超音波診断装置の特徴であるグレースケールによる断層像表示は，輝度情報の中に含まれる重要な診断情報を表現出来ることであり，それが医療現場における超音波診断装置の大きな信頼に繋がっている．本解説は，輝度表示であるBモード法の歴史に触れながら，その基礎的な原理と特徴について説明をする．また，超音波自体の減衰との関係や，肝実質に見られるスペックルパターンの発生メカニズムについても，技術的観点から説明をする．また，最後に最新技術としての組織弾性計測についての概説も付け加えたい．

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We developed a new computer language that describes three-dimensional graphical objects for visual simulation. The language, 3D-Talk, is a powerful means for visualizing the process of protein folding simulation. 3D-Talk is designed as an object-oriented language, with which users can create, manipulate, and edit graphical objects using commands with a syntax like a natural language.
We developed two novel applications for molecular biology using 3D-Talk. One is ProView, a protein visualization tool. ProViewconverts the data of PDB (Protein Data Bank) into 3D-Talk statement. Another is a visual simulation system for protein folding. It simulates the motion of protein folding using simulated annealing techniques and Hidden Markov Models.
In short, 3D-Talk serves as a powerful animation tool.

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This paper proposes a new methodology to improve the performance of multiple sequence alignment by combining a genetic algorithm and an iterative alignment algorithm. Iterative alignment algorithms usually achieve better alignment than other alignment algorithms, such as tournament based multiple alignment. They, also, can incorporate parallelism to improve execution performance. However, they sometimes suffer from being trapped in the local optima and result in relatively low-quality alignments due to their rapid convergence. A genetic algorithm can save this problem by exchanging partial alignment sequences between “individuals”. Our experiments show that the combination of a genetic algorithm and an iterative alignment algorithm produces better results than iterative aligners which employ hill-climbing search strategies.

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We have developed an alignment editor for protein sequences and/or nucleotide sequences that runs under the on X-window system. The system is equipped with automatical aligner and motif dictionary. The system can iteratively identify known motifs stored in the motif dictionary and gradually improve the quality of the alignment to discover new motifs.

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We are researching a mobile and sensor access platform network for the future called NerveNet, which accommodates ubiquitous sensors and provides services in local areas. The realization of reliable and accountable information collection, processing and provision over NerveNet poses a challenging and fundamental issue in promotion of the sensor business field. As a first step toward a reliable NerveNet, we investigate privacy preservation and the collection of reliable sensor information for prospective personalized sensor applications. The privacy requirement impels the logic separation between sensor networks and the communication platform in the design of NerveNet architecture. To enable sensor network users (SNUs) to reliably interact with sensors managed by different sensor network owners (SNOs), we designed a secure sensor sharing framework (S3F) based on two business models — the Ad Hoc Sales Model (AHSM) and Shopping Center Sales Model (SCSM). With S3F-AHSM, an SNU acquires permission from an SNO each time she wants to obtain information from a sensor. On the other hand, with S3F-SCSM, an SNU can obtain the access privilege to a flexible set of sensors based on the queried preferences via a third party called a sensor network service provider (SNSP). In S3F-SCSM, SNSPs that share the sensors owned by various SNOs have the ability to search the preferred sensors and provide the authorization certificate to the SNUs.

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第5世代コンピュータープロジェクトの目標と基本的アプローチを概観し, 研究・開発の現状と今後の展開を紹介する.視点を中心部に絞り, 大きく6つの研究・開発の分野について述べる.分野は言語, すなわち核言語 (第0版), 核言語 (第1版), 知識表現言語, 自然言語, 論理型プログラム言語, 数学言語の6つを手掛りに分類する.

