Low-Rate DoS (LDoS) attacks degrade the quality of service with less traffic than ordinary DoS attacks. LDoS attacks can easily evade conventional counter-DoS detection mechanisms because their time-averaged flow is small. Thus, LDoS attack is getting a serious problem nowadays. With the recent spread of IoT devices, Zigbee, one of the IoT communication standards, attracts much attention. Zigbee is a low-power wireless communication protocol at the sacrifice of its transfer range and bandwidth. Since Zigbee consumes low power, it is widely adopted for inexpensive small sensors and IoT that run on batteries. The advantage of the low power consumption of Zigbee is due to its use of indirect transmission. However, we newly found that vulnerability to LDoS attacks existed in the indirect transmission. In this study, we first point out this vulnerability and describe new LDoS attack scenarios exploiting it. Next, we propose simple countermeasures against them which require low computational cost. Then, simulation experiments are conducted to evaluate the impact of the attacks and the effectiveness of the proposed countermeasures. The experimental results show that the new LDoS attacks can decrease the normal packet arrival rate to 0% by adjusting the timing of attacks. It is also revealed that our proposed countermeasures successfully prevent such cases and improve the minimum arrival rate to 80% at best.

This paper proposes structured location, a semiautomatic technique and its supporting tool both for locating features and exposing their structures in source code, using a combination of dynamic analysis, sequential pattern mining and formal concept analysis.

The integrity in components of an operating nuclear power plant (NPP) is usually monitored on a daily basis by operator patrols. Currently, there is a pressing need to replace such human patrol activities by systems of automated monitoring in order to reduce radiation exposure and the workload imposed on operators. From this perspective, and under the sponsorship of the Ministry of International Trade and Industry (MITI), we embarked upon a R&D project in 1991 with the objective of developing TOSHIBA Remote Inspection System (TOSRIS) for NPPs. We built a prototype system and examined it in a mockup of a typical NPP environments. This project was completed in 1996. This paper describes the concept of TOSRIS, how the inspection position is calculated, how plans are passed using a 3D-CAD database, and mockup tests.

The integrity of components of an operating nuclear power plant (NPP) is usually monitored on a daily basis by operator patrols. Currently, there is a great need to replace such human patrol activities by systems of automated remote monitoring in order to reduce radiation exposure and the severe workload imposed on operators. From this perspective, and under the sponsorship of Ministry of International Trade and Industry (MITI), the authors embarked upon a R&D project in 1991 with the objective of developing an autonomous mobile patrol system for NPPs. The project is scheduled to be completed in 1996. This paper describes the concept of the autonomous mobile robot system, which comprises a hierarchical autonomous mobile control system, a localization system based on pattern matching between the data profile from a laser rangefinder and an environment map, a method of constructing environment maps from 3D-CAD of a NPP, and a cableless robot system.

The authors describe the basic design of Fun-ARM, and discuss the strategy of the fault tolerance on distributed manipulator. The fault model is described, and assumptions in the system are discussed. The methods to realize fault tolerance are proposed, including methods for fault-detection, fault-localization and fault-containment.

A robot vision system that automatically generates an object recognition strategy from a 3D model and recognizes the object using this strategy is presented. The appearance of an object from various viewpoints is described in terms of visible 2D features such as parallel lines and ellipses. Features are then ranked according to the number of viewpoints from which they are visible. The rank and feature extraction cost of each feature are used to generate a treelike strategy graph. This graph gives an efficient feature search order when the viewpoint is unknown, starting with commonly occurring features and ending with features specific to a certain viewpoint. The system searches for features in the order indicated by the graph. After detection, the system compares a lines representation generated from the 3D model with the image features to localize the object. Perspective projection is used in the localization process to obtain the precise position and attitude of the object, whereas orthographic projection is used in the strategy generation process to allow symbolic manipulation. Experimental results are given.>

A vision system is presented which automatically generates an object recognition strategy from a 3D model, and recognizes the object using this strategy. In this system, the appearances of an object from various viewpoints are described with visible 2D features, such as parallel lines and ellipses. Then, the features in the appearances are ranked according to the number of viewpoints from which they are visible. The rank and the feature extraction cost for each feature are considered to generate a tree-like strategy graph. It shows an efficient feature search order when the viewpoint is unknown, starting with commonly occurring features and ending with features specific to a certain viewpoint. The system searches for features in the order indicated by the graph. After detection, the system compares the line representation generated from the 3D model and the image features to localize the object.>