During the last two decades, development of 3D object selection techniques has been widely studied because it is critical for providing an interactive virtual environment to users. Previous techniques encounter difficulties with selecting small or distant objects, as well as naturalness and physical fatigue. Although eye-hand based interaction techniques have been promoted as the ideal solution to these problems, research on eye-hand based spatial interaction techniques in 3D virtual spaces has progressed very slowly. We propose a natural and efficient spatial interaction technique for object selection, which is motivated by understanding the human grasp. The proposed technique, gaze-grasp pose interaction (GG Interaction), has many advantages, such as quick and easy selection of small or distant objects, less physical fatigue, and elimination of eyehand visibility mismatch. Additionally, even if an object is partially overlapped by other objects, GG Interaction enables a user to select the target object easily. We compare GG Interaction with a standard ray-casting technique through a formal user study (participants = 20) across two scenarios. The results of the study confirm that GG Interaction provides natural, quick and easy selection for users.

Most wind turbines are monitored and controlled by supervisory control and data acquisition systems that involve remote communication through networks. Despite the flexibility and efficiency that network-based monitoring and control systems bring, these systems are often threatened by cyberattacks. Among the various kinds of cyberattacks, some exploit the system dynamics so that the attack cannot be detected by monitoring system output, the zero-dynamics attack is one of them. This paper confirms that the zero-dynamics attack is fatal to wind turbines and the attack can cause system breakdown. In order to protect the system, we present two defense strategies using a generalized hold and a generalized sampler. These methods have the advantage that the zeros can be placed so that the zero dynamics of the system become stable; as a consequence, the zero-dynamics attack is neutralized. The effects of the countermeasures are validated through numerical simulations and the comparative discussion between two methods is provided.

We consider the Kalman-filtering problem with multiple sensors which are connected through a communication network. If all measurements are delivered to one place called fusion center and processed together, we call the process centralized Kalman-filtering (CKF). When there is no fusion center, each sensor can also solve the problem by using local measurements and exchanging information with its neighboring sensors, which is called distributed Kalman-filtering (DKF). Noting that CKF problem is a maximum likelihood estimation problem, which is a quadratic optimization problem, we reformulate DKF problem as a consensus optimization problem, resulting in that DKF problem can be solved by many existing distributed optimization algorithms. A new DKF algorithm employing the distributed dual ascent method is provided and its performance is evaluated through numerical experiments.

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