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Electrical energy storage (EES) devices are at the core of the environmental technologies that are highly influential in advancing our life in a future society. Among different EES technologies, electric double layer capacitors (EDLCs) are considered as promising devices due to their high-power, safe and long-lived characteristics. One of the approaches to further enhance the cell voltage and energy density of EDLCs while maintaining their high power is to replace the activated carbon with ultrafast lithium ion battery materials. Increasing their cell voltage and energy density contribute to decrease a number of serial cell connection and a volumetric package, respectively. In this article, we introduce examples of the synthesis of several nanomaterials using our original ultracentrifugation process, allowing the in-situ growth of active materials onto carbon surface enables ultrafast electrochemical response for 2^nd and 3^rd generation supercapacitors.

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The primary challenges faced by wireless sensor networks are how to construct the shortest spanning tree and how to determine the optimal sink node position in terms of minimizing the data transmission times and their variances for data gathering from all sensor nodes to a sink node. To solve these two problems, we propose a novel algorithm that uses the polygonal affine shortening algorithm with flow aggregation. This algorithm enables a wireless sensor network that has movable sensor nodes and one movable sink node to self-organize the shortest spanning tree and self-determine the optimal sink node position in a fully distributed manner. We also show that our algorithm is faster than the existing shortest path algorithm in terms of computational complexity.

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In distributed spectrum sensing, spatially distributed sensors perform radio frequency (RF) sensing and forward the result to a fusion center (FC). Cognitive radio (CR) obtains spectral information from the FC. Distributed spectrum sensing facilitates reliable discovery of spectrum opportunity while providing enhanced protection to legacy systems. The overall performance of distributed spectrum sensing depends both on the quality of sensing at the individual sensors and the forwarding scheme from the individual sensors. In this aspect the choice of media access control (MAC) plays a significant role. We can improve the system performance by optimizing the MAC and the spectrum sensing parameters jointly. In this paper we propose an enhanced MAC scheme based on existing scheduled MAC protocols to yield a high performance distributed spectrum sensing. To demonstrate our idea, we provide computer simulation by considering energy detection based distributed spectrum sensors and IEEE 802.15.4 PHY and MAC parameters.

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Due to the advancement of software radio and RF technology, cognitive radio(CR) has become an enabling technology to realize dynamic spectrum access through its spectrum sensing and reconfiguration capability. Robust and reliable spectrum sensing is a key factor to discover spectrum opportunity. Single cognitive radios often fail to provide such reliable information because of their inherent sensitivity limitation. Primary signals that are subject to detection by cognitive radios may become weak due to several factors such as fading and shadowing. One approach to overcome this problem is to perform spectrum sensing by using multiple CRs or multiple spectrum sensors. This approach is known as distributed sensing because sensing is carried out through cooperation of spatially distributed sensors. In distributed sensing, sensors should perform spectrum sensing and forward the result to a destination where data fusion is carried out. Depending on the channel conditions between sensors (sensor-to-sensor channel) and between the sensor and the radio (user-channel), we explore different spectrum sensing algorithms where sensors provide the sensing information either cooperatively or independently. Moreover we investigate sensing schemes based on soft information combining (SC), hard information combining (HC). Finally we propose a two-stage detection scheme that uses both SC and HC. The newly proposed detection scheme is shown to provide improved performance compared to sensing based on either HC or SC alone. Computer simulation results are provided to illustrate the performances of the different sensing algorithms.

