This paper addresses the issues associated with deployment of sensors, which are critical in wireless sensor networks. This paper provides an improved particle swarm optimization (PSO) algorithm by changing the basic form of PSO and introducing disturbance (d-PSO). By comparing with other PSO-based algorithms, simulation results show that the d-PSO algorithm provides a good-coverage solution with a satisfying coverage rate in a short time. This feature is especially useful for the rapid deployment of sensors.

The combinatorial optimization problem of university classroom schedule assignments is formulated using multiagent simulation and genetic algorithms in the evaluation and optimization process. The method we propose consists of global and local multiagent planning. Conventional global planning requires setting subgoals manually, which became a bottleneck in optimization. To solve this problem, a multi-resolution Dijkstra method for selected autonomously, assuming eight classrooms as a real University of Tsukuba building and 250 agents, we confirmed the effectiveness of the proposed multi-resolution Dijkstra’s algorithm as for both global and local route selections, compared to the uniform Dijkstra’s method.

This paper presents a stabilization method for an underactuated ball-and-beam system (BABS) based on a second-order sliding mode (SOSM) control. The BABS is an underactuated nonlinear system that is widely used to verify nonlinear control performance. Virtual control is introduced to a second-order BABS subsystem to minimize control performance inaccuracy by using model linearization. An actual virtual controller with variable finite-time tracking is achieved using a second-order sliding mode controller. An adaptive robust method is proposed to solve an uncertainty problem with unknown upper bounds, and then a finite-time convergence theory proof is given. Theory, simulation and experiment results verify the efficiency of the BABS controller.

This paper is concerned with the stabilization problem of an optimal dynamic quantized system with packet loss. The optimal dynamic quantizer, which minimizes the quantized output error, is designed for a discrete-time system with packet loss occurring in the forward channel. A sufficient condition for the system’s mean square stability is developed based on matrix inequality method. A state feedback controller design method is also proposed, and numerical simulation demonstrates the effectiveness of the proposed method.

This research addresses a gait motion planning problem for a six-legged robot walking on an irregular field. In this proposal, we used a simplified neural network model called an Associatron that recalls total motion patterns sequentially from partial information. The Associatron is used here because it is more effective and adaptable than conventional methods. Using the proposed method, the robot is expected to walk in unknown fields. After verifying planning using an Open Dynamics Engine (ODE) by using simulations, we found that memorized patterns are recalled from developed patterns. We then conducted experiments using a real developed robot. Experiment results show that, when using the proposed planning method, the robot selects suitable gait motion patterns in the presence of an obstacle.

Quantum circuits to realize color operations of channel of interest, channel swapping, and alpha blending on images are proposed using five kinds of quantum gates, i.e., NOT, CNOT, Toffoli, Rotation, and Controlled Rotation gates. Complexities of the proposed circuits are for an N-sized image, whereas the color information must be transformed pixel by pixel in the case of operators on classical computers. Simulations on the proposed three quantum color operations using three human facial and one Japanese style house images demonstrate that at most 9, 3, and 5 basic quantum gates are requested, that shows the feasibility of quantum circuits. Based on proposed three operations, all invertible classical color information transformation on images may be designed and many applications can be realized on quantum computer, and the channel of interest based watermarking is being researched which the experiment results show that from the point of PSNR, our proposal is about 10 dB better than the chosen method of quantum image watermarking.

A method of compensating for Stribeck-type nonlinear friction torque for a two-mass mechatronic system has been devised based on the equivalent-input-disturbance (EID) approach. The nonlinearity in the system is treated as an input-dependent disturbance, and an EID estimator is designed that estimates it. The incorporation of the estimate into the control input compensates for the nonlinearity. Simulation results demonstrate the validity of the method and show that the compensation accuracy is closely related to the time constant of the low-pass filter.

