We consider a discrete event system controlled by a decentralized supervisor consisting of n local supervisors. Given a nonempty and closed language as the upper bound specification, we consider a problem to synthesize a reliable decentralized supervisor such that the closed-loop behavior is still legal under possible failures of any less than or equal to n-k (1 k n) local supervisors. We synthesize two such reliable decentralized supervisors. One is synthesized based on a suitably defined normal sublanguage. The other is the fully decentralized supervisor induced by a suitably defined centralized supervisor. We then show that the generated languages under the control actions of these two decentralized supervisors are incomparable.

In computer science, concepts of resource such as data consumption and of time such as execution time are very important. Logical systems which can treat them have been applied in that field. Linear logic has been called a resource conscious logic. The expressive power is enough to describe a dynamic change in process environments. However, linear logic is not enough to treat a dynamic change in environments with the passage of time since it does not include a concept of time directly. A typical example is the relation between linear logic and Petri nets. It is well known that the reachability problem for Petri nets is equivalent to the provability for the corresponding sequent of linear logic. But linear logic cannot naturally represent timed Petri nets which are extensions of ordinary Petri nets with respect to time concept. So we extend linear logic with respect to time concept in order to introduce a resource-conscious and time-dependent logical system, that is, temporal linear logic. This system has some temporal operators "" which means a resource usable only once at the next time, "" which means a resource usable only once at anytime, and a modal storage operator "!" which means a resource usable any times at anytime. We can show that the reachability problem for timed Petri nets is equivalent to the provability for the corresponding sequent of temporal linear logic. In this paper, we also represent the description of synchronous communication model by temporal linear logic. The expressive power of temporal linear logic will be applicable to various fields of computer science.

A new method of sequential control has been developed in order to increase the productivity and flexibility of production systems. This advanced sequential control (ASC) method is proposed for sequential control systems based on the autonomous decentralized system (ADS) architecture. The ADS defines the system software and message formats and makes it easy to expand the number of devices and software modules. The ASC method increases productivity because it minimizes the processing and adjustment times of production lines by adjusting the starting times of production processes automatically. Experimental evaluation results of the ASC method showed that it increases the productivity of production systems. It is also applied to an actual production system and the results are reported.

A distributed system is said to be self-stabilizing if it converges to some legitimate state from an arbitrary state in a finite number of steps. The number of steps required for convergence is usually referred to as the stabilization time, and its reduction is one of the main performance issues in the design of self-stabilizing systems. In this paper, we propose an automated method for computing the stabilization time. The method uses Boolean functions to represent the state space in order to assuage the state explosion problem, and computes the stabilization time by manipulating the Boolean functions. To demonstrate the usefulness of the method, we apply it to the analysis of existing self-stabilizing algorithms. The results show that the method can perform stabilization time analysis very fast, even when an underlying state space is very huge.

While a standard Kalman filter (or α-β filter) is commonly used for target tracking, it is well known that the filter performance is often degraded when the target heavily maneuvers. The usual way to accommodate maneuver is to adaptively adjust the filter gain. Our aim is to reduce the tracking error during substantial maneuvering using a combination of non-traditional "intelligent" algorithms. In particular, we propose an effective gain control using fuzzy rule followed by position error compensation via neural network. A Monte-Carlo simulation is performed for various target paths of representative maneuvers employing the proposed algorithm. The results of the simulation indicate a significant improvement over conventional methods in terms of stability, accuracy, and computational load.

This article addresses two issues. Firstly, the convergence property of conjugate gradient (CG) algorithm is investigated by a Chebyshev polynomial approximation. The analysis result shows that its convergence behaviour is affected by an acceleration term over the steepest descent (SD) algorithm. Secondly, a new CG algorithm is proposed in order to boost the tracking capability for time-varying parameters. The proposed algorithm based on re-initialising forgetting factor shows a fast tracking ability and a noise-immunity property when it encounters an unexpected parameter change. A fast tracking capability is verified through a computer simulation in a system identification problem.

In this letter, the constant amplitude transmission for orthogonal multicode systems is discussed. In order to obtain the high power efficiency, we require the high power amplifier which has non-linear characteristic. The nonlinear distortion, however, may occur because of the multicode signals having large amplitude fluctuations. If we can achieve the constant amplitude transmission, the nonlinear distortion can be neglected. In this letter, I investigate the property of the information bit streams that can achieve the constant amplitude transmission and show that the bent sequences can achieve the constant amplitude transmission.

