We previously proposed novel designs for artificial genes as media for storing digitally compressed image data, specifically for biocomputing by analogy to natural genes mainly used to encode proteins. A run-length encoding (RLE) rule had been applied in DNA-based image data processing, to form coding regions, and noncoding regions were created as space for designing biochemical editing. In the present study, we apply the RLE-based image-coding rule to creation of DNA-based animation. This article consisted of three parts: (i) a theoretical review of RLE-based image coding by DNA, (ii) a technical proposal for biochemical editing of DNA-coded images using the polymerase chain reaction, and (iii) a minimal demonstration of DNA-based animation using simple model images encoded on short DNA molecules.

We describe a new feature extraction method based on the geometric structure of matched local feature points that extracts robust features from an image sequence and performs satisfactorily in highly dynamic environments. Our proposed method is more accurate than other such methods in appearance-only simultaneous localization and mapping (SLAM). Compared to position-invariant robust features [1], it is also more suitable for low-cost, single lens cameras with narrow fields of view. Testing our method in an outdoor environment at Shibuya Station. We captured images using a conventional hand-held single-lens video camera. These environments of experiments are public environments without any planned landmarks. Results have shown that the proposed method accurately obtains matches for two visual-feature sets and that stable, accurate SLAM is achieved in dynamic public environments.

This paper proposes entropy-based L₁-regularized possibilistic clustering and a method of sequential cluster extraction from relational data. Sequential cluster extraction means that the algorithm extracts cluster one by one. The assignment prototype algorithm is a typical clustering method for relational data. The membership degree of each object to each cluster is calculated directly from dissimilarities between objects. An entropy-based L₁-regularized possibilistic assignment prototype algorithm is proposed first to induce belongingness for a membership grade. An algorithm of sequential cluster extraction based on the proposed method is constructed and the effectiveness of the proposed methods is shown through numerical examples.

Clustering is one of the most popular unsupervised classification methods. In this paper, we focus on rough clustering methods based on rough-set representation. Rough k-Means (RKM) is one of the rough clustering method proposed by Lingras et al. Outputs of many clustering algorithms, including RKM depend strongly on initial values, so we must evaluate the validity of outputs. In the case of objective-based clustering algorithms, the objective function is handled as the measure. It is difficult, however to evaluate the output in RKM, which is not objective-based. To solve this problem, we propose new objective-based rough clustering algorithms and verify theirs usefulness through numerical examples.

The pattern recognition method of clustering is a technique automatically classifying data into clusters. Among clustering methods, c-regression based on fuzzy set theory, called Fuzzy c-Regression (FCR), is proposed to get a linear dataset structure. The most recent clustering is based on rough set theory called rough clustering, which is less descriptive than fuzzy clustering. A typical rough clustering algorithm is Rough k-Regression (RKR). However, RKR has problems because it depends on initial values and has no optimum index, so we do not know whether a clustering result will be optimal. This paper proposes Rough c-Regression (RCR) based on the optimization of an objective function and demonstrates its effectiveness through numerical examples.

Fuzzy multisets defined by Yager take multisets on interval (0,1] as grades of membership. As Miyamoto later pointed out, the fuzzy multiset operations originally defined by Yager are not compatible with those of fuzzy sets as special cases. Miyamoto proposed different definitions for fuzzy multiset operations. This paper focuses on the two definitions of fuzzy multiset operations, one by Yager and the other by Miyamoto. It examines their differences in the framework of granular hierarchical structures generated from the free monoids as proposed in our previous papers. In order to define basic order between multisets on interval (0,1], Yager uses the natural order on the range ℕ, the set of natural numbers, whereas Miyamoto newly introduces an order generated from both domain (0,1] and range ℕ through the notion of cuts.

Switching regression models can output multiple clusters and regression models. However, there is one problem: the results have a strong dependency on the predefined number of clusters. To avoid these drawbacks, we have researched sequential extractions. In sequential extractions process, one cluster is extracted at a time using a method of noise-detection, and the number of clusters are determined automatically. We propose semi-supervised sequential kernel regression models with penalty functions. Additionally, we also find that the sensitivity against the regularization parameter λ can be alleviated by semi-supervisions using penalty functions. We show the effectiveness of the proposed method by using numerical examples.

