Kaoru Hirota's research while affiliated with Tokyo Institute of Technology and other places
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Publications (513)
Determining the volume percentages of flows passing through the oil transmission lines is one of the most essential problems in the oil, gas, and petrochemical industries. This article proposes a detecting system made of a Pyrex-glass pipe between an X-ray tube and a NaI detector to record the photons. This geometry was modeled using the MCNP versi...
Determining the amount of void fraction of multiphase flows in pipelines of the oil, chemical and petrochemical industries is one of the most important challenges. Performance of capacitance based two phase flow meters highly depends on the fluid properties. Fluctuation of the liquid phase properties such as density, due to temperature and pressure...
When scale builds up in a transmission pipeline, it narrows the pipe’s interior and causes losses in both power and efficiency. A noninvasive instrument based on gamma-ray attenuation is one of the most reliable diagnostic procedures for determining volumetric percentages in a variety of circumstances. A system with a NaI detector and dual-energy g...
A coupled multimodal emotional feature analysis (CMEFA) method based on broad–deep fusion networks, which divide multimodal emotion recognition into two layers, is proposed. First, facial emotional features and gesture emotional features are extracted using the broad and deep learning fusion network (BDFN). Considering that the bi-modal emotion is...
In the prediction of turning points (TPs) of time series, the improved model of integrating piecewise linear representation and weighted support vector machine (IPLR-WSVM) has achieved good performance. However, due to the single data source and the limitation of algorithm, IPLR-WSVM has encountered challenges in profitability. In this paper, a mod...
Despite substantial progress in Facial Expression Recognition (FER) in recent decades, most previous methods have been developed to recognize constrained facial expressions. Real-world occlusions lead to invisible facial regions and contaminated facial features, which undoubtedly increase the difficulty of FER in the wild. Therefore, a Patch Attent...
To enhance the performance of equilibrium optimizer (EO) and expand its application for smooth path planning of the unmanned ground vehicle (UGV), an ameliorated equilibrium optimizer (AEO) is developed and applied to the UGV smooth path planning problem. The main characteristics of AEO are the population initialization by an opposition-based learn...
Prediction of missing links is an important part of many applications, such as friends’ recommendations on social media, reduction of economic cost of protein functional modular mining, and implementation of accurate recommendations in the shopping platform. However, the existing algorithms for predicting missing links fall short in the accuracy an...
One of the main problems in oil fields is the deposition of scale inside oil pipelines, which causes problems such as the reduction of the internal diameter of oil pipes, the need for more energy to transport oil products, and the waste of energy. For this purpose, the use of an accurate and reliable system for determining the amount of scale insid...
To improve the accuracy of the maximum correntropy Kalman filter (MCKF) in wireless sensors networks (WSNs) positioning, a dynamic self-tuning maximum correntropy Kalman filter (DSTMCKF) is proposed, where innovation and the sensors information of the WSNs are used to adjust the noise covariance matrices, and the maximum correntropy criterion is th...
Existing social network simulation models exhibit several limitations, including extensive iteration requirements and multiple control parameters. In this study, an information propagation model based on continuous-time quantum walk (CTQW-IPM) is introduced to rank crucial individuals in undirected social networks. In the proposed CTQW-IPM, arbitra...
A Hadamard coin driven quantum walk (i.e., Hadamard walk) model is proposed to identify the important edges of undirected complex networks. In this proposed model, the importance of an edge is scored through the observed probabilities on a pair of nodes with a common edge, based on which the rankings of all important edges are obtained according to...
Two-phase flow is very important in many areas of science, engineering, and industry. Two-phase flow comprising gas and liquid phases is a common occurrence in oil and gas related industries. This study considers three flow regimes, including homogeneous, annular, and stratified regimes ranging from 5–90% of void fractions simulated via the Mont Ca...
In addition to the almost five million lives lost and millions more than that in hospitalisations, efforts to mitigate the spread of the COVID-19 pandemic, which that has disrupted every aspect of human life deserves the contributions of all and sundry. Education is one of the areas most affected by the COVID-imposed abhorrence to physical (i.e., f...
Quantum information science is an emerging research field devoted to the use of quantum mechanical systems to devise and implement information processing tasks faster than that possible with classical computers. In this study, two quantum image resolution enhancement (QIRE-I and QIRE-II) schemes are proposed based on quantum wavelet transform and q...
In this study, a novel distillation paradigm named perceptual distillation is proposed to guide the training of image fusion networks without ground truths. In the paradigm, the student network which we called main autoencoder takes in source images and produces a fused image, and the teacher network is a well-trained network exploited to compute t...
