyper-parameters for ssGMM that perform better on the testing data, but the performance is mixed for XGBoost.

Segmentation of Overlapped Cervical Cells Using Asymmetric Mixture Model and Shape Constraint Level Set Method

Accuracy segmentation of the nuclei and cytoplasm in Pap smear images is challenging in cervix cytological analysis. In this paper, a new fusion algorithm combining the asymmetric generalized Gaussian and Cauchy mixture model (GGCMM) with a shape constraint level set method to segment overlapping cervical smear cells is put forward. The proposed approach starts by separating nuclei and cytoplasm cluster through asymmetric GGCMM, where each component is a mixture of generalized Gaussian distribution and Cauchy distribution. The proposed asymmetric GGCMM takes into account the asymmetry of generalized Gaussian distribution and the heavier tail of Cauchy distribution. New probability distribution fits different shapes of observed data more flexibly. Then, we apply the morphological operation to remove fake nuclei which is usually much smaller than real nuclei. After that, the improved level set energy function with a distance map and a new shape prior term are applied to extract the contours of overlapping cervical cells. Due to this new level set energy function, the segmentation of every individual cell worked well, especially in overlapping areas. We evaluate the proposed method by using the ISBI 2014 Challenge Dataset. The results demonstrate that our approach outperforms existing methods in extracting overlapping cervical cells and obtains accurate cell contours.

Estimating fractional vegetation cover from leaf area index and clumping index based on the gap probability theory

Gap probability theory provides a theoretical equation to calculate fractional vegetation cover (FVC). However, the main algorithms used in present FVC products generation are still the linear mixture model and machine learning methods. The reason to limit the gap probability theory applied in the product algorithm is the availability and accuracy of leaf area index (LAI) and clumping index (CI) products. With the improvement of the LAI and CI products, it is necessary to assess whether the algorithm based on gap probability theory using the present products can improve the accuracy of FVC products. In this study, we generated the FVC estimates based on the gap probability theory (FVCgap) with a resolution of 500 m every 8 days for Europe. FVCgap estimates were validated with field FVC measurements of ImagineS from 2013 to 2015 for crop types. Two existing FVC products, Geoland2 Version1 (GEOV1) and Multisource data Synergized Quantitative remote sensing production system (MuSyQ), were used to inter-compare with the FVCgap estimates. FVCgap estimates showed a better agreement with field FVC measurements, with lowest root mean square error (RMSE) (0.1211) and bias (0.0224), than GEOV1 and MuSyQ FVC products. The inter-annual and seasonal variations of FVCgap estimates were also showed the most consistent with field measurements.

Use of Spectral Detector Computed Tomography to Improve Liver Segmentation and Volumetry.

Liver segmentation and volumetry have traditionally been performed using computed tomography (CT) attenuation to discriminate liver from other tissues. In this project, we evaluated if spectral detector CT (SDCT) can improve liver segmentation over conventional CT on 2 segmentation methods. In this Health Insurance Portability and Accountability Act-compliant institutional review board-approved retrospective study, 30 contrast-enhanced SDCT scans with healthy livers were selected. The first segmentation method is based on Gaussian mixture models of the SDCT data. The second method is a convolutional neural network-based technique called U-Net. Both methods were compared against equivalent algorithms, which used conventional CT attenuation, with hand segmentation as the reference standard. Agreement to the reference standard was assessed using Dice similarity coefficient. Dice similarity coefficients to the reference standard are 0.93 ± 0.02 for the Gaussian mixture model method and 0.90 ± 0.04 for the CNN-based method (all 2 methods applied on SDCT). These were significantly higher compared with equivalent algorithms applied on conventional CT, with Dice coefficients of 0.90 ± 0.06 (P = 0.007) and 0.86 ± 0.06 (P < 0.001), respectively. On both liver segmentation methods tested, we demonstrated higher segmentation performance when the algorithms are applied on SDCT data compared with equivalent algorithms applied on conventional CT data.