This paper proposes a recommender system based on personal-value-based user model. Conventional methods such as collaborative and content-based approaches tend to be less accurate for new users and items due to the lack of a relation between items and user preferences. While existing recommender systems usually employ user preferences of items for making recommendations, proposed method focuses on users’ personal values, which mean value judgments regarding on which attributes users put a high priority. The proposed recommender system employing personal-value-based user model is thus expected to realize more precise recommendations in cold-start situations. As one of typical cold-start situations, a prototype system is developed for recommendation using external resources. Experimental results show that generated user models reflect each user’s value judgment on attributes. In addition, the results also show that recommendation employing the proposed user model realizes improvements of precision in cold-start situations.

This paper describes a method for detecting affectively comparable terms. Comparable terms are often handled as sample objects instance in order to enrich linguistic expression, and using such terms explains and describes descriptions well. Coordinate terms in hierarchical knowledge are potentially comparable terms. Hierarchical coordinate terms are however sometimes affectively inappropriate as comparable term, because hierarchical knowledge is constructed by using only semantics without affections. We obtained the affections of terms obtained from blog and innovated them into hierarchical knowledge in order to detect affectively comparable terms. We conduct experiments to detect affectively comparable terms and discuss results, from which, we confirmed that affectively comparable terms could be detected by our proposed method. We deem detected affectively comparable terms to be applicable to creating artificial intelligence realizing intuitive interaction.

In this paper, two linear fuzzy clustering algorithms are proposed for relational data based on kernel fuzzy c-means, in which the prototypes of clusters are given by lines spanned in a feature space defined by the kernel which is derived from a given relational data. The proposed algorithms contrast the conventional method in which the prototypes of clusters are given by lines spanned by two representative objects. Through numerical examples, it is shown that the proposed algorithms can capture local sub-structures in relational data.

In this paper, two types of fuzzy co-clustering algorithms are proposed. First, it is shown that the base of the objective function for the conventional fuzzy co-clustering method is very similar to the base for entropy-regularized fuzzy nonmetric model. Next, it is shown that the non-sense clustering problem in the conventional fuzzy co-clustering algorithms is identical to that in fuzzy nonmetric model algorithms, in the case that all dissimilarities among rows and columns are zero. Based on this discussion, a method is proposed applying entropy-regularized fuzzy nonmetric model after all dissimilarities among rows and columns are set to some values using a TIBA imputation technique. Furthermore, since relational fuzzy c-means is similar to fuzzy nonmetric model, in the sense that both methods are designed for homogeneous relational data, a method is proposed applying entropy-regularized relational fuzzy c-means after imputing all dissimilarities among rows and columns with TIBA. Some numerical examples are presented for the proposed methods.

This paper proposes a flexible route planning problem for sightseeing with fuzzy random variables for travel times and satisfaction with activities under general sightseeing constraints. Travel time between sightseeing sites and satisfactions with activities depend on weather and climate conditions, and on traveler fatigue, so both fuzzy random variables for travel times and satisfactions and traveler fatigue-dependence are introduced. Tourists are likely to plan favored without drastically changing from the optimal route under usual conditions such as fine weather that suddenly changes for the worse. A route planning problem is proposed to obtain a favorite route similar to the optimal route under usual conditions. Trapezoidal fuzzy numbers and order relations are introduced as a basic case of fuzzy numbers. From order relations, the proposed model is transformed into an extended model of network optimization problems. A numerical example is used to compare the proposed model to standard route planning problems in sightseeing.

Economic analyses are typical methods based on time-series data or cross-section data. Economic systems are complex because they involve human behaviors and are affected by many factors. When a system includes such uncertainty, as those concerning human behaviors, a fuzzy system approach plays a pivotal role in such analysis.

In this paper, we propose a fuzzy autocorrelation model with confidence intervals of fuzzy random time-series data. These confidence intervals play an essential role in dealing with fuzzy random data on the fuzzy autocorrelation model that we have presented. We analyze tick-by-tick data of stock transactions and compare two time-series models, a fuzzy autocorrelation model proposed by us, and a new fuzzy time-series model that we propose in this paper.

