論文

The authors propose a "Boxed Economy Simulation Platform", which is a sharable basis for agent-based economic simulations. By providing the basic design of the social model, which we call "Boxed Economy Foundation Model", it enables collaborative research more efficiently. Sharing and cumulating the model components can be promoted by domain-specific design at the level of social model rather than the level of abstract general purpose model. It will be able to contribute to remove factors that have been making it difficult for social scientists to participate in and conduct agent-based research.

In this paper, the concept of Boxed Economy model is proposed. Boxed Economy model is an agent-based microeconomic simulation which is intended to make predictions and explanations of the real economic movements in the general economy. We suggest the importance of reusable design with object-oriented framework in the field of agent-based economic simulation. Two frameworks at the different level are described in this paper: "Boxed Simulator Framework" and "Boxed Economy Framework". The source code and model is going to be open to public for sharing and co-improving.

The field of remote sensing and sensor technology has undergone tremendous development in the past decades. Sensors technologies of all kinds such as electro-optics, acoustic, active/passive UV to LWIR, ground-penetrating radar, passive mm wavelength, X-ray tomography, neutron activation imaging, multi-spectral, hyper-spectral, and ultra-spectral imaging, will provide valuable images that normal CCD camera cannot offer. By combining algorithms and images taken by sensors at different part of the electromagnetic spectrum, we will be able to extract valuable images automatically. By using multi-spectral images and processing them with neural network computing, our `Third Eye' team is able to extract human face features from those images. In this paper, we will present an application for detecting human facial parts, images taken by different imaging systems and sensors, and the current status of image processing applications.

In this paper we present an adaptive detection and coordination system which consists of anomaly and misuse detector combined by lightweight neural networks to synchronize with specific data control of proxy server.The proposed method is able to correct false positive of anomaly detector for the unusual changes in the segment monitored by the subsequent misuse detector. The orthogonal outputs of these two detectors can be applied for the switching condition between the parameter settings and the protective data modification of proxy. In the unseen attacks our model detects, the forwarding delay time set in the proxy server according to the detection intervals enable us to protect the system faster and prevent effectively the malicious code from spreading.

In this paper, we present a new clustering method for segmentations of moving target and non-target objects. We assume that the moving target object has the following conditions: (1) object motion data continuity inter-frame, and (2) object motion data continuity intra-frame. In our model, clusters tend to form as filling these two conditions. The experimental results showed the effectiveness of the proposed algorithm and the performance of this model in terms of the quality of the recognition results. Our algorithm is able to clean the input noise by removing non-target objects before the recognition process.

A spectroscopic camera has been developed which has spectral resolution of less than 1.5nm in the ultraviolet (UV) and visible wavelength bands (320-580 nm). Its main components are a specially coated UV objective lens, a UV Acousto-Optic Tunable Filter (AOTF) with a thermo-electric cooling system, and a imaging system based on a high-gain avalanche rushing amorphous photoconductor (HARP) developed by NHK Science and Technical Research Laboratories. Research is currently under way to develop the hyperspectral camera into a sensor package for airborne and ultimately space-based remote sensing applications. This paper presents the basic principle and configuration of the hyperspectral camera, and gives details of tests to measure its performance. The results of spectral resolution tests analyzing very close two spectra from a helium-discharge lamp demonstrate the camera's high spectral resolution performance. Full color and spectral images obtained by a spectrometry experiment are also presented to demonstrate the camera's hyperspectral capabilities.

In this paper, we propose a new clustering method for the first phase of a two-phase method of the vehicle routing problems (VRPs) and the traveling salesman problems (TSPs). In the first phase, the customers are grouped as several delivery areas for vehicle by using maximum neuron model. In the second phase, the TSPs for each areas are solved by using elastic net model proposed by Andrew et al. Conventional maximum neuron model proposed by Takefuji et al. is not suitable for these continuous problems. But by including a self-organization rule to this model, the solution quality is improved. Our simulation results show that maximum neuron model can achieve to obtain better solutions than other methods for some kinds of problems in VRPs and TSPs.

The rapid increase of software vulnerabilities shows us the limitation of patch-dependent countermeasures for malicious code. We propose a patch-independent protection technique of remote infection which enables each process to identify itself with "being infected" and nullify itself spontaneously. Our system is operating system independent and therefore does not need software rebuilding. Previously, no method for stopping malicious process without recompiling source code or re-building software has been proposed. In proposal system, target process is running under self debugging mode which is activated by enhancing debug() exception handler and utilizing MSR debug register. In this paper we show the effectiveness of proposal method by protecting the remote process infection without patching security holes. Implemention of device driver call back function and BranchIP recorder provides the real-time prevention of unregistered worm attack through Internet. In experiment, function test of stack buffer overflow of Win32.SQLExp.Worm is presented. Also CPU utilization corresponding to the number of calling function and some database operations is showed.

The issues that must be addressed before commercial database management systems can be used to manage VLSI CAD data are defined. A survey is presented of approaches addressing four of the defined issues: design hierarchies and multilevel representations, design alternatives and version control, common interface between cell libraries, and efficient cell selection based on given design constraints. A frame-based model is considered as a case study of the special-purpose design database management system approach. This framework for capturing design data is based on semantic networks. It is well suited for application-specific ICs, yet general enough for other CAD/CAM environments. Benchmark results for the selection algorithms that run on top of the frame-based database system are presented.

The authors address the implementation of a fuzzy simulator (FSIM) and discuss architectures for a general-purpose VLSI fuzzy-inference processor. The FSIM tool aids in the rapid prototyping of fuzzy production system (FPS), and represents a convenient transitional step in the implementation of an FPS on silicon. They present a brief theoretical review of fuzzy reasoning, introduce the FSIM, and discuss FPS development using the FSIM. An overall picture of the various stages involved in developing a fuzzy-inference processor is provided. The authors then outline the general architecture of a VLSI inference processor for FPSs. To further illustrate the development of a fuzzy inference processor, they describe an FPS example from conceptualization to implementation on silicon chips.

Fuzzy inference engines based on the existing fuzzy theory are inadequate to perform reliable decision making. Besides requiring the fuzzy sets and data to be normalized, the inference engine is also sensitive to noise in observational data. Inaccurate conclusions are produced if noise is present and also when the fuzzy sets are not normalized. In this paper, a new term 'similarity' (σ) and the method to compute σ to enhance the capability of fuzzy set theory for application in expert systems is introduced. Even though the complexity of the hardware engine is slightly increased, it actually reduces the overhead of computation by eliminating the need for normalization of fuzzy data. With reliable fuzzy data manipulation, it is easy to extend to a multi-dimensional membership function which has a wider scope of applications. To To implement the Very Large Scale Integration fuzzy inference engine, two general schemes of the hardware architecture that van be easily reconfigured to satisfy given performance requirements are discussed.

A parallel and stochastic version of Hopfield-like neural networks is presented. Cauchy color noise is assumed. The specific noise is desirable for fast convergence to a fixed point representing a neighborhood minimum. It can be quickly quenched at each iteration according to a proven cooling schedule in generating random states on the energy landscape. An exact Cauchy acceptance criterion is analytically derived for hill-climbing capability. The improvement is twofold: a faster cooling schedule (the inversely linear cooling schedule characterized by the Cauchy simulated annealing) and parallel executions of all neurons. Such a Cauchy machine can be electronically implemented, and the design is given.