AN ERROR ANALYSIS ON SENTENCE TYPES BASED ON STRUCTURE IN ATMARITA’S SHORT STORY“ WHEN BIRUNI REMEMBERS IT”

Errors may occur in different components of language, such as grammar, pronunciation, and vocabulary. Errors can occur because people have not internalized the grammar of the second language. This study aimed to get the answer of research problems, namely (1) What kinds of sentence types that are used in short story in “Reform” magazine No (19,2001)?, (2) What kinds of errors in sentence types that are found in the object of the study? and (3) What is the most dominant error that are found in this investigation?. This thesis used descriptive research design. Besides, the thesis writer used a documentary analysis and selected one short story “When Biruni Remembers It” in “Reform” magazine No (19,2001) as the object of the study. In analyzing the data, the writer investigated the kinds of sentence types based on structure and the kinds of errors in sentence types that are found in the object of the study by taking some steps: (1) Classifying the kinds of sentence types based on structure that are used in short story in “Reform” magazine No (19, 2001), (2) Classifying the kinds of errors in sentence types that are found in short story, (3) Describing the kinds of sentence types based on structure that are found in short story, (4) Describing the kinds of errors in sentence types based on their classification, and (5) Describing the most dominant error in sentence types that is found in short story. From the research findings and discussions, it can be concluded that all the types of sentence based on structure are used in the short story. They are: two simple sentences, nine compound sentences, seven complex sentences, and third teen compound complex sentences. All of them are 31 sentences. In addition, in terms of errors, Oshima’s theory was used in this investigation, that is errors of sentence fragments, errors of choppy sentences, and errors of stringy sentences. Based on the result of the data analysis, it is found out that there are two errors of choppy sentences. However, there is no error of sentence fragments or error of stringy sentences. Therefore, it is concluded that the most dominant error is error of choppy sentence

Training a Fast Object Detector for LiDAR Range Images Using Labeled Data from Sensors with Higher Resolution

In this paper, we describe a strategy for training neural networks for object detection in range images obtained from one type of LiDAR sensor using labeled data from a different type of LiDAR sensor. Additionally, an efficient model for object detection in range images for use in self-driving cars is presented. Currently, the highest performing algorithms for object detection from LiDAR measurements are based on neural networks. Training these networks using supervised learning requires large annotated datasets. Therefore, most research using neural networks for object detection from LiDAR point clouds is conducted on a very small number of publicly available datasets. Consequently, only a small number of sensor types are used. We use an existing annotated dataset to train a neural network that can be used with a LiDAR sensor that has a lower resolution than the one used for recording the annotated dataset. This is done by simulating data from the lower resolution LiDAR sensor based on the higher resolution dataset. Furthermore, improvements to models that use LiDAR range images for object detection are presented. The results are validated using both simulated sensor data and data from an actual lower resolution sensor mounted to a research vehicle. It is shown that the model can detect objects from 360{\deg} range images in real time