Development of Computational Model of Motorcycle and Rider during Collision

The goal of this project is to develop the computational model of motorcycle and rider for deformable body. Also, to identify the response of rider and motorcycle on collision. Computational model is the one method that can replace the actual experiment on crash test. From the simulation can save cost by actual impact crash test. This project begins with design the model of actual motorcycle. Because the model of the project using Honda Wave 100R is to complex and the computer not powerful enough to generate mesh the simplified model is use for the project. Then, the material of this project using ANSI 304 stainless steel. The simulation of the experiment run by the ANSYS software to calculate mathematical model result after impact. Finally, the result of deformation was recorded to compare the result of deformation on actual crash test. The Comparison result of deformation actual and simulation are quietly similar

Investigation of the visual appearance of different skirt styles and materials during the fitting simulation with Lectra Modaris

This paper examines the influence of different fabric types and material properties on the visual appearance of three types of skirt styles using numerical simulation. The purpose of the simulation is to present and analyze the appearance of virtual garments, create the best-fitting pattern pieces, and choose the appropriate fabric type based on the multiple clothing styles to achieve the intended design. The results show that when the type of fabric changes without changing the clothing style and on the other hand the style changes without changing the type of material, the appearance of the garment changes and the effect can be clearly seen with the 3d comparison result. These changes should be taken into account when choosing the right fabric for different styles.

QUANTITATIVE COMPARISON OF THE EFFICIENCY OF THE COLOR PROXIMITY CRITERIA OF OBJECTS WITH KNOWN SPECTRA

The work is devoted to the problem of comparing objects by color. The following statement of the problem is considered: among the set of objects it is necessary to find such an object, the color of which is most similar to the color of the given object. It is assumed that for each object only its spectrum (transmission, reflection, radiation) is known, which is an exhaustive characteristic of the color of the object. In addition, the spectrum of the radiation source is assumed to be known. The use of standard methods for determining color differences has shown that the problem does not have an unambiguous solution. Two approaches to its solution have been proposed: the first is based on the transition from the spectrum to color spaces with the subsequent calculation of the Euclidean distance, and the second is based on a direct comparison of the spectra as functional dependences of the intensity on the wavelength. Within each of the approaches, two criteria for the "similarity" of objects in color are proposed, and an original approach to assessing the effectiveness of these criteria is proposed. This approach is based on the use of expert assessments of the color proximity of glass samples with known transmission spectra from a standard set. For each sample from the set, experts selected the glass closest in color from the remaining ones, after which a generalized opinion of experts was formed. To obtain an assessment of the quality of each of the criteria, for each of them and for each test glass, the remaining samples were ranked in order of increasing color distance to the given test glass. After that, the results of the criteria were compared with the generalized opinion of experts. To make the comparison result "fuzzy", for each test glass it was proposed to consider a set of five glasses closest in color (for each of the criteria). The resulting estimates of the effectiveness of each of the criteria for a set of 89 glasses are obtained and an approach to the construction of more effective complex criteria is proposed.

Dinamika Stres Pengasuhan Pada Orangtua dengan Anak Bibir Sumbing

This study aims to explore the dynamics of parenting stress experienced by parents with cleft children. Previous studies were conducted with quantitative methods. therefor a qualitative approach is used to better understand the dynamics of parenting stress through parents' perspective. Purposive sampling is used with the help of NGO who facilitate families with cleft children. The data is analyzed with thematic analysis and cross-case comparison. Result shows that parenting stress began with a stressor, which is the fact that parents had clef children. Parents then showed emotional and behavioral responses which potentially trigger parenting stress influenced by risk and protective factors. Existing risk factors could predict a higher level of parenting stress, meanwhile protective factors are able to reduce the level of parenting stress. This study further finds that mixed emotion could appear as an emotional response, also that religious value played an important role as a protective factor.

Religious Commitment Inventory-10: On Comparison Result between Minangkabau Nomad Traders and Java Transmigrants

Religious Commitment Inventory–10 (RCI-10) in this study was used to compare the religious commitment between Minangkabau nomad traders and Java transmigrants. This research used a quantitative approach with independent sample test analysis methods. The samples of this study involved 15 respondents of Minangkabau nomad traders and 15 respondents of Java transmigrants residing in Aceh Tengah and were taken using random sampling technique. The measurements used in this study were adaptations of the RCI-10. Results of this study indicated that there is no significant difference between RCI-10 results between Minangkabau nomad traders and Java transmigrants. The conclusion is that both Minangkabau nomad traders and Java transmigrants are enjoy their lives and work in the activities of their religious affiliation.