The scheduling of semiconductor wafer testing processes may be seen as a resource constraint project scheduling problem (RCPSP), but it includes uncertainties caused by wafer error, human factors, etc. Because uncertainties are not simply quantitative, estimating the range of the parameters is not useful. Considering such uncertainties, finding a good situation-dependent dispatching rule is more suitable than solving an RCPSP under uncertainties. In this paper we apply machine learning approaches to acquiring situation-dependent dispatching rule. We compare obtained rules and examine their effectiveness and usefulness in problems with unpredictable wafer testing errors.

The present study proposes an algorithm for sequential cluster extraction using power-regularized possibilistic c-means (pPCM). First, pPCM is derived in a similar manner to two types of entropy-regularized possibilistic c-means (ePCM) derivations, where a power function is utilized instead of the negative entropy in ePCM. The cluster fusion with pPCM is identical to the mean-shift with a generalized Epanichnikov kernel, whereas the proposed method employs sequential cluster extraction with pPCM. Numerical examples show that the cluster number produced by the proposed algorithm did not match with the true class number in real datasets, but the extracted clustering results were partially successful in terms of capturing dense regions of objects.

An inference method for sparse fuzzy rules is proposed which interpolates fuzzy rules at an infinite number of activating points and deduces consequences based on α-GEMII (α-level-set and generalized-mean-based inference). The activating points, proposed in this paper, are determined so as to activate interpolated fuzzy rules by each given fact. The proposed method is named α-GEMINAS (α-GEMII-based inference with fuzzy rule interpolation at an infinite number of activating points). α-GEMINAS solves the problem in infinite-level interpolation where fuzzy rules are interpolated at the least upper and greatest lower bounds of an infinite number of α-cuts of each given fact. The infinite-level interpolation can nonlinearly transform the shapes of given membership functions to those of deduced ones in accordance even with sparse fuzzy rules under some conditions. These conditions are, however, strict from a practical viewpoint. α-GEMINAS can deduce consequences without these conditions and provide nonlinear mapping comparable with infinite-level interpolation. Simulation results demonstrate these properties of α-GEMINAS. Thereby, it is found that α-GEMINAS is practical and applicable to a wide variety of fields.

In this study, a method for acquiring deep level emotion understanding is proposed to facilitate better human-robot communication, where customized learning knowledge of an observed agent (human or robot) is used with the observed input information from a Kinect sensor device. It aims to obtain agent-dependent emotion understanding by utilizing special customized knowledge of the agent rather than ordinary surface level emotion understanding that uses visual/acoustic/distance information without any customized knowledge. In the experiment employing special demonstration scenarios where a company employee’s emotion is understood by a secretary eye robot equipped with a Kinect sensor device, it is confirmed that the proposed method provides deep level emotion understanding that is different from ordinary surface level emotion understanding. The proposal is being planned to be applied to a part of the emotion understanding module in the demonstration experiments of an ongoing robotics research project titled “Multi-Agent Fuzzy Atmosfield.”

Companies often carry out questionnaires, in order to gain a better grasp of consumer trends for the design of marketing strategies. The proper definition of a set of questions presents some difficulties. For example, if respondents take the meaning of two or more questions in one questionnaire to have the same/similar meanings, these questions could be rendered redundant. However, it is difficult to know beforehand how a respondent will interpret the meaning of a question. On the other hand, it is possible to assess the meaning that respondents assumed for questions, and how appropriate the set of questions is, in retrospect. In this paper, we propose a method for visualizing groups of respondents who assumed distinct meanings for questions, by applying Higher Order Singular Value Decomposition (HOSVD) to a tensor consisting of cosine similarity matrices. The proposed method is applied to a Web questionnaire dataset, and it is shown that the new method can identify the respondents’ unique understanding of the meanings of questions, which are not found using the conventional method. We also show that the proposed method is the most effective for the visualization of relationships between questions, among the possible ways of applying HOSVD to cosine similarity matrices.

Notification delivered at an inappropriate time is usually considered an interruption. To ensure appropriate timing, we considered treating the self-initiated intermission as a period for interrupting users without causing distractions. This intermission is the time to report oneself as being available for an interaction or being ready for an interruption. This gives users the privilege of choosing a suitable time to handle interruptions without hampering any currently active task. Users’ interruptibility is compared at the time of self-initiated intermission with two alternative types of interruption presentation: application switching and regular intervals. An empirical study showed that the self-initiated intermission is the best approach for interrupting users because their interruptibility is high at this time. We also found that users report on their intermission approximately up to four times during an hour long time span.

