It is wrong to consider that the more difficult the papers are, the more valuable. We should write a paper in a top-down manner, starting from the central idea that explains the originality of the paper down to the details.
In the abstract, we should state the originality, the new approach, and the contribution of the paper. In the introduction, we should explain the problems of the previous work citing references and explain what is the originality of the paper and how to solve the problems in a new way. In the body of the paper, we should discuss the idea more elaborately, if possible, using figures and then move to the details. In the conclusions, we should state the method and contribution clearly.
In presenting a paper, we should explain the idea by figures not resorting to mathematical equations.

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In this paper, we propose a thresholding algorithm. This algorithm works under a severe constraint: each pixel in a processed image must be derived from only information of its neighboring pixels. This constraint is very important for a low cost device such as a mobile camera, because it makes possible to process each pixel in parallel. The proposed algorithm deals with gray-scale images, and determines the threshold based on edge information. The proposed algorithm is represented by local and parallel image processing and has been tested using 104 scenery images. The result shows that the proposed algorithm can binarizing images.

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This paper presents a two-stage self-organizing map algorithm what we call Two-stage SOM which combines Kohonen's basic SOM (BSOM) and Aoki's SOM with threshold operation (THSOM). In the first stage of Two-stage SOM, we use BSOM algorithm in order to acquire topological structure of input data, and then we apply THSOM algorithm so that inactivated code-vectors move to appropriate region reflecting the distribution of the input data. Furthermore, we show that Two-stage SOM can be applied to clustering problems. Some experimental results reveal that Two-stage SOM is effective for clustering problems in comparison with conventional methods.

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Conventional energy management systems (EMS) for factories have been developed to minimize energy costs. However we have to reduce energy consumptions and CO₂ emissions to preserve our earth environment. This paper proposes a multi-objective optimization method based on particle swarm optimization (PSO) to minimize energy costs and CO₂ emissions, and presents its effectiveness through simulation results.

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In a previous study, we proposed a step-up training method for the multitrailer truck control system using neurocontrollers (NCs) evolved by a genetic algorithm (GA) and showed its efficiency. However, the method does not enable the training of NCs for a five-trailer connected truck system. In this paper, we present a new version of the step-up training method that enables the training of NCs for a five-trailer connected truck system. The proposed method is as follows: First, NCs are trained only to avoid the “jackknife phenomenon". Second, NCs are trained for minimizing squared errors starting from easy initial configurations. Finally, NCs are trained for minimizing the squared errors starting from more difficult initial configurations. The difficulty of training steps increases gradually. To improve training performance, we applied a recessive gene model to network weight coding and genetic operations. In this study, we applied the recessive gene model to the classic exclusive-or (XOR) training problem and showed its convergence performance. The GA training of NCs with the recessive gene model maintains diversity in the population and avoids evolutionary stagnation. Simulation shows that NCs with the recessive gene model and the proposed step-up training method are useful in the controller design of the multitrailer system.

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A charge-average type DC-DC converter for cellular phones is proposed in this paper. The converter is designed by using switched-capacitor (SC) techniques. In the proposed power converter, the DC-DC conversion is performed by iterating the following processes. Firstly, an input voltage V_in is charged in capacitors which are connected in series. In this timing, an output voltage is taken out from arbitrary terminals of the capacitors. Next, the electric charges are equalized by connecting the capacitors in parallel. Therefore, different from conventional converters such as a series-parallel type converter and a Dickson-type converter, the proposed converter can provide stepped-down and stepped-up voltages which are expressed by (Q/P)×V_in (P ∈ {1, 2,..., N} and Q ∈ {1, 2,..., N}). Furthermore, the hardware cost for the proposed converter is smaller than that for the conventional SC power converters such as a ring-type converter and a series-parallel type converter. Concerning 3-stage power converters, SPICE simulations are performed to confirm the validity of the circuit design. For the input voltage 3.6 V, the power efficiency of the proposed circuit is about 90 % in the output current about 300 mA.

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In this paper, we propose an algorithm to estimate sleep quality based on a heart rate variability using chaos analysis. Polysomnography(PSG) is a conventional and reliable system to diagnose sleep disorder and to evaluate its severity and therapeatic effect, by estimating sleep quality based on multiple channels. However, a recording process requires a lot of time and a controlled environment for measurement and then an analyzing process of PSG data is hard work because the huge sensed data should be manually evaluated. On the other hand, it is focused that some people make a mistake or cause an accident due to lost of regular sleep and of homeostasis these days. Therefore a simple home system for checking own sleep is required and then the estimation algorithm for the system should be developed. Therefore we propose an algorithm to estimate sleep quality based only on a heart rate variability which can be measured by a simple sensor such as a pressure sensor and an infrared sensor in an uncontrolled environment, by experimentally finding the relationship between chaos indices and sleep quality. The system including the estimation algorithm can inform patterns and quality of own daily sleep to a user, and then the user can previously arranges his life schedule, pays more attention based on sleep results and consult with a doctor.