In this study, the shielding effect of high-loss dielectric and magnetic materials themselves and also an oval human model placed behind these material, were investigated by the FDTD method, for near- and far-field exposure, using the half-wave length dipole antenna. According to the results, a high-loss magnetic material showed a large shielding effect (average 20 dB) compared to the high-loss dielectric material, for near- and far-field shields. Also, the reduction of the shielding effect was small (2 dB) for the high-loss magnetic material, while it was large for the high loss dielectric material, on decreasing the distance between the antenna and shield. Moreover, the variation of the shielding effect on a human model placed behind the shield was small (0.2-1.5 dB) for the high-loss magnetic material, but large for the high-loss dielectric material. This is similar to the results of the shield materials themselves, for the close antenna-shield and human-shield distances, respectively.

This paper describes a new individual identification system employing a novel application of the PC/SS scheme. Since the proposed system requires no magnetic information, it is robust to various changes in environment. Moreover, it is applicable to contemporary identification systems as an officer identifies one's face with the card's portrait at immigration. In addition, cooperation with existing identification systems using magnetic information and this proposed system provides more secure identification. The proposed system embeds a cardholder-related information that forms a watermark generated by the modulating manner of the PC/SS scheme into cardholder's portrait printed on the card using a simple watermarking scheme also proposed in this paper. Experimental results show that the proposed system extracts embedded information correctly as its fundamental ability. Furthermore, various properties of this system have been investigated, and it has been found that the spatial resolution of scanners is the most dominant to the performance of the proposed system.

This paper proposes the binary second-order recurrent neural networks (BSRNN) equivalent to the modified finite automata (MFA) and presents the learning algorithm to construct the stable BSRNN for inferring regular grammar. This network combines two trends; one is to transform strings of a regular grammar into a recurrent neural network through training with no restriction of the number of neurons, the number of strings, and the length of string and the other is to directly transform itself into a finite automaton. Since neurons in the BSRNN employ a hard-limiter activation functions, the proposed BSRNN can become a good alternative of hardware implementation for regular grammars and finite automata as well as grammatical inference.

Numerous scientific and engineering fields extensively utilize optimization techniques for finding appropriate parameter values of models. Various optimization methods are available for practical use. The optimization algorithms are classified primarily due to the rates of convergence. Unfortunately, it is often the case in practice that the particular optimization method with specified convergence rates performs substantially differently on diverse optimization tasks. Theoretical classification of convergence rates then lacks its relevance in the context of the practical optimization. It is therefore desirable to formulate a novel classification framework relevant to the theoretical concept of convergence rates as well as to the practical optimization. This article introduces such classification framework. The proposed classification framework enables specification of optimization techniques and optimization tasks. It also underlies its inherent relationship to the convergence rates. Novel classification framework is applied to categorizing the tasks of optimizing polynomials and the problem of training multilayer perceptron neural networks.

With the rising innovative antigens (such as intruders and viruses) through Internet, reliable security mechanisms are required to perceptively detect and put them down. However, defense techniques of the current host system over Internet may not properly analyze Internet antigens, because trends of attacks are unexpectedly shifted. In this paper, we introduce an Antibody Layer that mediates proper security services based on the biological mechanism to rapidly disclose and remove innovative antigens. The proposed Antibody Layer also employs a new topology called antibody cooperation protocol to support real-time security QoS for one host as well as host alliance.

Recently we have studied the performance of the VS/VD behavior for two binary feedback congestion control schemes, FECN and BECN, when they are applied to multicast ABR services. It is shown that the VS/VD schemes alleviate the problems of consolidation noise and consolidation delay, which are two major design issues in multicast congestion control. To complement, we further explore the VS/VD behavior for the explicit rate feedback control scheme in this letter. Through an efficient rate estimation method, it is again observed that VS/VD behavior performs better than the traditional end-to-end schemes.

This letter describes two adaptive timing synchronization schemes for a short-ranged Bluetooth system in the partial-band noise environments. One estimates the variance of the partial-band interference, which is utilized for the trigger threshold value of the inquiry scan and page scan states, while second is designed using the scaled partial correlation value for the connection state. Numerical results show the proposed synchronization algorithms are robust to the partial-band noise interference and of low complexity, which is suitable for a low-cost personal area network (PAN).

