Deep embedded clustering of coral reef bioacoustics

우리나라 식생활에 밀접한 관련을 가지고 있는 채소인 양파의 수급불균형 해결을 위한 생산량 예측 모형 개발의 노력이 많은 연구를 통해 이뤄지고 있다. 하지만 양파의 수확기와 저장 가능성을 고려해 봤을 때 생산량 예측만으로는 수급불균형 해결이 어렵다. 따라서 본 논문에서는 양파의 생산량 정보와 가격의 다양한 요인이 포함되어 있으며 일상에서 쉽게 접할 수 있는 인터넷 기사를 이용하여 가격 예측을 위한 감성사전을 구축하고자 한다. 양파 기사는 2012년부터 2016년까지의 데이터를 사용하였고 도매시장 가격을 통한 문서구분을 통해 4가지 TF-IDF를 비교하여 적합한 TF-IDF를 사용하였다. 분석을 위하여 분할적 군집분석 중 k-means 군집, 밀도기반군집(DBSCAN; density based spatial cluster applications with noise), 가우시안혼합분포군집(GMM; Gaussian mixture model) 군집을 통하여 가격에 대한 긍정/부정 단어를 구분한 결과 GMM 군집이 의미 있는 긍정, 부정, 무정의 3개의 사전으로 구성되었다. 구축된 사전의 합리성을 비교하기 위하여 가격 상승 기사와 가격 하락 기사의 분류에 로지스틱 회귀분석을 적용한 결과 85.7%의 정확도로 구축된 사전의 합리성을 확인할 수 있었다.Many researches are accomplished as a result of the efforts of developing the production predicting model to solve the supply imbalance of onions which are vegetables very closely related to Korean food. But considering the possibility of storing onions, it is very difficult to solve the supply imbalance of onions only with predicting the production. So, this paper s purpose is trying to build a sentiment dictionary to predict the price of onions by using the internet articles which include the informations about the production of onions and various factors of the price, and these articles are very easy to access on our daily lives. Articles about onions are from 2012 to 2016, using TF-IDF for comparing with four kinds of TF-IDFs through the documents classification of wholesale prices of onions. As a result of classifying the positive/negative words for price by k-means clustering, DBSCAN (density based spatial cluster application with noise) clustering, GMM (Gaussian mixture model) clustering which are partitional clustering, GMM clustering is composed with three meaningful dictionaries. To compare the reasonability of these built dictionary, applying classified articles about the rise and drop of the price on logistic regression, and it shows 85.7% accuracy.

In conventional full-duplex communications, dedicated symbols are transmitted to estimate both the self-interference channel and the desired signal channel in order to perform self-interference cancellation (SIC) and to detect the desired signal. However, inaccurate channel estimation will produce residual self-interference and degrade the detection performance. In this paper, we exploit Gaussian mixture model (GMM) clustering to design a full-duplex transceiver (FDT), which is able to detect the desired signal without requiring digital domain channel estimations and SIC. The frame structure of the designed FDT contains two successive phases: labelling phase and data transmission phase. In particular, the designed FDT performs cluster labelling in the labelling phase and performs GMM clustering based on expectation-maximization (EM) algorithm in the data transmission phase. Furthermore, the labelling symbol design is optimized for the overhead reduction and clustering accuracy improving. Finally, simulation results show that, the bit error rate (BER) of the designed FDT is closed to the performance of the FDT with a maximum likelihood (ML) detector and perfect channel knowledge, meanwhile is superior to the BER performance of the FDT with a ML detector and a least square (LS) channel estimator.

We introduce a method based on Gaussian mixture model (GMM) clustering and level-set to automatically detect intraretina fluid on diabetic retinopathy (DR) from spectral domain optical coherence tomography (SD-OCT) images in this paper. First, each B-scan is segmented using GMM clustering. The original clustering results are refined using location and thickness information. Then, the spatial information among every consecutive five B-scans is used to search potential fluid. Finally, the improved level-set method is used to obtain the accurate boundaries. The high sensitivity and accuracy demonstrated here show its potential for detection of fluid.

