Intelligent Interaction Research Articles

Research Group Introduction: Motion Control Laboratory, Department of Intelligent Interactive Systems, Faculty of Engineering, Information and Systems, University of Tsukuba

Research Group Introduction: Motion Control Laboratory, Department of Intelligent Interactive Systems, Faculty of Engineering, Information and Systems, University of Tsukuba

Intelligent Target Detection and Tracking Algorithm for Martial Arts Applications

Moving object detection and tracking is the basis and key technology for intelligent video surveillance, human-computer interaction, mobile robot and vehicle visual navigation, industrial robot system, and other applications. It has important applications in intelligent monitoring, human-computer interaction, visual navigation, and so on. Intelligent technology can greatly facilitate the monitoring of countless targets. This paper adopts the random motion model to describe the motion state of the target. Based on wireless communication and information security, this paper studies the communication and information security of the intelligent system of martial arts target detection and tracking algorithm, and the basic idea of mean-shift tracking algorithm is to use gradient climbing of probability density to find local optimal. This paper uses the background subtraction method based on the vibe algorithm to obtain the binary image of the foreground object; the shadow detection algorithm is used to remove the shadow of the foreground image; the haar-like (Haar) feature is selected as the feature of motion detection, and the feature value of the rectangular area is quickly calculated to describe the adjacent image area. The difference between the features, the final result image, and then the intelligent system for analysis. Experimental data shows that the time consumed by the tracking algorithm is less than 20 ms, which can meet the real-time requirements of ordinary target tracking systems. The average processing time of the hybrid modeling method is 62.8 ms, and the detection rate is 15.92 frames/SEC. The results show that the algorithm improves the utilization of particles, greatly reduces the complexity, and reduces the degradation of the particle filter.

Intelligent Interaction Honeypots for Threat Hunting within the Internet of Things

As the Internet of Things (IoT) grows exponentially, security is falling farther and farther behind. Several new initiatives show promise for expanding the privacy and security around these devices in the future. But what about the billions of devices already out there in the wild? Security researchers are responsible for developing the tools and procedures for discovering these devices quickly, understanding the risks they bring with them, and developing tools to mitigate those risks to more manageable levels. Honeypots and honeynets have traditionally supported this work in traditional IT. However, the challenges faced by the highly distributed, incredibly heterogeneous Internet of Things make deploying such tools difficult and costly. Recent research in honeypot architectures explicitly designed for the chaotic nature of the IoT ecosystem brings a new sense of hope that may lead to significant improvements in IoT security. There is still much work to do, but research continues. IoT cybersecurity experts and threat hunters are developing strategies for securing this new frontier of technology. This study will lay the foundations for an intelligent and highly interactive honeypot solution that can scale with the researchers' requirements, providing a much-needed framework for deploying targeted IoT honeypots.

Enterprise Strategic Management From the Perspective of Business Ecosystem Construction Based on Multimodal Emotion Recognition.

Emotion recognition (ER) is an important part of building an intelligent human-computer interaction system and plays an important role in human-computer interaction. Often, people express their feelings through a variety of symbols, such as words and facial expressions. A business ecosystem is an economic community based on interacting organizations and individuals. Over time, they develop their capabilities and roles together and tend to develop themselves in the direction of one or more central enterprises. This paper aims to study a multimodal ER method based on attention mechanism. It analyzes the current emotional state of consumers and the development direction of enterprises through multi-modal ER of human emotions and analysis of market trends, so as to provide the most appropriate response or plan. This paper firstly describes the related methods of multimodal ER and deep learning in detail, and briefly outlines the meaning of enterprise strategy in the business ecosystem. Then, two datasets, CMU-MOSI and CMU-MOSEI, are selected to design the scheme for multimodal ER based on self-attention mechanism. Through the comparative analysis of the accuracy of single-modal and multi-modal ER, the self-attention mechanism is applied in the experiment. The experimental results show that the average recognition accuracy of happy under multimodal ER reaches 91.5%.

Expression Recognition Using Improved AlexNet Network in Robot Intelligent Interactive System

Aiming at the insufficient feature extraction in the expression feature extraction stage of traditional convolutional neural network and the misclassification of mislabeled samples, an expression recognition and robot intelligent interaction method using deep learning is proposed. First, in image preprocessing, the dimension of the color image is reduced by image gray adjustment to reduce the amount of calculation, the shadow interference is eliminated by the average method, and the image is enhanced by histogram equalization. Second, multichannel convolution is used to replace the single convolution size in the second convolution layer in AlexNet, the Global Average Pooling layer is introduced to replace the fully connected layer, and Batch Normalization is introduced to improve the feature extraction ability of the model and avoid gradient explosion. Finally, the Focal Loss is improved by setting the probability threshold to avoid the impact of mislabeling samples on the classification performance of the model. The experimental results show that the recognition accuracy of the model on FER2013 data set is 98.36%. The effectiveness of the algorithm is verified on the intelligent interactive system of service robot based on expression recognition. Compared with other expression recognition methods, the proposed method can extract more expression features and recognize facial expression more accurately.

3D-RPP: a novel 3D vision-based Pose Perception Approach for Industrial Robots

Enhanced by artificial intelligence, industrial robots are becoming more powerful, gaining a large variety of applications in intelligent factories. Pose perception, the aim of which is to obtain the joint coordinates of the robot in the camera coordinate system, has been proposed as a promising technology for multi-robot intelligent interaction. A large number of efforts have been made on studying robot pose perception. However, most of existing studies estimate the position of robot joints in 3D space by just using 2D color images, and perform pose perception by estimating the position of joint key points in the image, which could become infeasible in scenarios with various camera views and background environments. To address the issue, considering the information about the robot itself as a prior knowledge, we propose a novel approach for robot pose perception, named 3D-RPP. We adopt a 3D visual point cloud to estimate the rigid transformation of the camera coordinate system with respect to the robot base coordinate system, which effectively improves the accuracy of the obtained robot joint position in the camera coordinate system. We conduct extensive experiments on ROKAE xMate3 robot to investigate the performance of 3D-RPP, and the experimental results show that 3D-RPP could solve the pose perception problem well.