Most automated analysis methods related to biosignal-based human Emotions collect their data using multiple physiological signals, long-term physiological signals, or both. However, this restricts their ability to identify Emotions in an efficient manner. This study classifies evoked Emotions based on two types of single, short-term physiological signals: electrocardiograms (ECGs) and galvanic skin responses (GSRs) respectively. Estimated recognition times are also recorded and analyzed. First, we perform experiments using film excerpts selected to elicit target Emotions that include anger, grief, fear, happiness, and calmness; ECG and GSR signals are collected during these experiments. Next, a wavelet transform is applied to process the truncated ECG data, and a Butterworth filter is applied to process the truncated GSR signals, in order to extract the required features. Finally, the five different Emotion types are classified by employing an artificial neural network (ANN) based on the two signals. Average classification accuracy rates of 89.14% and 82.29% were achieved in the experiments using ECG data and GSR data, respectively. In addition, the total time required for feature extraction and emotional classification did not exceed 0.15 s for either ECG or GSR signals.

In the speed-sensorless induction motor drives system, Model Reference Adaptive System (MRAS) is the most common strategy. However, speed estimation using reactive power based MRAS has the problem of instability in the regenerating mode of operation. Such estimation technique is simple and has several notable advantages, but is not suitable for induction motor drives. To overcome these problems, a suitable Artificial Neural Networks (ANN) is presented to replace the adjustable model to make the system stable when working at low speed and zero crossing. Simultaneously, in order to enhance the ANN convergence speed and avoid the trap of local minimum value of algorithm, we used the modified Particle Swarm Optimization (PSO) to optimize the weights and threshold values of neural networks. Then the ANN-based MRAS was used to identify the speed of motor in the indirect vector control system. The results of the simulation show that, by this method, the speed of motor can be identified accurately in different situations, and the result is reliable.

A Bloch Sphere-based Emotion Space (BSES), where two angles ϕ and θ in the Bloch sphere represent the emotion (such as happiness, surprise, anger, sadness, expectation, or relaxation in [0, 2π)) and its intensity (from neutral to maximum in [0, π]), respectively, is proposed. It exploits the psychological interpretation of color to assign a basic color to each emotion subspace such that the BSES can be visualized, and by using quantum gates, changes in emotions can be tracked and recovered. In an experimental validation, two typical human emotions, happiness and sadness, are analyzed and visualized using the BSES according to a preset emotional transmission model. A transition matrix that tracks emotional change can be used to control robots allowing them to adapt and respond to human emotions.

Surgical robots have improved considerably in recent years, but their intuitive operability, and thus their user interoperability, has yet to be quantitatively evaluated. Thus, we propose a method for measuring a user’s brain activity while operating such a robot, to better enable the design of a robot with intuitive operability. The objective of this study was to determine the angle and radius between an endoscope and manipulator that best allows the user to perceive the manipulator as being part of their own body. In the experiments, a subject operated a hand controller to position the tip of a virtual slave manipulator onto a target in a surgical simulator while his/her brain activity was measured using a brain imaging device. The experiment was carried out several times with the virtual slave manipulator configured in a variety of ways. The results show that the amount of brain activity is significantly greater with a particular slave manipulator configuration. We concluded that the hand-eye coordination between the body image and the robot should be closely matched in the design of a robot having intuitive operability.

We focus on developing an e-learning system that supports the grasping of misunderstanding from descriptive answers. We propose real-time keyword extraction and an interface for grasping misunderstanding based on extracted keywords. The system extracts keywords without extra information. Teachers find major misunderstandings by using the proposed interface, which consists of two views – keyword and description. Using these views, teachers browse answers in three steps – finding keywords, reading around keywords, and reading full answers. We use experiments to demonstrate the effectiveness of our system, this proposed keyword extraction extracts expected words. Subjects evaluate the proposed interface for its effectiveness in grasping misunderstandings. Using our proposed, teachers found major misunderstandings quickly and easily.

Recently, the three-dimensional (3D) sensing technique that uses multiple cameras has been applied to various areas, such as visualization, motion capturing, and so on. However, improvement of camera model calibration is required for higher precision of measurements. In this study, we propose a practicable fuzzy modeling approach for 3D sensing that utilizes stereo vision configuration. The distance between a sensing target and the camera is used to construct a camera fuzzy model that considers optical projection characteristics. In our approach, the weighted least squares method is successfully applied considering a fuzzy partition to formulate the fuzzy model. Then, iterative calculations for solving the inverse problem of the camera fuzzy model are performed to obtain measured coordinates. Through sensing experiments of stereo vision measurement based on the proposed approach, we show that performance of the model is drastically improved compared with the conventional modeling approach.