Rice seed image classification based on HOG descriptor with missing values imputation

Rice is a primary source of food consumed by almost half of world population. Rice quality mainly depends on the purity of the rice seed. In order to ensure the purity of rice variety, the recognition process is an essential stage. In this paper, we firstly propose to use histogram of oriented gradient (HOG) descriptor to characterize rice seed images. Since the size of image is totally random and the features extracted by HOG can not be used directly by classifier due to the different dimensions. We apply several imputation methods to fill the missing data for HOG descriptor. The experiment is applied on the VNRICE benchmark dataset to evaluate the proposed approach

Vehicle logo recognition using histograms of oriented gradient descriptor and sparsity score

Most of vehicle have the similar structures and designs. It is extremely complicated and difficult to identify and classify vehicle brands based on their structure and shape. As we requirea quick and reliable response, so vehicle logos are an alternative method of determining the type of a vehicle. In this paper, we propose a method for vehicle logo recognition based on feature  selection method in a hybrid way. Vehicle logo images are first characterized by histograms of oriented gradient descriptors and the final features vector are then applied feature selection method to reduce the irrelevant information. Moreover, we release a new benchmark dataset for vehicle logo recognition and retrieval task namely, VLR-40. The experimental results are evaluated on this database which show the efficiency of the proposed approach

Data augmentation by combining feature selection and color features for image classification

Image classification is an essential task in computer vision with various applications such as bio-medicine, industrial inspection. In some specific cases, a huge training data is required to have a better model. However, it is true that full label data is costly to obtain. Many basic pre-processing methods are applied for generating new images by translation, rotation, flipping, cropping, and adding noise. This could lead to degrade the performance. In this paper, we propose a method for data augmentation based on color features information combining with feature selection. This combination allows improving the classification accuracy. The proposed approach is evaluated on several texture datasets by using local binary patterns features

Machine learning-based conditional mean filter: a generalization of the ensemble Kalman filter for nonlinear data assimilation

Filtering is a data assimilation technique that performs the sequential inference of dynamical systems states from noisy observations. Herein, we propose a machine learning-based ensemble conditional mean filter (ML-EnCMF) for tracking possibly high-dimensional non-Gaussian state models with nonlinear dynamics based on sparse observations. The proposed filtering method is developed based on the conditional expectation and numerically implemented using machine learning (ML) techniques combined with the ensemble method. The contribution of this work is twofold. First, we demonstrate that the ensembles assimilated using the ensemble conditional mean filter (EnCMF) provide an unbiased estimator of the Bayesian posterior mean, and their variance matches the expected conditional variance. Second, we implement the EnCMF using artificial neural networks, which have a significant advantage in representing nonlinear functions over high-dimensional domains such as the conditional mean. Finally, we demonstrate the effectiveness of the ML-EnCMF for tracking the states of Lorenz-63 and Lorenz-96 systems under the chaotic regime. Numerical results show that the ML-EnCMF outperforms the ensemble Kalman filter

Identify aerodynamic derivatives of the airplane attitude channel using a spiking neural network

The paper proposes a method for identifying aerodynamic coefficient derivatives of aircraft attitude channel using spiking neural network (SNN) and Gauss-Newton algorithm based on data obtained from actual flights. Using SNN combination with Gauss-Newton iterative calculation algorithm allows the identification of aerodynamic coefficient derivatives in a nonlinear model for aerodynamic parameters with higher accuracy and faster calculation time. The paper proposes an algorithm to train the SNN multi-layer network by Normalized Spiking Error Back Propagation (NSEBP), in which, in the forward propagation period, the time of output spikes is calculating by solving quadratic equations instead of detection by traditional methods. The phase of propagation of errors backward uses the step-by-step calculation instead of the conventional gradient calculation method. The identification results are compared with the results when using the RBN network to prove the algorithm efficienc

Realtime face matching and gender prediction based on deep learning

Face analysis is an essential topic in computer vision that dealing with human faces for recognition or prediction tasks. The face is one of the easiest ways to distinguish the identity people. Face recognition is a type of personal identification system that employs a person’s personal traits to determine their identity. Human face recognition scheme generally consists of four steps, namely face detection, alignment, representation, and verification. In this paper, we propose to extract information from human face for several tasks based on recent advanced deep learning framework. The proposed approach outperforms the results in the state-of-the-art

