Analisa Perbandingan Citra Hasil Segmentasi Menggunakan Metode K-Means dan Fuzzy C Means pada Citra Input Terkompresi

<p><em style="text-align: justify; text-indent: 14.2pt;"><span style="font-family: 'Times New Roman',serif; mso-ansi-language: EN;" lang="EN">In pattern recognition, image processing plays a role in automatically separating objects from the background. In addition, the object will be processed by the pattern classifier. In the medical world, image processing plays a very important role. CT Scan (Computed Tomography) or CAT Scan (Computed Axial Tomography) is an example of an image processing application that can be used to view fragments or cross sections of parts of the human body. Tomography is the process of producing two-dimensional images from three-dimensional film through several one-dimensional scans. Magnetic resonance imaging (MRI) is the image most often used in the field of radiology. MRI images can display the anatomical details of objects clearly in multiple sections (multiplanar) without changing the patient's position. In this study, two methods were compared, namely K-Means and Fuzzy C Means, in a segmentation process with the aim of separating between normal areas or areas with disturbances (lesions). The images used are brain and chest MRI images with a total of 10 MRI images. The image quality of the segmentation results is compared with the quality test using the Variation of Information (VOI) parameters, Global Consistency Error (GCE), MSE (Mean Square Error), PSNR (Peak Signal to Noise Ratio) and segmentation time.</span></em></p><pre style="text-align: justify; text-indent: 14.2pt;"><em><span style="font-family: 'Times New Roman',serif; mso-ansi-language: EN;" lang="EN">In pattern recognition, image processing plays a role in automatically separating objects from the background. In addition, the object will be processed by the pattern classifier. In the medical world, image processing plays a very important role. CT Scan (Computed Tomography) or CAT Scan (Computed Axial Tomography) is an example of an image processing application that can be used to view fragments or cross sections of parts of the human body. Tomography is the process of producing two-dimensional images from three-dimensional film through several one-dimensional scans. Magnetic resonance imaging (MRI) is the image most often used in the field of radiology. MRI images can display the anatomical details of objects clearly in multiple sections (multiplanar) without changing the patient's position. In this study, two methods were compared, namely K-Means and Fuzzy C Means, in a segmentation process with the aim of separating between normal areas or areas with disturbances (lesions). The images used are brain and chest MRI images with a total of 10 MRI images. The image quality of the segmentation results is compared with the quality test using the Variation of Information (VOI) parameters, Global Consistency Error (GCE), MSE (Mean Square Error), PSNR (Peak Signal to Noise Ratio) and segmentation time.</span></em></pre>

Dual graph wavelet neural network for graph-based semi-supervised classification

Vertex classification is an important graph mining technique and has important applications in fields such as social recommendation and e-Commerce recommendation. Existing classification methods fail to make full use of the graph topology to improve the classification performance. To alleviate it, we propose a Dual Graph Wavelet neural Network composed of two identical graph wavelet neural networks sharing network parameters. These two networks are integrated with a semi-supervised loss function and carry out supervised learning and unsupervised learning on two matrixes representing the graph topology extracted from the same graph dataset, respectively. One matrix embeds the local consistency information and the other the global consistency information. To reduce the computational complexity of the convolution operation of the graph wavelet neural network, we design an approximate scheme based on the first type Chebyshev polynomial. Experimental results show that the proposed network significantly outperforms the state-of-the-art approaches for vertex classification on all three benchmark datasets and the proposed approximation scheme is validated for datasets with low vertex average degree when the approximation order is small.

Robust Tightly Coupled Pose Measurement Based on Multi-Sensor Fusion in Mobile Robot System

Currently, simultaneous localization and mapping (SLAM) is one of the main research topics in the robotics field. Visual-inertia SLAM, which consists of a camera and an inertial measurement unit (IMU), can significantly improve robustness and enable scale weak-visibility, whereas monocular visual SLAM is scale-invisible. For ground mobile robots, the introduction of a wheel speed sensor can solve the scale weak-visibility problem and improve robustness under abnormal conditions. In this paper, a multi-sensor fusion SLAM algorithm using monocular vision, inertia, and wheel speed measurements is proposed. The sensor measurements are combined in a tightly coupled manner, and a nonlinear optimization method is used to maximize the posterior probability to solve the optimal state estimation. Loop detection and back-end optimization are added to help reduce or even eliminate the cumulative error of the estimated poses, thus ensuring global consistency of the trajectory and map. The outstanding contribution of this paper is that the wheel odometer pre-integration algorithm, which combines the chassis speed and IMU angular speed, can avoid the repeated integration caused by linearization point changes during iterative optimization; state initialization based on the wheel odometer and IMU enables a quick and reliable calculation of the initial state values required by the state estimator in both stationary and moving states. Comparative experiments were conducted in room-scale scenes, building scale scenes, and visual loss scenarios. The results showed that the proposed algorithm is highly accurate—2.2 m of cumulative error after moving 812 m (0.28%, loopback optimization disabled)—robust, and has an effective localization capability even in the event of sensor loss, including visual loss. The accuracy and robustness of the proposed method are superior to those of monocular visual inertia SLAM and traditional wheel odometers.