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Hypothesis-based reasoning is a basic technique for building AI systems based on incomplete knowledge. The hypothesis-based reasoning system derives conclusions despite the absence of total domain knowledge. When we modify the knowledge base, the beliefs of the conclusions should be partially revised on the system. An assumption-based truth maintenance system (ATMS) is a hypothesis-based reasoning system whose inputs are a set of propositional Horn clauses and a set of assumptions. The ATMS is often used with a problem solver because we need logical variables to represent domain knowledge. APRICOT/0, a hypothesis-based forward-reasoning system, consists of a compiler, the Rete-based inference engine and the ATMS. The compiler translates first-order Horn clauses and normal defaults into a Rete-like network. The Rete-like network consists of a root node,one-input nodes, two-input nodes and terminal nodes. Allowing faster hypothesis-based reasoning, the inference engine gives intermediate justifications to the ATMS at two-input nodes and store the sets of hypotheses where those intermediate data hold. However, the whole of the input knowledge base has to be recompiled and the previous beliefs of the conclusions have to be revised on the whole when the input knowledge base is partially modified by the user. Therefore, both an incremental compiler and an incremental inference system are required. This paper presents an incremental hypothesis-based reasoning system which includes an incremental compiler and an incremental inference system. The incremental compiler is implemented with a modified ATMS which is called RATMS. The RATMS can efficiently offer the inference engine the updated Rete-like network. The incremental inference system can partially revise previous beliefs of conclusions with additional knowledge including variables. The incremental hypothesis-based reasoning system has been applied to a logic circuit design problem. The expermental result shows that beliefs of the solutions are efficiently revised with additional knowledge about datapath design.

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Hypothetical reasoning is a basic technique for building AI system based on incomplete knowledge. Knowledge bases include hypotheses that are not always valid. Systems for hypothetical reasoning usually take a lot of time because they need to maintain the consistency of knowledge bases. This paper presents an efficient method for hypothetical reasoning on a forward-chaining system with the assumption-based truth maintenance system (ATMS). The forward-chaining system consists of a compiler of Horn clauses and normal defaults into a Rete-like network and a Rete-based inference engine. The Rete-like network as a flow graph consists of a root node, one-input nodes, two-input nodes and terminal nodes. The Rete algorithm is an efficient method for matching a large collection of objects with many conjunctive patterns. The inference engine is extended to reason in multiple contexts without conflict resolution. It gives justifications that are propositional Horn clauses to the ATMS. In the ATMS, each datum is labeled with a collection of sets of assumptions where the datum holds. The feature of a proposed reasoning method is that the inference engine gives intermediate justifications to the ATMS and stores intermediate dependent assumptions of two-input nodes, allowing faster hypothetical reasoning. By means of this method, the APRICOT/0 system for hypothetical reasoning has been implemented on the PSI-II machine (Personal Sequential Inference machine) in ESP (Extended Self-contained Prolog). An experimental result shows that APRICOT/0 is about seven times faster than a system that neither gives intermediate justifications to the ATMS nor stores the intermediate dependent assumptions under a tested knowledge base. The compared system, called SCS, is a simple combination system of a conventional inference engine and the ATMS. The cost of total ATMS label computations on APRICOT/O is generally less than the cost of the compared system in the following cases : (1) When the Rete-like network includes a two-input node shared by some clauses or defaults. (2) When a one-input node passes tokens to its successor that is a successor of another two-input node.

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This paper describes Expert Model, a knowledge representation for knowledge acquisition and Pre-post method, a knowledge acquisition method based on the expert model. The expert model consists of operations which represent small tasks in human experts' problem solving processes. We have prepared generic operations for effective model building. We assumed that there is some representation technique in real knowledge bases because knowledge engineers build them, although the production rules are in a very simple and general form. We have obtained seven types of generic operations by analyzing real knowledge bases which are written for diagnosis problems in production rules. The pre-post method has two interview strategies. It stimulates a human expert to remember by ask what operations are done before and after an operation. It extracts knowledge about operations efficiently, according to the operation type. This paper also introduces a knowledge acquisition support system, EPSILON/One, based on the expert model and the pre-post method. It has been implemented on PSI (Personal Sequential Inference machine) in ESP (Extended Self-contained Prolog). It consists of a human interface for interview, a knowledge elicitation module, a knowledge refinement module, and a knowledge translation and inference module. The interface supports graphic multi-windows and a mouse. The knowledge elicitation module extracts knowledge in the form of the expert model by the pre-post method. The knowledge refinement module supports to detect a lack of knowledge. The knowledge translation and inference module translates the expert model into knowledge in the form of ESP language and supports to evaluate the knowledge in inference processes.

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