Data visualization assists in the evaluation and analysis of large graphical data sets. In this study, quantum data visualization (QDV) is proposed as the first attempt to aid users in more effectively comprehending data via quantum mechanical effects. The QDV framework is introduced to fully illustrate the steps necessary for implementing this nov...
In the prediction of turning points (TPs) of time series, the improved model of integrating piecewise linear representation and weighted support vector machine (IPLR-WSVM) has achieved good performance. However, due to the single data source and the limitation of algorithm, IPLR-WSVM has encountered challenges in profitability. In this paper, a mod...
By introducing the Vehicle Routing, Scheduling & Dispatching Problem for Multiples Depot (VRSDP/MD) and the description of formalization, it is helpful to offer a solution to solve the complex situation in practical transportation problem. In order to decrease the influence of the problem, A computing model embodying Hierarchical Multiplex Structur...
Quantum information science is an interdisciplinary subject spanning physics, mathematics, and computer science. It involves finding new ways to apply natural quantum-mechanical effects, particularly superposition and entanglement, to information processing in an attempt to exceed the limits of traditional computing. In addition to promoting its ma...
In this article,
K
-meansclustering-based Kernel canonical correlation analysis algorithm is proposed for multimodal emotion recognition in human–robot interaction (HRI). The multimodal features (gray pixels; time and frequency domain) extracted from facial expression and speech are fused based on Kernel canonical correlation analysis.
K
-means...
Let us have a population of objects that are subjected to a given event. Each object can be assigned a degree of membership to the population. Assume that for a set of objects, a continuous parameter is measured before and after the given event. We can now form two paired fuzzy samples from the population—Z1 and Z2. We then measure the change ΔZ of...
Multimodal fusion-based emotion recognition has attracted increasing attention in affective computing because different modalities can achieve information complementation. One of the main challenges for reliable and effective model design is to define and extract appropriate emotional features from different modalities. In this paper, we present a...
A bilinear interpolation technique is proposed for flexible representations of quantum images (FRQIs). In this process, several quantum modules were developed, including assignment, increment, and quarter modules, for use in an interpolation circuit. The network structure of these circuits, capable of both up-sampling and down-sampling FRQIs, was i...
Image processing with any potential quantum computing hardware requires a quantum color model capable of capturing and manipulating color information in images. In this study, a quantum hue, saturation, and lightness (QHSL) model is proposed as a first attempt to encode perceptually relevant triplet color components using the properties of quantum...
Improving the interaction between artificial systems and their users is an important issue in artificial intelligence. In current trends in social robotics, this is accomplished by making such systems not just intelligent but also emotionally sensitive. Therefore, artificial emotional intelligence (AEI) is focused on simulating and extending natura...
Emotion recognition and intention understanding are important components of human-robot interaction. In multimodal emotion recognition and intent understanding, feature extraction and selection of recognition methods are related to the calculation of affective computing and the diversity of human-robot interaction. Therefore, by studying multimodal...
Facial Expression Recognition (FER) has long been a challenging task in the field of computer vision. Most of the existing FER methods extract facial features on the basis of face pixels, ignoring the relative geometric position dependencies of facial landmark points. This paper presents a hybrid feature extraction network to enhance the discrimina...
An improved fully convolutional network based on post-processing with global variance (GV) equalization and noise-aware training (PN-FCN) for speech enhancement model is proposed. It aims at reducing the complexity of the speech improvement system, and it solves overly smooth speech signal spectrogram problem and poor generalization capability. The...
Hybrid A * algorithm has been widely used in mobile robots to obtain paths that are collision-free and drivable. However, the outputs of hybrid A * algorithm always contain unnecessary steering actions and are close to the obstacles. In this paper, the artificial potential field (APF) concept is applied to optimize the paths generated by the hybrid...
The randomness of path generation and slow convergence to the optimal path are two major problems in the current rapidly exploring random tree (RRT) path planning algorithm. Herein, a novel reinforcement-learning-based hybrid bidirectional rapidly exploring random tree (H-BRRT) is presented to solve these problems. To model the random exploration p...
An ameliorated Frenet trajectory optimization (AFTO) method based on artificial emotion (AE) and an equilibrium optimizer (EO) is proposed for the local trajectory planning of an unmanned ground vehicle (UGV). An artificial emotional potential field (AEPF) model is established to simulate AE. To realize a humanoid driving mode with emotional intell...
This study presents a modest attempt to interpret, formulate, and manipulate the emotion of robots within the precepts of quantum mechanics. Our proposed framework encodes emotion information as a superposition state, whilst unitary operators are used to manipulate the transition of emotion states which are subsequently recovered via appropriate qu...