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Non-destructive inspection by the electromagnetic wave (Radar) conventionally makes use of the shaded images on the magnitude variation of the reflected waves, i. e., B-mode images, and anomaly diagnosis has been qualitatively done with inspector's eye. This yielded the diagnosis of low reliability and low accuracy.
The paper proposes two diagnosis methods of high reliability and high accuracy based on the received signal obtained as a time-series. One is the method which evaluates the similarity of the peak pattern of the received signal with a standard one when the radar is crossed over an iron bar. Another is the one which evaluates the symmetricity and the continuity of the peak pattern. By the peak pattern meant is the location of “mountains and valleys" of the received signal, where the mountains and valleys represent respectively the relative maxima and minima of the received signal.

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It proposes the technique of the augmented template matching for positioning the object that has two-dimensional standard shape under the outdoor environment. First of all, a whole template and some partial templates of the object are prepared. Moreover, a lot of images except the object and the object are prepared for the training data, and the linear discriminant function is decided by Fisher's linear discriminant method. Some similarities of an unknown image are calculated with the whole and partial templates. Next, similarities are substituted for the linear discriminant function, and the discriminant score is calculated. Finally, the discriminant score is judged whether it is the object by the threshold. The effectiveness of the proposed technique is shown by the corner fittings detection experiment of the container under the outdoor environment.

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This paper considers the robust stabilization of uncertain multimodeling systems. The guaranteed cost control technique is used to choose the design parameter which is included in the control gain. The ε-independent quadratically stabilizing controller is newly proposed. It is shown that if the reduced order algebraic Riccati equations (AREs) have positive definite stabilizing solution then the closed-loop uncertain multimodeling systems with the proposed controller are quadraticaly stable and have the cost bound. To show the effectiveness of the proposed algorithm, numerical example is included.

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An approach to control-oriented uncertainty modeling is presented for a class of elastic vibrating systems such as flexible structures, beams and strings, described by partial differential equations. Uncertainty bounding techniques are developed using upper and lower bounds of the unknown eigenparameters. The result forms a basis for a finite-dimensional controller design in which closed loop stability and performance are guaranteed. A feasible set of systems is defined of all systems governed by a class of differential equations with certain norm bounds of unknown input and output operators and with partially known bounds of eigenparameters. Then the perturbation magnitude covering the feasible set is evaluated in frequency domain where a standard truncated modal model is chosen as the nominal one. An upper bound to the truncated error magnitude is proposed which is calculated using linear programming. It is demonstrated that all the parameters formulating a feasible set are derived using finite element analysis for a flexible beam example, and feasibility of the proposed scheme is also illustrated by numerical bounding results.

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In a genetic local search (GLS) which is a hybrid technique of a genetic algorithm (GA) and a local search (LS), the undesirable phenomenon of premature convergence can often occur. Premature convergence is the phenomenon where the diversity of a group is lost. This phenomenon occurs because identical individuals are increased rapidly in the group while searching. Therefore, crossover loses its function. Once the premature convergence occurs, the search by the GLS becomes meaningless. Therefore, it is important to avoid the premature convergence and maintain the diversity. We made the parallel GLS to improve its searching ability and, in this paper, we propose a method named “Islands' Distance Strategy" to improve the searching ability of the GLS by introducing the concept of distance between the islands on one-dimensional torus type island model and by limiting the islands' range to which migrants can migrate. The problems used in these experiments are traveling salesman problems (TSPs) in 48 cities arranged in double-concentric-circle. The experimental results show that the proposed method achieved 100.0% as the attainment rate to the optimal solution, where the number of islands (subpopulations) N_sp=20, 25 and the islands' range to which migrants can migrate r=1, 2. Furthermore we demonstrated the existence of an appropriate islands' range to which migrants can migrate, and confirmed that the appropriate islands' range r=2 in these experimental conditions.

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A variant of the highly optimized tolerance model is applied to a toy problem of bioterrorism to determine the optimal arrangement of hypothetical bio-sensors to avert epidemic outbreak. Nonlinear loss function is utilized in searching the optimal structure of the sensor network. The proposed method successfully averts disastrously large events, which can not be achieved by the original highly optimized tolerance model.

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A new system of Computer Synthesis Hologram (CSH) compressed and transmitted and reconstructed has been established with Shape Adaptive Binary Tree Predictive Coding (SA-BTPC) and Fast Fourier Transform (FFT) technique. In this system, the photographs can be directly calculated into the digital hologram using the holographic principle of D. Gabor. In coding, SA-BTPC algorithm adapts a non-causal, shape-adaptive predictor to decompose a digital hologram into a binary tree of prediction errors and zero blocks. Thus its coding speed is faster than JPEG baseline processing for loss compression scheme. In experiments, when its compression ratio is achieved to 0.4683% for "lossy" compression, the image lineament shape of processed CSH still can be effectively reconstructed by FFT. Moreover, the reasons have been explained why the shape adaptive predictive coding algorithm is chosen to process CSH. And the reconstructed image information of processed CSH has been compared with the reconstructed image information of original CSH. Finally, Compression ratio (R), Mean squared error (MSE) and Pear signal to noise ratio (PSNR) have been precisely calculated and analyzed to evaluate the reconstructed images variation of processed CSH. The better predictive coding model for processing digital hologram can be determined by the distortion measure.