The execution model of mobile code inherits from traditional remote execution model such as telnet that needs two conditions. First, the proper program must exist in advance in the remote system. Second, there should be a process in the remote system waiting for requests. Therefore mobile code also bears the same conditions in order to be executed in a remote system. But these conditions constrain an important aspect of mobile code, which is the dynamic extension of system functionality. In this paper we propose a new approach, named Function Message that enables remote execution without these two conditions. Therefore, Function Message makes it easy and natural for mobile codes to extend system functionality dynamically. This paper describes the design of Function Message and implementation on Linux. We measure the overhead of Function Message and verify its usefulness with experimental results. On the ATM network, Function Message can be about five times faster than the traditional remote execution model based on exec().

This paper describes a method to normalize the lip position for improving the performance of a visual-information-based speech recognition system. Basically, there are two types of information useful in speech recognition processes; the first one is the speech signal itself and the second one is the visual information from the lips in motion. This paper tries to solve some problems caused by using images from the lips in motion such as the effect produced by the variation of the lip location. The proposed lip location normalization method is based on a search algorithm of the lip position in which the location normalization is integrated into the model training. Experiments of speaker-independent isolated word recognition were carried out on the Tulips1 and M2VTS databases. Experiments showed a recognition rate of 74.5% and an error reduction rate of 35.7% for the ten digits word recognition M2VTS database.

We have developed a new method to improve cooperation in concurrent systems for distributed control by using multi-agent (MA) functions. Since field terminals (FTs) work concurrently, cooperation among them is essential to the effectiveness and efficiency of the overall system. When FTs are modeled as agents, it is easy to explicitly deal with the interactions among them because those interactions can be modeled naturally as communication among agents with cooperation and negotiation. In conventional central control systems, the host computer supervises and controls all FTs in accordance with a pre-installed control algorithm. Our method instead uses intelligent field terminals (IFTs) that can evaluate the diverse information from devices of other IFTs autonomously. In the work reported here, we have evaluated the effectiveness and efficiency of our cooperative control method experimentally and have developed a system using this method to control various kinds of water delivery systems. The IFT providing MA functions that can evaluate the control parameters (CPs) and conditions of the other IFTs. If turn-around time is to be shortened, the conflicts that occur when the data processed by different IFTs is inconsistent or irregular must be resolved autonomously. Each IFT therefore cooperates with diverse functional agents (FA)s of other IFTs by using priority levels, conditions, and evaluation points in order to maintain the continuity of water delivery.

We propose a new CALL (Computer-Assisted Language Learning) system for non-native learners of Japanese using speech recognition methods. The aim of the system is to help them develop natural pronunciation by automatically detecting their pronunciation errors and then providing effective feedback instruction. An automatic scoring method based on HMM log-likelihood is used to assess their pronunciation. Native speakers' scores are normalized by the mean and standard deviation for each phoneme and are used as threshold values to detect pronunciation errors. Unlike previous CALL systems, we not only detect pronunciation errors but also generate appropriate feedback to improve them. Especially for the feedback of consonants, we propose a novel method based on the classification of the place and manner of articulation. The effectiveness of our system is demonstrated with preliminary trials by several non-native speakers.

A uniquely parsable grammar (UPG) introduced by Morita et al. is a kind of generative grammar, in which parsing can be performed without backtracking. It is known that UPGs and their three subclasses form the "deterministic Chomsky hierarchy. " In this paper, we introduce three kinds of normal forms for UPGs, i.e., Type-0, Type-1 and Type-2 UPGs by restricting the forms of rules to very simple ones. We show that the upper three classes in the deterministic Chomsky hierarchy can be exactly characterized by the three types of UPGs.

In this paper, visualized sound retrieval and categorization methods using a feature-based image search engine were evaluated aiming at efficient video scene query. Color-coded patterns of the sound spectrogram are adopted as the visualized sound index. Sound categorization experiments were conducted using visualized sound databases including speech, bird song, musical sounds, insect chirping, and the sound-track of sports video. The results of the retrieval experiments show that the simple feature-based image search engine can be effectively used for visualized sound retrieval and categorization. The results of categorization experiments involving humans show that after brief training humans can at least do rough categorization. These results suggest that using visualized sound can be effective method for an efficient video scene query.