The Dempster-Shafer (D-S) theory based on multi-SVM to deal with multimodal gesture images for intention understanding is proposed, in which the Sparse Coding (SC) based Speeded-Up Robust Features (SURF) are used for feature extraction of depth and RGB image. Aiming at the problems of the small sample, high dimensionality and feature redundancy for...
The weight-adapted convolution neural network (WACNN) is proposed to extract discriminative expression representations for recognizing facial expression. It aims to make good use of the convolution neural network’s potential performance in avoiding local optimal and speeding up convergence by hybrid genetic algorithm (HGA) with optimal initial popu...
Two-layer Fuzzy SVR-TS Model is proposed for emotion understanding in human-robot interaction, where the real-time dynamic emotion is recognized according to facial expression, and emotional intention understanding is obtained mainly based on human emotions and identification information. It aims to make robots capable of recognizing and understand...
Deep neural network (DNN) has been used as a learning model for modeling the hierarchical architecture of human brain. However, DNN suffers from problems of learning efficiency and computational complexity.
In recent years, vision-based gesture adaptation has attracted great attention from many experts in the field of human-robot interaction, and many methods have been proposed and successfully applied, such as particle swarm optimization and genetic algorithm. However, the reduction of the error and energy consumption of a robot while paying attentio...
The presenters’ group has been studying on humans-robots interaction based on Computational Intelligence in the frame work of multiagent smart society, where a concept of Fuzzy Atmosfield (FA) is proposed to express the atmosphere in humans-robots communication. The FA is characterized by a 3D fuzzy cubic space with “friendly-hostile”, “lively-calm...
AdaBoost-KNN using adaptive feature selection with direct optimization is proposed for dynamic emotion recognition in human-robot interaction, where the real-time dynamic emotion is recognized based on facial expression.
This book focuses on the key technologies and scientific problems involved in emotional robot systems, such as multimodal emotion recognition (i.e., facial expression/speech/gesture and their multimodal emotion recognition) and emotion intention understanding, and presents the design and application examples of emotional HRI systems. Aiming at the...
This study presents a modest attempt to interpret, formulate, and manipulate emotion of robots within the precepts of quantum mechanics. Our proposed framework encodes the emotion information as a superposition state whilst unitary operators are used to manipulate the transition of the emotion states which are recovered via appropriate quantum meas...
In this paper, Spinning Detail Perceptual Generative Adversarial Networks (SDP-GAN) is proposed for single image de-raining. The proposed method adopts the Generative Adversarial Network (GAN) framework and consists of two following networks: the rain streaks generative network G and the discriminative network D. To reduce the background interferen...
To detect the students’ concentration state in classroom, a DS (Dempster–Shafer theory)-based evaluation algorithm is proposed by measuring the students’ Euler angles of their facial attitude. The detection of facial attitude angles can be implemented under the surveillance video with lower pixels. Therefore, compared with other methods for student...
Lithium battery packs are the main driving energy source for electric vehicles. A battery pack equalization charging solution using a constant current source for variable rate charging is presented in this paper. The charging system consists of a main constant current source and independent auxiliary constant current sources. Auxiliary constant cur...
An emotion recognition framework based on a two-channel convolutional neural network (CNN) is proposed to detect the affective state of humans through facial expressions. The framework consists of three parts, i.e., the frontal face detection module, the feature extraction module, and the classification module. The feature extraction module contain...
Multi-modal emotion feature extraction is an indispensable part of multi-modal emotion recognition. In order to make effective use of emotion information in multi-modal emotion recognition, the corresponding feature extraction method should be adopted according to the characteristics of emotion information of different modes.
The two-layer fuzzy multiple random forest (TLFMRF) is proposed for speech emotion recognition. When recognizing speech emotion, there are some problems. One is that feature extraction relies on personalized features. The other is that emotion recognition doesn’t consider the differences among different categories of people. In the proposal, person...
This chapter introduces the basic structure of robot emotion system, analyzes the development function of the system according to the needs of users, and constructs a set of robot emotion interaction system based on the actual equipment. The interaction system of emotion robot constructed in this chapter provides an experimental platform for the ve...
The two-stage fuzzy fusion based-convolution neural network is proposed for dynamic emotion recognition by using both facial expression and speech modalities, which not only can extract discriminative emotion features which contain spatio-temporal information, but can also effectively fuse facial expression and speech modalities. Moreover, the prop...
A three-layer weighted fuzzy support vector regression (TLWFSVR)Three-Layer Weighted Fuzzy Support Vector Regression (TLWFSVR) model is proposed for understanding human intention, and it is based on the emotion-identification information in human-robot interaction. TLWFSVR model consists of three layers, including adjusted weighted kernel fuzzy c-m...