Tipologi batas wilayah administrasi untuk memudahkan pengelolaan batas daerah di Indonesia

Abstrak Terhitung sejak 1945 – 2017, baru sekitar 48% dari 977 segmen batas daerah di Indonesia yang disahkan melalui Peraturan Menteri Dalam Negeri tentang batas daerah. Pengelolaan batas wilayah daerah sangat penting untuk berbagai urusan pembangunan misalnya pengelolaan sumber daya alam. Oleh karena itu, penelitian ini mengusulkan pendekatan baru untuk mendukung pengelolaan batas wilayah yang efisien yaitu melalui segmentasi berbasis tipologi batas wilayah. Tahapan analisis meliputi: membandingkan, memotong, dan mengelompokkan garis batas. Single Buffer Overlay Method digunakan untuk membandingkan unsur geografis pada Peta Rupabumi Indonesia (data referensi) dan garis batas 2017 (data yang diuji). Selanjutnya, dilakukan pemotongan garis sesuai hasil perbandingan. Pada akhirnya, garis tersebut dikelaskan berdasarkan tipologinya (igir, jalan, dan sungai) menggunakan metode SQL (Structured Query Language). Hasil penelitian menunjukkan bahwa mayoritas (41,8%) batas daerah di Indonesia tidak menggunakan unsur geografis tertentu sebagai penanda batasnya, sedangkan persentase penanda batas berupa sungai 35,9%, igir 16,4%, dan jalan 5,8%. Abstract Since 1945 – 2017, only 48% of 977 regional borderlines of Indonesia were legalized by The Ministry of Home Affairs. Properly managed intra-national boundaries are fundamental for development purposes e.g. natural resource management. Therefore, this research proposed a new approach to help managing the boundary efficiently through typology-based borderlines segmentation which was conducted by some stages: compare, split, and classify lines. Single Buffer Overlay Method used for comparison purpose by utilizing some geographical features on a topographic map as a referenced dataset and boundary line (2017 database) as a tested dataset. Then we split the lines based on the comparison result. Finally, each split line was classified into border typologies (road, ridge, and stream) by using the SQL (Structured Query Language) method. We found that most of The Indonesian administrative boundary segments (41.8%) did not use a geographical feature, while the boundary on the rivers 35.9%, ridges 16.4%, and roads 5.8%.

Exploring the Detection Accuracy of Concrete Cracks Using Various CNN Models

Automatic crack detection with the least amount of workforce has become a crucial task in the inspection and evaluation of the performances of concrete structure in civil engineering. Recently, although many concrete crack detection models based on convolutional neural networks (CNNs) have been developed, the accuracy of the proposed models varies. Up-to-date, the issue regarding the convolutional neural network architecture with best performance for detecting concrete cracks is still debated in many previous studies. In this paper, we choose three established open-source CNN models (Model1, Model2, and Model3) which have been well-illustrated and verified in previous studies and test them for the purpose of crack detection of concrete structures. The chosen three models are trained using a concrete crack dataset containing 40,000 images those with 227 × 227-pixel in size. The performance of three different convolutional neural network (CNN) models was then evaluated. The comprehensive comparison result indicates that Model2 which used batch normalization is capable of the best performance amongst the three models as selected for concrete cracks detection, with recording the highest classification accuracy and low loss. In a conclusion, we recommend Model2 for a concrete crack detection task.

Four Solutions for Fractional p-Laplacian Equations with Asymmetric Reactions

We consider a Dirichlet type problem for a nonlinear, nonlocal equation driven by the degenerate fractional p-Laplacian, whose reaction combines a sublinear term depending on a positive parameter and an asymmetric perturbation (superlinear at positive infinity, at most linear at negative infinity). By means of critical point theory and Morse theory, we prove that, for small enough values of the parameter, such problem admits at least four nontrivial solutions: two positive, one negative, and one nodal. As a tool, we prove a Brezis-Oswald type comparison result.

The Feature Extraction for Coronavirus Disease Classification Techniques

COVID-19 has been an epidemic since the end of 2019. The number of patients with COVID-19 continues to escalate until new variants emerge. The COVID-19 detection procedure begins with detecting early symptoms, furthermore confirmed by the swab and CXR methods. The process of swab and CXR takes a relatively long time since in CXR some patients have the same symptoms as pneumonia. This study carried out the classification of COVID-19 and not COVID-19 with feature extraction techniques and classification methods. The result of this study capable to identify CXR with COVID-19 and an accuracy of 96.5%. In addition, this study even compares the classification results without using feature extraction techniques. The comparison result showed that feature extraction was able to significantly improve accuracy.

A Fast and Simple Face Detection Algorithm Using Neural Network and Its Implementation on FPGA

Face recognition is one of the interesting types of biometric which determines the presence or absence of human faces in the picture. In this paper, a face recognition system is presented that benefits from an optimized architecture based on the MLP neural network. The proposed method considerably improves the speed and the accuracy of detection compared to traditional architectures of neural network. To reduce the overall computation, neural network is organized so that to be able to rule out the majority of the non-image areas located in the image’s background before applying the main algorithm. An important advantage of this new architecture is its homogeneous structure that makes it suitable for optimized implementation on a hardware platform. In this work, FPGA is used as the platform for implementation of the proposed algorithms. The implementation was done considering Taylor expansion of the activation functions. The performance of the proposed method and the implemented system was evaluated on the BioID dataset. Accomplishment of the proposed method is high precision while reducing training time and total calculations, together with appropriate robustness. Finally, a comparison with other face recognition methods has been done to show the performance of the presented system. The comparison result shows that the proposed system outperforms the other mentioned methods.