Pengembangan Buku Ajar Mata Kuliah Bahasa Indonesia Untuk Mahasiswa STMIK Palangka Raya

Penelitian ini bertujuan mendeskripsikan proses perkuliahan Bahasa Indonesia di STMIK Palangka Raya, mengembangkan buku ajar mata kuliah Bahasa Indonesia yang sesuai dengan capaian kompetensi lulusan STMIK Palangka Raya, dan mengevaluasi validitas, efektivitas, serta kepraktisan buku ajar tersebut. Metode yang digunakan adalah Research and Development dengan menganalisis kebutuhan kemudian mengembangkan produk dan diuji validitas, efektivitas, dan kepraktisanya hingga diperoleh produk final. Produk yang dikembangkan adalah buku ajar mata kuliah Bahasa Indonesia untuk mahasiswa STMIK Palangka Raya. Hasil analisis menunjukkan buku ajar validitas sebesar 93%, efektif kerena sebesar 92,67% mahasiswa tuntas KKM, dan praktis karena mahasiswa sebesar 87,33% merespon keterterapan produk dengan baik, serta 89,60% praktisi merespon dengan baik saat diterapkan. Buku ajar ini memiliki keterterapan yang tinggi karena memiliki validitas yang tinggi pula. Buku ajar yang dikembangkan juga memiliki efektivitas yang baik, karena memiliki validitas yang tinggi dan kepraktisan yang tinggi pula. Dengan kepraktisan buku ajar yang tinggi, maka belajar materi Bahasa Indonesia akan lebih mudah.Disarankan agar dosen dapat menerapkan buku ajar Bahasa Indonesia untuk mahasiswa STMIK Palangka Raya guna membekali mahasiswa untuk cakap menerapkan kaidah-kaidah Bahasa Indonsia yang baik dan benar, dalam bidang ilmu pengetahuan dan teknologi maupun dalam kehidupan sehari-hari

Uncertainty Quantification in the Directed Energy Deposition Process Using Deep Learning-Based Probabilistic Approach

peer reviewedThis study quantifies the effects of uncertainty raised from process parameters, material properties, and boundary conditions in the directed energy deposition (DED) process of M4 High-Speed Steel using deep learning (DL)-based probabilistic approach. A DL-based surrogate model is first constructed using the data obtained from a finite element (FE) model, which was validated against experiment. Then, sources of uncertainty are characterized by the probabilistic method and are propagated by the Monte-Carlo (MC) method. Lastly, the sensitivity analysis (SA) using the variance-based method is performed to identify the parameters inducing the most uncertainty to the melting pool depth. Using the DL-based surrogate model instead of solely FE model significantly reduces the computational time in the MC simulation. The results indicate that all sources of uncertainty contribute to a substantial variation on the final printed product quality. Moreover, we find that the laser power, the convection, the scanning speed, and the thermal conductivity contribute the most uncertainties on the melting pool depth based on the SA results. These findings can be used as insights for the process parameter optimization of the DED process.EDPOM

Fast and accurate prediction of temperature evolutions in additive manufacturing process using deep learning

Typical computer-based parameter optimization and uncertainty quantification of the additive manufacturing process usually requires significant computational cost for performing high-fidelity heat transfer finite element (FE) models with different process settings. This work develops a simple surrogate model using a feedforward neural network (FFNN) for a fast and accurate prediction of the temperature evolutions and the melting pool sizes in a metal bulk sample (3D horizontal layers) manufactured by the DED process. Our surrogate model is trained using high-fidelity data obtained from the FE model, which was validated by experiments. The temperature evolutions and the melting pool sizes predicted by the FFNN model exhibit accuracy of 99% and 98%, respectively, compared with the FE model for unseen process settings in the studied range. Moreover, to evaluate the importance of the input features and explain the achieved accuracy of the FFNN model, a sensitivity analysis (SA) is carried out using the SHapley Additive exPlanation (SHAP) method. The SA shows that the most critical enriched features impacting the predictive capability of the FFNN model are the vertical distance from the laser head position to the material point and the laser head position.VINIF.2020.DA15 EDPOMP projec