An intention understanding model based on two-layer fuzzy support vector regression (TLFSVR) is proposed in human-robot interaction, where Fuzzy C-Means clustering is used to classify the input data, and intention understanding is mainly obtained by emotion, with identification information such as age, gender, and nationality. It aims to realize th...
The ability of robots to correctly recognize emotional states, understand emotional intentions, and give emotional feedback is an important aspect and challenge in the study of emotional robot systems. In order to realize a human-robot interaction system with certain emotion recognition and intention understanding, and to establish a natural and ha...
An approach to N-best hypotheses re-ranking using a sequence-labeling model is applied to resolve the data deficiency problem in Grammatical Error Correction (GEC). Multiple candidate sentences are generated using a Neural Machine Translation (NMT) model; thereafter, these sentences are re-ranked via a stacked Transformer following a Bidirectional...
Quantum image processing employs quantum computing to capture, manipulate, and recover images in various formats. This requires representations of encoded images using the quantum mechanical composition of any potential computing hardware. In this study, a quantum hue, saturation, and lightness (QHSL) color model is proposed to organize and concept...
Physics and computer science have a long tradition of cross-fertilization. One of the latest outcomes of this mutually beneficial relationship is quantum information science, which comprises the study of information processing tasks that can be accomplished using quantum mechanical systems. Quantum Image Processing (QIMP) is an emergent field of qu...
This paper presents a nonlinear equivalent-input-disturbance (NEID) approach to rejecting an unknown exogenous disturbance in a nonlinear system. An NEID compensator has two parts: a conventional equivalent-input-disturbance estimator and a nonlinear state feedback term. This design ensures that only the exogenous disturbance is rejected and the us...
We present a simple yet highly dimensional hybrid diode bridge circuit network that can exhibit complex chaotic behaviours. Further, since our network is characterised by smooth fourth-order exponential nonlinearity, we employ a distinctive approach to assess its different properties: we examine the circuit stability near fixed points. Specifically...
A concept of the VRSD problem (Vehicle Routing, Scheduling and Dispatching Problem) and its formularization are proposed in order to bridge the gap between conventional methods and complex situations in the real world. A HIMS model (a calculation model with HIerarchical Multiplex Structure), is also proposed for the VRSD problem. It contains 3 leve...
Although promising in terms of its applications in many facets of science and engineering; notably, in laser technology and remote sensing, ghost imaging is primarily impeded by its intense demands related to computational overhead, which impacts on the quality of output images. Advances in imaging and computing technologies have seen many efforts...
The two-stage fuzzy fusion based-convolution neural network is proposed for dynamic emotion recognition by using both facial expression and speech modalities, which not only can extract discriminative emotion features which contain spatio-temporal information, but also can effectively fuse facial expression and speech modalities. Moreover, the prop...
A fuzzy deep neural network with sparse autoencoder (FDNNSA) is proposed for intention understanding based on human emotions and identification information (i.e., age, gender, and region), in which the Fuzzy C-Means (FCM) is used to cluster the input data, and deep neural network with sparse autoencoder (DNNSA) is designed for emotional intention u...
Convolutional neural network-based broad learning with efficient incremental reconstruction model (CNNBL) is proposed to recognize emotions in human-robot interaction. It aims to extract deep and abstract features from facial emotional images, and reduce the influence of the complex structure and slow network updates on facial emotion recognition i...
Emotion is an important part of human interaction. Emotional recognition can greatly promote human-centered interaction techniques. On this basis, multimodal feature fusion can effectively improve the emotion recognition rate. However, in the multimodal feature fusion at the feature level, most of the methods do not consider the intrinsic relations...
The Dempster–Shafer theory based on multi-SVM to deal with multimodal gesture images for intention understanding is proposed, in which the Sparse Coding (SC) based Speeded-Up Robust Features (SURF) are used for feature extraction of depth and RGB image. Aiming at the problems of the small sample, high dimensionality and feature redundancy for image...
The two-layer fuzzy multiple random forest (TLFMRF) is proposed for speech emotion recognition. When recognizing speech emotion, there are usually some problems. One is that feature extraction relies on personalized features. The other is that emotion recognition doesn’t consider the differences among different categories of people. In the proposal...
A quantum circuit implementation of Powell’s conjugate direction method (“Powell’s method”) is proposed based on quantum basic transformations in this study. Powell’s method intends to find the minimum of a function, including a sequence of parameters, by changing one parameter at a time. The quantum circuits that implement Powell’s method are logi...