In this paper, we propose a modified anisotropic diffusion algorithm using the dynamic morphological filtering as a new precise edge-preserving smoothing technique for an accurate segmentation of color images with heavy noise. We incorporate a dynamic selection of multiple structuring elements for each pixel in the morphological filter before the anisotropic diffusion in order to improve the ability of edge-preserving smoothing. As a result, noise and unnecessary details of images are effectively smoothed while preserving small image structures before performing a segmentation algorithm. Several simulated examples are presented that demonstrate the effectiveness of the proposed technique.

Soft tissue or ligament balancing in total knee arthroplasty is important for ensuring knee joint stability. Correct balancing and appropriate alignment of ligaments extend prosthesis life by preventing unnecessary force from acting on the prosthesis during routine activities. The current implementation of total knee arthroplasty relies heavily on the subjective “feel” of the surgeon for correct prosthesis implantation onto tibiofemoral components. We developed a force distribution sensing system to provide quantitative information to surgeons during ligament balancing. The measurement system consists of two main components: two force sensors embedded in trial insert for each condyle and signal acquisition for data processing and force visualization. Sensors were designed and developed using pressure-sensitive conductive rubber that measures changes in resistance in the event of deformation caused by external force. Corresponding voltage measured by circuits is transmitted via an RF transceiver to a computer and visualized as color gradient. Current sensors could measure maximum force of 196.13 N (20 kgf). Results from calibration and experiments on a plastic trial prosthesis indicated that the device has good potential for providing appropriate force distribution information on the knee during total knee arthroplasty procedure.

As an evolutionary approach to solve multi-objective optimization problems involving several constraints, recently a multi-objective evolutionary algorithm (MOEA) using two-stage non-dominated sorting and directed mating (TNSDM) has been proposed. In TNSDM, directed mating utilizes infeasible solutions dominating feasible solutions in the objective space to generate offspring. In our previous studies, significant contribution of directed mating to the improvement of the search performance was verified on several benchmark problems. However, in the conventional TNSDM, infeasible solutions utilized in directed mating are discarded in the selection process of parents (elites) population and cannot be utilized in the next generation. TNSDM has potential to further improve the search performance by archiving useful solutions for directed mating to the next generation and repeatedly utilizing them in directed mating. To further improve effects of directed mating in TNSDM, in this work, we propose an archiving strategy of useful solutions for directed mating. We verify the search performance of TNSDM using the proposed archive by varying the size of archive, and compare its search performance with the conventional CNSGA-II and RTS on m objectives k knapsacks problems. As results, we show that the search performance of TNSDM is improved by introducing the proposed archive in aspects of diversity of the obtained solutions in the objective space and convergence of solutions toward the optimal Pareto front.

Constrained clustering is a framework for improving clustering performance by using constraints about data pairs. Since performance of constrained clustering depends on the set of constraints used, a method is needed to select good constraints that promote clustering performance. In this paper, we propose an active sampling method working with a constrained cluster ensemble algorithm that aggregates clustering results that a modified COP-Kmeans iteratively produces by changing the priorities of constraints. Our method follows the approach of uncertainty sampling and measures uncertainty using variations of clustering results where data pairs are clustered together in some results but not in others. It selects the data pair to be labeled that has the most variable result during cluster ensemble process. Experimental results show that our method outperforms random sampling. We further investigate the effect of important parameters.

In order to perform collaborative filtering with published databases in a privacy preserving manner, databases must be anonymized beforehand. This paper studies the applicability of fuzzy k-member clustering in privacy preserving collaborative filtering with k-anonymized data, in which users’ historical data of k or more users are suppressed considering soft data partitions. By allowing boundary samples to be shared by multiple clusters, data anonymization is performed without significant loss of information. Its performances are compared with several different types of fuzzy membership functions.