Volume 5 (2015): Edizione 3 (July 2015)

This paper proposes a new information-theoretic method based on the information enhancement method to extract important input variables. The information enhancement method was developed to detect important components in neural systems. Previous methods have focused on the detection of only the most important components, and therefore have failed to fully incorporated the information contained in the components into learning processes. In addition, it has been observed that the information enhancement method cannot always extract input information from input patterns. Thus, in this paper a computational method is developed to accumulate information content in the process of information enhancement. The method was applied to an artificial data set and the analysis of mission statements. The results demonstrate that while we were able to explicitly extract the symmetric properties of the data from the artificial data set, only one main factor was able to be extracted from the mission statement, namely, “contribution to the society”. The companies with higher profits tend to have mission statements concerning the society. The results can be considered to be a first step toward the full clarification of the importance of mission statements in actual business activities.

“Focalization” is a narrative discourse technique that produces different narrative structures based on choosing unique perspectives from which to present a story. This study designs a focalization mechanism and presents an experimental implementation. The proposed system functions as part of our integrated narrative generation system (INGS). In addition, the approach computationally extends the conceptual research of focalization by Genette to techniques for narrative generation. We define focalization as a procedure to transform a story structure into discourse structures through the following two steps: 1) restricting the scope of story information perceived from a chosen perspective, and 2) generating a discourse structure based on perceived story information. In particular, we define two types of rules for restricting the perception scope based on: a) objective perceptible possibility of constituent elements in a story and b) situations or states in which constituent elements in a story are positioned. Based on the experimentally implemented system, we present generated examples from a story using different focalization types. Through analysis, we show that the basic function of the focalization mechanism was achieved by the aforementioned rules.

Artificial Potential Filed (APF) is the most well-known method that is used in mobile robot path planning, however, the shortcoming is that the local minima. To overcome this issue, we present a deadlock free APF based path planning algorithm for mobile robot navigation. The Proposed-APF (P-APF) algorithm searches the goal point in unknown 2D environments. This method is capable of escaping from deadlock and non-reachability problems of mobile robot navigation. In this method, the effective front-face obstacle information associated with the velocity direction is used to modify the Traditional APF (T-APF) algorithm. This modification solves the deadlock problem that the T-APF algorithm often converges to local minima. The proposed algorithm is explained in details and to show the effectiveness of the proposed approach, the simulation experiments were carried out in the MATLAB environment. Furthermore, the numerical analysis of the proposed approach is given to prove a deadlock free motion of the mobile robot.

¹ Industrial Control Systems (ICS) are commonly used in industries such as oil and natural gas, transportation, electric, water and wastewater, chemical, pharmaceutical, pulp and paper, food and beverage, as well as discrete manufacturing (e.g., automotive, aerospace, and durable goods.) SCADA systems are generally used to control dispersed assets using centralized data acquisition and supervisory control.

Originally, ICS implementations were susceptible primarily to local threats because most of their components were located in physically secure areas (i.e., ICS components were not connected to IT networks or systems). The trend toward integrating ICS systems with IT networks (e.g., efficiency and the Internet of Things) provides significantly less isolation for ICS from the outside world thus creating greater risk due to external threats. Albeit, the availability of ICS/SCADA systems is critical to assuring safety, security and profitability. Such systems form the backbone of our national cyber-physical infrastructure.

Herein, we extend the concept of mean failure cost (MFC) to address quantifying availability to harmonize well with ICS security risk assessment. This new measure is based on the classic formulation of Availability combined with Mean Failure Cost (MFC). The metric offers a computational basis to estimate the availability of a system in terms of the loss that each stakeholder stands to sustain as a result of security violations or breakdowns (e.g., deliberate malicious failures).

Brain computer interface (BCI) is a system allows a user to control external devices or to communicate with other people using only his or her thoughts. The P300 speller is one such BCI in which users input letters. For inputting letters via the P300 speller, higher accuracy and shorter input times are needed, especially given densely populated display screens. We propose a new interface with a second display in the P300 speller that the user can switch to and from by selecting the “next” or “back” commands, therby reducing the density of displayed letters and improving the performance of the P300 speller. We show the comparison results in terms of accuracy and input times between the conventional interface and proposed interface.