In this work we propose a strategy to jointly estimate activity and borders of oncological lesions in PET/CT. The starting step is constituted by a lesion contouring on PET image volume and by a Gaussian Mixture Model (GMM) clustering which individuates a set of regions in the lesion area (lesion, uncertainty, lesion spillout, organ). A maximum likelihood (A WOSEM) reconstruction step refines regions' borders and estimates a mean convergence activity for the lesion region. It applies a model of the scanner Point Spread Function (PSF) to recover blurring and it contemporaneously works on regional basis functions and single voxels. The area outside the four regions is frozen (i.e. not updated). The algorithm was validated on an anthropomorphic phantom in which lesions have been simulated with zeolites (clinoptilolite samples, volume 0.6 - 5.2 ml) loaded with 18F-FDG. Zeolite borders for ground truth definition were derived segmenting zeolites on coregistered CT images. For each zeolite, three different initial contouring were considered, corresponding to volumes about 100%, 60% and 140% of the true volume: the GMM clustering was able to robustly delineate regions independently from the initial contouring (variations 0.75) and in estimating zeolite activity (activity error 1ml). Suboptimal results were found for zeolites at the border of the axial FOV, since the PSF model, supposed invariant to axial shift, was inadequate at the axial borders. The proposed strategy appears promising and can be proposed as a general approach for a semi-automatic quantification and segmentation of lesions previously detected on standard clinical images. It will be further validated on data sets provided with a ground truth.

The competition among Food & Beverage companies has substantially increased in today's age of digitization. Sales forecasting is one of their main challenges. Due to space limitations, employee shortages, and rising online demand, retail sales forecasting became extremely important for Food and Beverage companies. This research analyzed the sales data of a multinational Food & Beverage Company. It proposed a framework using Gaussian Mixture Model (GMM) clustering, Hierarchical Agglomerative Clustering (HAC), and Random Forest algorithm for forecasting sales. This model analyzes the impact of the weekends, holidays, promotional activities, customer sentiments, festivals, and socio-economic situations in sales data and is able to forecast sales ranging from one to 15 months. An investigation of the suggested model's performance compared to numerous cutting-edge sales forecasting techniques is carried out to show its efficacy. Here, we demonstrate that the proposed hybrid model surpasses current predicting and computing efficiency methods. The results of this study can help retail managers to allocate resources and manage inventories in well-informed ways. The findings suggest that combining many strategies may produce the most precise forecasts.

This study aimed to assess the impact and vulnerability of climate change by classifying 26 clusters of meteorologically homogeneous regions. To determine the optimal clustering method, both K-means and Gaussian Mixture Model (GMM) clustering were analyzed using the effective storage capacity to watershed area ratio. The optimal number of clusters was derived based on several evaluation metrics, including the Silhouette Score, Calinski-Harabasz Index, Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC). Ultimately, GMM clustering was identified as the optimal method, with the best clustering results obtained at k = 4 for an effective storage capacity of 100,000 to 400,000 tons and k = 5 for an effective storage capacity of 400,000 to 10,000,000 tons. Additionally, standard reservoirs applicable to agricultural production infrastructure design standards were identified based on homogeneous weather region clusters, the optimal clustering method, and centroid results. The findings of this study can serve as fundamental data for the development and revision of design standards, contributing to more climate-resilient agricultural infrastructure.

In this paper, we propose a novel method which utilizes the Gaussian Mixture Model (GMM) to estimate the leakage distribution of a circuit. Our proposed method assumes that the leakage distribution can be represented using the GMM which can cover any continuous function. After the GMM clustering using the leakage simulation data, the leakage distribution of the input circuit can be obtained. The experimental results with the K-S test showed that the proposed method exhibited 1.82e+05 times larger p-value and 7.74e-01 times smaller K-S statistics compared to the state-of-the-art benchmark method on average.

Recently, many fields have widely used cluster analysis: psychology, biology, statistics, pattern recognition, information retrieval, machine learning, and data mining. Diagnosis of histopathological images of prostate cancer is one of the routine tasks for pathologists and it is challenging for pathologists to analyze the formation of glands and tumors based on the Gleason grading system. In this study, unsupervised classification has been performed for differentiating malignant (cancerous) from benign (non-cancerous) tumors. Therefore, the unsupervised-based computer-aided diagnosis (CAD) technique would be of great benefit in easing the workloads of pathologists. This technique is used to find meaningful clustering objects (i.e., individuals, entities, patterns, or cases) and identify useful patterns. Radiomic-based features were extracted for cluster analysis using the gray-level co-occurrence matrix (GLCM), gray-level run-length matrix (GLRLM), and gray-level size zone matrix (GLSZM) techniques. Multi-clustering techniques used for the unsupervised classification are K-means clustering, K-medoids clustering, Agglomerative Hierarchical (AH) clustering, Gaussian mixture model (GMM) clustering, and Spectral clustering. The quality of the clustering algorithms was determined using Purity, Silhouettes, Adjusted Rand, Fowlkes Mallows, and Calinski Harabasz (CH) scores. However, the best-performing algorithm (i.e., K-means) has been applied to predict and annotate the cancerous regions in the whole slide image (WSI) to compare with the pathologist annotation.

