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  1. null (Ed.)
    Analysis of large data sets is increasingly important in business and scientific research. One of the challenges in such analysis stems from uncertainty in data, which can produce anomalous results. This paper proposes a method for detecting an anomaly in time series data using a Support Vector Machine (SVM). Three different kernels of the SVM are analyzed to predict anomalies in the UCR time series benchmark data sets. Comparison of the three kernels shows that the defined parameter values of the Radial Basis Function (RBF) kernel are critical for improving the validity and accuracy in anomaly detection. Our results show that the RBF kernel of the SVM can be used to advantage in detecting anomalies. 
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  2. null (Ed.)
    This study focuses on an autonomous moving system for the automation of the harvesting process by high-performance machines in the forestry. Many fatal accidents occur due to the harvesting process. In this research, a navigation system has been developed to enable autonomous travel between accumulation sites and trees to be harvested to improve productivity and safety. A 3D map is generated by LiDAR observation, and harvester moves autonomously towards the tree as specified by the operator. A test of the harvesting process was performed in an experimental environment. The evaluation focused on the required time of the autonomous movement in the process. The effectiveness of the system was confirmed in operations such as row thinning by the results. 
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