# A Pilot Study of Stacked Autoencoders for Ship Mode Classification
## 1 Introduction  
In the maritime transport industry, smart ships and reducing ship greenhouse gas emissions have been actively researched [1]. Smart ships have emerged as information and communications technology, the internet of things, and big data technologies have advanced. Different from a conventional ship, a smart ship is characterized by its ability to use data collected by sensors installed within the ship to self-navigate or to provide appropriate information to assist in the decisions of crew members operating the ship [2]. Studies on reducing ship greenhouse gas emissions have mainly focused on reducing ship fuel consumption and eco-friendly ships that do not use oil. Research on smart ships has been actively pursued, and various methodologies and network architectures that can smoothly collect data from ships in operation have been proposed. This research has included areas such as big data collection systems [3], cyber security considerations [4], data management to reduce learning costs [5], framework structures for index systems [6], surveys of architectures and applications [7], and priority items for smart shipping [8]. Furthermore, various data on actual ships is being collected,which is used in research on reducing ship fuel consumption by deep learning or conventional methods to perform knowledge-free path planning with reinforcement learning [9], energy-saving auto path planning algorithms [10], energy-saving management systems for smart ships [11], power scheduling for saving energy with reinforcement learning [12], and forecasting ship fuel consumption [13]. However, the application of ship operation mode classification remains unresearched. Due to the characteristics of ship operations, the operating state of a ship can be broadly classified as At Sea, Stand By, or In Port. In the At Sea state, all of the devices on the ship are connected and powered, and the load changes in each device are small. Stand By refers to the state in which the ship is entering or exiting a port, and it is characterized by large fluctuations in the total power consumption due to changes in the ship speed and the use of auxiliary devices. Lastly, In Port refers to the operating state in which a ship has entered a port and cargo is being loaded or unloaded from the ship. In this state, fewer auxiliary devices are being powered on the ship, and total power consumption and power fluctuations are low. The power fluctuation characteristics of each ship type are as follows:  
* Container Ship: During the In Port state, the total power consumption and power fluctuations are large if many reef containers are being carried. * LNG Ship: Cargo pumps are used when loading or unloading crude oil. Therefore, the ship is under the highest power load during the In Port state. * Bulk Ship: Bulk ships with cargo cranes installed are under a very large power load during the In Port state. The operating states of ships, which are presently being acquired in large quantities, have not, however, been adequately researched. As such, researchers are faced with the problem of needing to label data manually based on ship voyage information to use the collected ship data. Research on ship state classification models that can classify the operating states of ships is required to overcome this issue. Prior studies have not addressed the development of algorithms or deep learning-based models to classify the operating states of ships. Autoencoders are used in handwriting recognition [14], anomaly detection [15], fault diagnosis [16], and fraud diagnosis [17] and produce better results in comparison with existing algorithms. Thus, we selected the autoencoder model for classifying ship data. However, the performance of the stacked autoencoder model depends on proper control of the components. When utilizing an autoencoder-based classification model, model design considerations include:  
* Which structure has better performance?