This thesis investigates the integration of machine learning techniques into computational fluid dynamics (CFD) to improve simulation accuracy and computational performance. It proposes the use of the Gaussian Mixture Model (GMM) and unsupervised clustering as novel tools for mesh adaptation and flow feature detection. The methodology applies GMM to segment the computational domain into regions with distinct physical behaviour, viscous/turbulent and inviscid/irrotational. This clustering-based p-adaptation approach removes the need for predefined thresholds, enabling a fully automated and localised adjustment of the solution's polynomial degree. As a result, mesh refinement becomes more efficient and precise. A series of numerical experiments, including simulations of flow past a cylinder at varying Reynolds numbers, demonstrate the ability of the method to balance computational cost and solution fidelity. The framework is also tested on real-world engineering cases, such as wind turbine wake simulations, confirming its scalability and robustness. The results show substantial gains in efficiency and adaptability, establishing this approach as a promising solution for a wide range of CFD applications. By embedding machine learning into high-order solvers, this work advances the state-of-the-art and opens new directions for adaptive simulation strategies. It also lays the groundwork for future research in error-driven refinement, 3D turbulence modeling, and the broader application of AI in fluid mechanics. Keywords: Computational fluid dynamics, machine learning, Gaussian Mixture Model, p-adaptation, flow feature detection, mesh refinement, high-order methods. RESUMEN Esta tesis examina la integración de técnicas de aprendizaje automático en la dinámica de fluidos computacional (CFD) para mejorar la precisión de las simulaciones y reducir el coste computacional. Se propone el uso del Modelo de Mezclas Gaussianas (GMM) y métodos de agrupamiento no supervisado para desarrollar nuevas estrategias de adaptación de malla y detección de regiones del flujo sin recurrir a umbrales predefinidos. La metodología aplica GMM para segmentar automáticamente el dominio en regiones con diferentes características físicas, como zonas viscosas/turbulentas y no viscosas/irrotacionales. Esta clasificación guía un proceso de p-adaptación, ajustando el orden polinomial de la solución numérica de forma localizada, lo que permite un refinamiento automático, preciso y eficiente. Diversos experimentos numéricos, incluyendo simulaciones del flujo alrededor de un cilindro para distintos números de Reynolds, demuestran que el enfoque propuesto equilibra de manera eficaz el coste computacional y la calidad de la solución. Además, su aplicabilidad se valida en casos reales como las estelas de aerogeneradores, evidenciando la escalabilidad del método. Los resultados confirman que estas técnicas mejoran la eficiencia y adaptabilidad de los solucionadores de alto orden, lo que las convierte en herramientas prometedoras para aplicaciones complejas en CFD. La tesis contribuye así al estado del arte al integrar aprendizaje automático con métodos numéricos avanzados, y sienta las bases para investigaciones futuras sobre refinamiento adaptativo basado en errores, simulaciones tridimensionales de turbulencia y uso extendido de IA en mecánica de fluidos. Palabras clave: dinámica de fluidos computacional, aprendizaje automático, Modelo de Mezclas Gaussianas, p-adaptación, detección de características, refinamiento de mallas, métodos de alto orden. RÉSUMÉ Cette thèse explore l’intégration des techniques d’apprentissage automatique dans la dynamique des fluides numérique (CFD) afin d’améliorer la précision des simulations et la surcharge de calcul. En utilisant le modèle de mélanges gaussiens (GMM) et des méthodes de regroupement non supervisé, cette recherche introduit des approches innovantes pour l’adaptation du maillage et la détection des structures d’écoulement. La méthodologie se concentre sur l’application du GMM pour partitionner les champs d’écoulement en régions distinctes, régions distinctes, différenciant les zones visqueuses/turbulentes des zones inviscides/irrotationnelles. Cette technique de p-adaptation basée sur le regroupement élimine le besoin de seuils prédéfinis, permettant ainsi un raffinement du maillage automatisé, efficace et précis. De nombreuses expériences numériques sur divers cas tests, notamment l’écoulement autour d’un cylindre à différents nombres de Reynolds, démontrent la capacité de la méthode proposée à équilibrer le coût computationnel et la fidélité de la solution. Des applications en ingénierie, telles que la simulation du sillage d’une éolienne, illustrent davantage l’évolutivité des méthodes proposées. Les résultats montrent des améliorations significatives en termes du coût computationnel et d’adaptabilité, rendant ces techniques particulièrement adaptées à un large éventail d’applications en CFD. Ce travail contribue à l’état de l’art en intégrant de manière fluide l’apprentissage automatique dans les solveurs CFD d’ordre élevé, ouvrant ainsi la voie à de futures avancées dans les méthodes de simulation adaptative. Les méthodologies proposées établissent une base pour l’exploration du raffinement basé sur l’erreur, la modélisation tridimensionnelle de la turbulence et l’adoption plus large de l’apprentissage automatique dans la recherche en dynamique des fluides.