Discourse-Level Event Temporal Ordering with Uncertainty-Guided Graph Completion

Learning to order events at discourse-level is a crucial text understanding task. Despite many efforts for this task, the current state-of-the-art methods rely heavily on manually designed features, which are costly to produce and are often specific to tasks/domains/datasets. In this paper, we propose a new graph perspective on the task, which does not require complex feature engineering but can assimilate global features and learn inter-dependencies effectively. Specifically, in our approach, each document is considered as a temporal graph, in which the nodes and edges represent events and event-event relations respectively. In this sense, the temporal ordering task corresponds to constructing edges for an empty graph. To train our model, we design a graph mask pre-training mechanism, which can learn inter-dependencies of temporal relations by learning to recover a masked edge following graph topology. In the testing stage, we design an certain-first strategy based on model uncertainty, which can decide the prediction orders and reduce the risk of error propagation. The experimental results demonstrate that our approach outperforms previous methods consistently and can meanwhile maintain good global consistency.

PEMILIHAN METODE SEGMENTASI PADA CITRA ULTRASONOGRAFI OVARIUM

Penelitian ini membandingkan metode segmentasi untuk mengenali folikel pada citra ultrasonografi ovarium, metode segmentasi yang paling baik akan digunakan untuk proses perhitungan jumlah folikel. Penilaian kinerja metode segmentasi active contour dan active contour without edge dievaluasi menggunakan Probabilistic Rand Index (PRI) dan Global Consistency Error (GCE). Hasil penelitian ini menunjukkan metode segmentasi yang terbaikadalah active contour without edge karena memiliki nilai PRI lebih tinggi dan pada nilai GCE lebih rendah dari pada hasil metode segmentasi active contour.

Agent-based Multi-tier SLA Negotiation for Intercloud

The evolving intercloud enables idle resources to be traded among cloud providers to facilitate optimizing utilization and to improve the cost-effectiveness of service for cloud consumers. However, several challenges are raised for this multi-tier dynamic market, where cloud providers not only compete for consumer requests but also cooperate with each other. To establish a healthier and more efficient intercloud ecosystem, this paper proposed a multi-tier agent-based fuzzy constraint-directed negotiation (AFCN) model for a fully distributed negotiation environment without a broker to coordinate the negotiation process. The novelty of AFCN is the use of a fuzzy membership function to represent imprecise preferences of the agent, which not only reveals the opponent’s behavior preference but can also specify the possibilities prescribing the extent to which the feasible solutions are suitable for the agent’s behavior. Moreover, this information can pass and guide each tier of negotiation to generate a more favorable proposal. Thus, the multi-tier AFCN can not only improve the performance of negotiation, but also enforce global consistency to improve the integrated solution capacity in the intercloud. The experimental results demonstrate that the proposed multi-tier AFCN model outperforms other agent negotiation models and gives full play to the efficiency and scalability of the intercloud in terms of the level of satisfaction, the ratio of successful negotiation, the average revenue of the cloud provider, and the buying price of the unit cloud resource.

Language and Brand

The language used in a product or service has an extraordinary impact on the creation of its brand and on its online success. As localization is a key aspect of a globalized business, attention should be given to the localization of brand language to ensure global consistency. This study explores brand language localization problems in an online help corpus. Specifically, it analyzes the problems posed by the localization of brand names and terms in the pre-translation phase, following Nord’s pre-translation text analysis theory (2012). The main objective of the study is to understand the nature of identified brand language problems (professional purposes) and examine them (research purposes). The method implemented is a qualitative, interpretative analysis of a monolingual corpus in English comprising representative extracts from the Dropbox and Google Drive Online Help systems. The study is part of a wider research project exploring the concept of localization problems in online help localization.