Ship Berthing Information Extraction System Using Three-Dimensional Light Detection and Ranging Data

Safe and efficient berthing is essential to ensure maritime transportation and the safety of ships and ports. Three-dimensional (3D) light detection and ranging (LiDAR) can monitor and support ship berthing because it provides abundant target information and offers excellent advantages in measuring accuracy. Hence, a berthing information extraction system has been developed based on 3D LiDAR. Principal component analysis is used to calculate a ship’s heading and the normal vector, and the feature points of the bow and stern are determined. The segments passing through the points are obtained via region growing. The bow and stern are recognized by the similarity of the normal vector of the segments and ship’s heading according to the positions of the ship relative to the berth through visibility analysis. Qualitative and quantitative calculated analyses of the distance, velocity, and approach angle of the dynamic ship’s bow and stern relative to the dock are performed based on the feature points of 3D LiDAR data. A laser scanner, used as the detection unit, efficiently monitored the Ro–Ro ship Ocean Island berthing at Lushun Port in field experiments. On-site applications demonstrated the feasibility and effectiveness of the proposed method for the recognition of dynamic ship target and ensuring safe ship berthing.

An Ontology-Based Information Extraction System for Organic Farming

In the existing farming system, information is obtained manually, and most times, farmers act based on their discretion. Sometimes, farmers rely on information from experts and extension officers for decision making. In recent times, a lot of information systems are available with relevant information on organic farming practices; however, such information is scattered in different context, form, and media all over the internet, making their retrieval difficult. The use of ontology with the aid of a conceptual scheme makes the comprehensive and detailed formalization of any subject domain possible. This study is aimed at acquiring, storing, and providing organic farming-based information available to current and intending software developer who may wish to develop applications for farmers. It employs information extraction (IE) and ontology development techniques to develop an ontology-based information extraction (OBIE) system called ontology-based information extraction system for organic farming (OBIESOF). The knowledge base was built using protégé editor; Java was used for the implementation of the ontology knowledge base with the aid of the high-level application programming language for working web ontology language application program interface (OWL API). In contrast, HermiT was used to checking the consistencies of the ontology and for submitting queries in order to verify their validity. The queries were expressed in description logic (DL) query language. The authors tested the capability of the ontology to respond to user queries by posing instances of the competency questions from DL query interface. The answers generated by the ontology were promising and serve as positive pointers to its usefulness as a knowledge repository.