Measurement of particle decontamination behavior using interferometer

Pool scrubbing is an important factor in the dischargement process of fission products during severe accidents in nuclear power plants. Therefore, a reliable model is required to analyze particle decontamination efficiency by pool scrubbing. However, the fundamental process of particle decontamination behavior during pool scrubbing remains unknown. In an experiment, an interferometer was built to measure particle decontamination behavior from interference fringe. From the interferometer, a bright field image and interference image around the bubble were obtained from high-speed cameras. However, it was difficult to apply existing interference fringe data processing method against our images, since the method requires to adjust the area of the phase extraction manually for each image. It was unrealistic to adjust the range of the extraction area manually for each image from high-speed camera measurement. As to overcome this problem, we developed a new data processing method to separate the phase information from qualitative criteria using data clustering, called the Gaussian mixture model method. From the data processing results, we measured the detailed particle decontamination behavior around a single bubble. From a series of experiments, we discovered that the particle distribution around the bubble tends to be unsteady, although existing models predict the decontamination phenomena to be steady. The results suggest that bubble deformation, which is not considered in existing pool scrubbing models, may be a dominant factor in particle decontamination. We also confirmed that the particle distribution in the lower part of the bubble is strongly affected by gravity.

Identifying Effortful Individuals With Mixture Modeling Response Accuracy and Response Time Simultaneously to Improve Item Parameter Estimation.

The responses of non-effortful test-takers may have serious consequences as non-effortful responses can impair model calibration and latent trait inferences. This article introduces a mixture model, using both response accuracy and response time information, to help differentiating non-effortful and effortful individuals, and to improve item parameter estimation based on the effortful group. Two mixture approaches are compared with the traditional response time mixture model (TMM) method and the normative threshold 10 (NT10) method with response behavior effort criteria in four simulation scenarios with regard to item parameter recovery and classification accuracy. The results demonstrate that the mixture methods and the TMM method can reduce the bias of item parameter estimates caused by non-effortful individuals, with the mixture methods showing more advantages when the non-effort severity is high or the response times are not lognormally distributed. An illustrative example is also provided.

RMF based target position estimation and collision probability forecasting technique in freeway junctions

Collision between vehicles and pedestrians leads to brutal loss of life and assets on Indian roads. Accidents happen due to individual's negligence and misjudgement of the speed of vehicles at freeway junctions. In this paper, a novel feature extraction technique is used for estimating the target position and to update the trajectory information. A vision-based technique is incorporated to acquire the target information that is a simple and cost-effective method to examine the target's current position. Moreover, a distribution-based evaluation method is introduced to calculate the degree of conflict and avoid crashes by alerting the target. The experimental results of the proposed technique reveal an improved performance of 9% in detection rate for public datasets over the existing Gaussian mixture model (GMM) method. The proposed probabilistic collision avoidance system could be implemented on highways to reduce the accidents to a greater extent.

RMF based target position estimation and collision probability forecasting technique in freeway junctions

Collision between vehicles and pedestrians leads to brutal loss of life and assets on Indian roads. Accidents happen due to individual's negligence and misjudgement of the speed of vehicles at freeway junctions. In this paper, a novel feature extraction technique is used for estimating the target position and to update the trajectory information. A vision-based technique is incorporated to acquire the target information that is a simple and cost-effective method to examine the target's current position. Moreover, a distribution-based evaluation method is introduced to calculate the degree of conflict and avoid crashes by alerting the target. The experimental results of the proposed technique reveal an improved performance of 9% in detection rate for public datasets over the existing Gaussian mixture model (GMM) method. The proposed probabilistic collision avoidance system could be implemented on highways to reduce the accidents to a greater extent.

MULTI VEHICLE SPEED DETECTION USING EUCLIDEAN DISTANCE BASED ON VIDEO PROCESSING

One component of smart city is smart transportation, known as Intelligent Transportation Systems (ITS). In this study, we discuss the estimation of moving vehicle speed based on video processing using the Euclidean Distance method. In this study, we examine the effect of camera angles on the video acquisition to speed estimation accuracy. In addition, Region of Interest (ROI) will be designed into three parts to determine which area is the most appropriate to be chosen, so that the estimated vehicle speed will be better. These approaches have never been studied by previous researchers. The separation between the background and foreground is conducted using Gaussian Mixture Models method. By comparing the displacement distance and the number of frames per second (fps), we obtain speed estimate for each vehicle. According to the experimental results, our system can estimate the speed of the vehicle with an accuracy of 99.38%.