Multi-person behavior event recognition has become an increasingly challenging research field in human–computer interaction. With the rapid development of deep learning and computer vision, it plays an important role in the inference and analysis of real sports events, that is, given the video frequency of sports events, when letting it analyze and judge the behavior trend of athletes, often faced with the limitations of large-scale data sets and hardware, it takes a lot of time, and the accuracy of the results is not high. Therefore, we propose a deep clustering learning network for motion recognition under the self-attention mechanism, which can efficiently solve the accuracy and efficiency problems of sports event analysis and judgment. This method can not only solve the problem of gradient disappearance and explosion in the recurrent neural network (RNN), but also capture the internal correlation between multiple people on the sports field for identification, etc., by using the long and short-term memory network (LSTM), and combine the motion coding information in the key frames with the deep embedded clustering (DEC) to better analyze and judge the complex behavior change types of athletes. In addition, by using the self-attention mechanism, we can not only analyze the whole process of the sports video macroscopically, but also focus on the specific attributes of the movement, extract the key posture features of the athletes, further enhance the features, effectively reduce the amount of parameters in the calculation process of self-attention, reduce the computational complexity, and maintain the ability to capture details. The accuracy and efficiency of reasoning and judgment are improved. Through verification on large video datasets of mainstream sports, we achieved high accuracy and improved the efficiency of inference and prediction. It is proved that the method is effective and feasible in the analysis and reasoning of sports videos.

Research on FT-transformer-based intelligent discrimination model for roof stability classification of retreating coal roadways

This paper presents a method for the establishment of mapping between low-level visual features and high-level semantic features based on Gaussian mixture model (GMM). We combine color features and texture features based on Gabor filter, an image annotation algorithm based on GMM clustering is put forward through the research in image processing. In an image database that contains 1000 images, the experimental results show that the proposed method has better performance.

Forests are the main part of the terrestrial ecosystem. Airborne LiDAR is fast, comprehensive, penetrating, and contactless and can depict 3D canopy information with a high efficiency and accuracy. Therefore, it plays an important role in forest ecological protection, tree species recognition, carbon sink calculation, etc. Accurate recognition of individual trees in forests is a key step to various application. In real practice, however, the accuracy of individual tree segmentation (ITS) is often compromised by under-segmentation due to the diverse species, obstruction and understory trees typical of a high-density multistoried mixed forest area. Therefore, this paper proposes an ITS optimization method based on Gaussian mixture model for airborne LiDAR data. First, the mean shift (MS) algorithm is used for the initial ITS of the pre-processed airborne LiDAR data. Next, under-segmented samples are extracted by integrated learning, normally segmented samples are classified by morphological approximation, and the approximate distribution uncertainty of the normal samples is described with a covariance matrix. Finally, the class composition among the under-segmented samples is determined, and the under-segmented samples are re-segmented using Gaussian mixture model (GMM) clustering, in light of the optimal covariance matrix of the corresponding categories. Experiments with two datasets, Trento and Qingdao, resulted in ITS recall of 94% and 96%, accuracy of 82% and 91%, and F-scores of 0.87 and 0.93. Compared with the MS algorithm, our method is more accurate and less likely to under-segment individual trees in many cases. It can provide data support for the management and conservation of high-density multistoried mixed forest areas.

Background noise, speech privacy, and productivity are strictly related in offices. Employees’ speech may affect the performance of colleagues disturbing their concentration. On the other hand, HVAC noise can mask irrelevant speech noise, improving in some way employee’s comfort. Thus, it becomes important to achieve the ability to individuate in an unattended way the different sound sources during working hours: mechanical (HVAC, devices, computers, …), human, traffic, activities in nearby spaces. Clustering techniques provide tools to find patterns among unlabeled data. In a previous study, Gaussian Mixture Model and K-means clustering were applied to sound level meter measurements carried out during working hours. The reliability of results encourages the investigation to find robust features to label the sound sources. In the present work, a sound level measurement has been carried out alongside the audio recording of the working activities throughout a whole day. Besides the clustering performed via Gaussian Mixture Model and K-means clustering over the sound pressure levels strings, a Variational Autoencoder was used to find latent features from the recording. Correlations between the methods explore the chance to obtain a broader understanding of data obtained by long-term monitoring.

Image segmentation is an important process in the field of medical imaging.Recently, much work has been reported in medical image segmentation. Among these techniques, finite Gaussian mixture models are considered to be more recent and accurate. However, in this approach, a number of segments that an image can be divided are taken through apriori and if these segments are not initiated properly it leads to misclassification. Hence, to overcome this disadvantage, we proposed an algorithm for Medical Image Segmentation using Hierarchical Clustering and Skew Gaussian Mixture. The experimentation is done with two different brain images and the results obtained are evaluated using both Image Quality metrics and Segmentation Quality Metrics.