Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告

Industrial automation based on cyber-physical systems technologies

Advancements in the field of autonomous underwater vehicle

Precision fish farming: A new framework to improve production in aquaculture

Farming of Atlantic salmon in exposed areas poses unique challenges to operations, structures and equipment due to severe and irregular wind, wave and current conditions, and sheer remoteness. Many of the operational challenges seen at present sheltered sites are likely to be amplified when moving production to more exposed locations. There is, however, a strong Norwegian industrial interest in utilizing such areas. A new research center, the Exposed Aquaculture Operations center has been initialized to develop competence and technology to address the challenges. Six core research areas are identified that will be crucial to address the challenges with exposed farming, with a focus on the industrial status in Norway. Four areas target technological innovations that will enable safe and reliable exposed aquaculture operations: 1) Autonomous systems and technologies for remote operations, 2) Monitoring and operational decision support, 3) Structures for exposed locations and 4) Vessel design for exposed operations. Two areas represent core requirements for sustainable production: 5) Safety and risk management and 6) Fish behavior and welfare. This paper describes the research needs and the research strategy planned for the Exposed Aquaculture Operations center.

Exposed Aquaculture in Norway

Risk Management of Autonomous Marine Systems and Operations

Towards autonomy in ROV operations

Finite element cable-model for Remotely Operated Vehicles (ROVs) by application of beam theory

Attitude estimation by multiplicative exogenous Kalman filter

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