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This paper discusses a mechanism to ensure safe behaviors of HMSs (Holonic Manufacturing Systems). The change from low-variety high-volume to high-variety low-volume production requires highly flexible and adaptive manufacturing systems. Multi agent systems, in which decisions are made through cooperation among autonomous and cooperative elements, fulfill these requirements by exploiting full abilities of individual elements while eliminating various bottlenecks that exist in conventional systems. However, highly adaptive features induce non-deterministic behaviors of systems, and make it difficult to adopt multi agent systems as primary bases of manufacturing systems. In order to apply these systems to large and complicated applications, mechanisms that make their behaviors more predictable are essential. HMSs are a kind of multi agent systems, and an HMS safety ensuring mechanism proposed here is one of the attempts to make behaviors of multi agent systems more predictable.

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This paper presents a new method of document image retrieval that is capable of spotting parts of document images relevant to users' queries. This enables us to improve effectiveness and usability of retrieval, since users are relieved from burdens of finding relevant parts in retrieved documents. The proposed method is based on the assumption that parts of document images which densely contain characters in queries are relevant to them. For the purpose of ranking relevant parts, two-dimensional density distributions of characters are calculated based on layout features such as locations of characters and distance to the nearest characters. Based on the experimental results of retrieving Japanese newspaper articles, it is shown that the proposed method is superior to a method without a function of retrieving the parts.

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This paper reports an evaluation of European Telecommunications Standards Institute (ETSI) standard Distributed Speech Recognition (DSR) front-end through continuous speech recognition on a Japanese speech corpus and proposes methods, the Bias Removal Methods (BRMs), that reduce the distortion between feature parameters and the VQ codebook. Experimental results show that (1) using non-quantized features in an acoustic model training procedure can improve the recognition performance of DSR front-end features and (2) broadening the analysis band can improve the recognition performance for the same bitrate. The proposed method can improve the recognition performance in DSR condition. Notably, we observed an 18% relative improvement in the error rate using the proposed method under mismatch of channel characteristic conditions.

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To learn the computer engineering, students used a single board computer system in the seventies. It was possible to learn how to control devices by the system. Recently, single board computers are being replaced with a CAI(Computer Assisted Instruction) system such as a PC-based assembly language programming laboratory. However, conventional CAI systems originally have not been implemented for understanding hardware technology. For the reason, we have developed the x80 that is a virtual single board computer on the X Window System. The x80 provides students with the learning environment similar to the real single board computer. The paper describes an outline of the learning style on the real and virtual single board computer.

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In recent years, as supervisory and control systems software for steel plants is getting more and more complicated, it has been the most important issue to build the system low cost and efficiently. As the programming environment for plant systems, the graphical language such as the function block diagram is widely utilized. This kind of language contributes to the improvement of software productivity, while the view point of reuse is focused on programs. In order to aim at further improvement, it is required to promote the reuse from the view point of plant level design.
In this paper, we propose an engineering model such that reusable parts is build from the view point of plant level design of plant equipment and operation functions. We also introduce the engineering tool based on the model. An evaluation result of partial application of the tool for a real plant system shows that 70% of developed parts can be estimated to be reused in the future application and half of inter-connections among software parts can be automated.

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An approximate complex filter including no transformers is proposed. The proposed filter does not have the equiripple passband response. However, the narrower the passband width of the prototype filter, the smaller the deviation of the proposed filter from the prototype filter is. A third-order complex filter is designed.

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The paper proposes a Web page that can automatically generate a map-based interface for any VRML contents on the Web. This new approach reduces map development costs and provides a common interface to the users. 3D contents reconstruction is distributed among the client computers to guarantee Web service efficiency.

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The Nonharmonic Analysis, recently proposed by the authors, is able to obtain voice and noise spectra independent of each other as a result of high frequency resolution. Therefore, NHA may even enable voices in a noisy environment to be extracted from spectra without distortion. For preprocessing by the spectral subtraction method of effective noise suppression, we used NHA instead of the discrete Fourier transform and the effects on noise suppression were quantitatively verified. In an environment of SNR from -10 dB to +10 dB, the proposed technique showed an improvement of about 4 dB on average.

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Adaptive-Subspace Self-Organizing Map (ASSOM) is a variant of Self-Organizing Map, where each computational unit defines a linear subspace. The subspace in a unit is represented by a set of basis vectors. After training, these units result in a set of subspace detectors. In numerous cases, however, these are not enough to describe a class of patterns because of a linearity. In this letter, the ASSOM on the high-dimensional space with kernel method is proposed in order to achieve efficient classification. By using the kernel method, linear subspaces in the ASSOM can be extended to non-linear subspaces easily. This improves the representation of subspace. The effectiveness of the proposed method is verified by applying it to a well-known problem, or two spirals classification.

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