Analysis of Language Errors in the “Larise” Magazine Article at SMP Negeri 8 Surakarta

This study aims to analyze language errors and their interpretations at the level of syntax, morphology, and Indonesian Spelling System as well as to increase knowledge and insight about how to write properly and correctly according to the language rules that have been regulated in the KBBI and PUEBI. This research uses descriptive qualitative research method. The data in this study are in the form of words (not numbers) sourced from Larise magazine in an article entitled "Philosophy of Kidungan Jawa "Ana Kidung Rumeksa ing Wengi" published on Sunday, October 11, 2020. The data collection technique in this study is a note-taking technique, namely: by reading Larise magazine as a data source. The analysis used in this study is an interactive analysis which includes the steps of a) data collection, b) error identification, c) error explanation, d) error classification, and e) error evaluation. In this study, an analysis of errors in writing rules was carried out at the level of syntax, morphology, and accuracy in the use of Indonesian Spelling (EBI). Errors at the syntactic level are in the form of errors in the use of effective sentences, errors at the morphological level are affixation errors, and errors related to Indonesian spelling include errors in using punctuation marks, using capital letters, using standard words, using prepositions, and using particles

Research on Micro/Nano Surface Flatness Evaluation Method Based on Improved Particle Swarm Optimization Algorithm

Flatness error is an important factor for effective evaluation of surface quality. The existing flatness error evaluation methods mainly evaluate the flatness error of a small number of data points on the micro scale surface measured by CMM, which cannot complete the flatness error evaluation of three-dimensional point cloud data on the micro/nano surface. To meet the needs of nano scale micro/nano surface flatness error evaluation, a minimum zone method on the basis of improved particle swarm optimization (PSO) algorithm is proposed. This method combines the principle of minimum zone method and hierarchical clustering method, improves the standard PSO algorithm, and can evaluate the flatness error of nano scale micro/nano surface image data point cloud scanned by atomic force microscope. The influence of the area size of micro/nano surface topography data on the flatness error evaluation results is analyzed. The flatness evaluation results and measurement uncertainty of minimum region method, standard least squares method, and standard PSO algorithm on the basis of the improved PSO algorithm are compared. Experiments show that the algorithm can stably evaluate the flatness error of micro/nano surface topography point cloud data, and the evaluation result of flatness error is more reliable and accurate than standard least squares method and standard PSO algorithm.

Improved algorithm for minimum zone of roundness error evaluation using alternating exchange approach

Based on the computational geometry technique, an improved algorithm for minimum zone of roundness error evaluation using an alternating exchange method is presented. A minimum zone fitting function was created to enhance the roundness error evaluation. The function uses three candidate points to determine the initial solution: the expected centre, the mean circle radius, and the corresponding zone half-width. The best solution function is designed to use the initial solution as the input to determine the optimum solution for the minimum zone circle. The proposed algorithm was validated using data available in the literature. The roundness error evaluation comparison results demonstrate that the proposed method accurately detects both the centre error magnitude and minimum zone circle and overcomes the insufficiency of using selected colinear points for four selected points.

A method for estimating the statistical error of the solution in the inverse spectroscopy problem

A method for obtaining the interval of statistical error of the solution of the inverse spectroscopy problem, for the estimation of the statistical error of experimental data of which the normal distribution law can be applied, has been proposed. With the help of mathematical modeling of the statistical error of partial spectral components obtained from the numerically stable solution of the inverse problem, it has become possible to specify the error of the corresponding solution. The problem of getting the inverse solution error interval is actual because the existing methods of solution error evaluation are based on the analysis of smooth functional dependences under rigid restrictions on the region of acceptable solutions (compactness, monotonicity, etc.). Their use in computer processing of real experimental data is extremely difficult and therefore, as a rule, is not applied. Based on the extraction of partial spectral components and the estimation of their error, a method for obtaining an interval of statistical error for the solution of inverse spectroscopy problems has been proposed in this work. The necessity and importance of finding the solution error interval to provide reliable results is demonstrated using examples of processing Mössbauer spectra.

Applying Reverse Engineering Principles on Analysis of Failures Occurred in Ship Production

In the current work, a new error evaluation methodology is introduced based on error analysis in ship production with reverse engineering data. The aim is to determine the errors and prevent or reduce the occurrence in other projects. First step is to compose a database of the errors; then, group the similar errors and calculate the Error Priority Number (EPN) by the evaluation of the predetermined criteria. The radar diagrams, which are suitable for representing a number of parameters having the same variables, were used to present the error groups in a simple way. The error groups were created on the diagram with the scores taken from the specific criteria. With the aid of the radar diagram, valuable information is given by presenting similarities and dissimilarities of these errors with other error groups. After examining the radar diagrams and evaluating the results, the cause and effect diagrams were prepared for these error groups from the field experts. Thus, the methodology should be customized for the shipyard to ensure maximum efficiency.

Methods on Detecting Closely Related Topics and Spatial Events

Topic detection is a hot issue that many researchers are interested in. The previous researches focused on the single data stream, they did not consider the topic detection from different data streams in a harmonious way, so they cannot detect closely related topics from different data streams. Recently, Twitter, along with other SNS such as Weibo, and Yelp, began backing position services in their texts. Previous approaches are either complex to be conducted or oversimplified that cannot achieve better performance on detecting spatial topics. In our paper, we introduce a probabilistic method which can precisely detect closely related bursty topics and their bursty periods across different data streams in a unified way. We also introduce a probabilistic method called Latent Spatial Events Model (LSEM) that can find areas as well as to detect the spatial events, it can also predict positions of the texts. We evaluate LSEM on different datasets and reflect that our approach outperforms other baseline approaches in different indexes such as perplexity, entropy of topic and KL-divergence, range error. Evaluation of our first proposed approach on different datasets shows that it can detect closely related topics and meaningful bursty time periods from different datasets.