Interactive Art Design Based on Intelligent Sensors and Information Fusion Technology

In the era of rapid development of information technology, the application of smart sensors is becoming more and more extensive. All measurement and control equipment need to obtain raw data through sensors, and machines can also obtain various information through sensors. And this information, especially from the perspective of reliability, accuracy, and intelligent interaction, requires higher interactivity in people’s lives. In order to solve the problem that the existing interactive design is difficult and not intelligent enough, and the information fusion is not uniform enough, the results of the design have various deviations. This article intends to design through the use of smart sensors and information fusion technology to make an improvement to its interactive art design.

Artificial Intelligence Interactive Design System Based on Digital Multimedia Technology

The current extraction speed of artificial intelligence interactive elements is low, with the low effect, resulting in the poor effect of artificial intelligence interaction. Therefore, a new artificial intelligence interaction method has been developed for digital multimedia technology, performing analysis based on the current background of artificial intelligence, providing a good environment foundation for the interactive place, so that it can integrate artificial intelligence technology after interaction. Aiming at the current problems of digital multimedia technology in the use of artificial intelligence interaction design, innovative exploration of artificial intelligence technology will be carried out based on multimedia technology and innovative thinking. Based on the in-depth analysis of digital multimedia technology, the relationship between artificial intelligence technology and digital multimedia technology is analyzed, and an artificial intelligence interactive design system based on digital multimedia technology is proposed. Finally, the digital multimedia technology is verified on the artificial intelligence interactive design through case analysis.

Virtual sports rehabilitation and monitoring system for the elderly based on intelligent interaction and embedded system

Virtual sports rehabilitation and monitoring system for the elderly based on intelligent interaction and embedded system

Virtual sports rehabilitation and monitoring system for the elderly based on intelligent interaction and embedded system

This article uses medical electrode pads as the ECG sensor, introduces the signal into the subsequent conditioning circuit, sticks to the electrode pads on both sides of the human body's chest and abdomen, and ensures a firm fit. After each signal acquisition module works normally, connect each module to the wireless body area network terminal node for system networking test. System testing includes not only signal acquisition testing and network communication testing, but also host computer software testing. The BCG signal is collected by a piezoelectric film sensor, which converts the mechanical vibration in the human body into a variable voltage output. The results show that the system can accurately collect and process data, maintain good communication with the software platform, and has high reliability.

Research on Teaching Mode of College Subject—Based on Intelligent Interaction

Research on Teaching Mode of College Subject—Based on Intelligent Interaction

On the Application of Virtual Simulation in Teaching of Engineering Surveying and Mapping

Virtual systems aim to provide a virtual environment and intelligent interaction for building measurements. In the face of complex scene tasks, measurement process simulation and analysis can be completed in advance, so as to assist the actual construction. For the requirements in the virtual simulation teaching of surveying and mapping, the virtual measurement area and its corresponding surveying and mapping response methods are required. The paper presents 3-dimensional visual measurement area that makes the mapping and surveying works. In our experiment, we use the teacher platform that is a contemporary educational application based on cloud computing technology. It is a 3D digital curriculum possibility that integrates an interactive reading experience, multimedia learning materials, and learning aids. Based on the simulator process, we show an application of virtual simulation is a complex micro individual with prominent intelligent interaction characteristics in construction simulation scenarios, which has great research significance in both theory and practice. Taking this simulator prototype as an example, the paper comprehensively applies virtual simulation to carry out research on building key technologies. The paper presents the virtual simulation in teaching can improve students learning enthusiasm and learning efficiency in many ways such as teaching and learning.

New human-computer interaction regulations for ship safe driving based on multi-mode data interconnection

Based on the analysis of the multi-mode data of ship mechatronics and the new human-computer interaction regulations for safety driving, a new safety driving regulation based on multi-mode data is put forward. The new regulations for ship safe driving use mechanical and electrical data to form small-world data interconnection. Artificial intelligence and human-computer interaction operation information are used to integrate and communicate, and human-computer interaction data are incorporated to standardize driving behavior to integrate historical driving data, and finally, the standardized automatic self-driving is formed. The new human-computer interaction regulations formed by the safe driving system make it possible to solve and optimize the ship safe driving mode.

Research on Flexible Capacitive Sensors for Smart Textiles

Smart textiles are a new era of smart textiles that not only have traditional textile functions, but also have information collection, feedback, and multiple intelligent interaction functions with users. As a manifestation of the combination of art and technology in the textile field, smart textiles are of great significance to traditional textiles, clothing, home textiles, and wearable devices. From the perspectives of the background, technology, and development prospects of smart textiles, this article systematically analyses the application technology of smart textiles in practice. Starting from the current state of smart textile research, the article explains the promotion of new material technology to the research of smart textiles. Focus on the technical improvement of capacitive sensing equipment based on flexible fabrics, and find a sensing fabric structure with lower hysteresis, fast response time, good repeatability and stability through design experiments.

Toward Wisdom-Evolutionary and Primitive-Concise 6G: A New Paradigm of Semantic Communication Networks

The sixth generation (6G) mobile networks will reshape the world by offering instant, efficient, and intelligent hyper-connectivity, as envisioned by the previously proposed Ubiquitous-X 6G networks. Such hyper-massive and global connectivity will introduce tremendous challenges into the operation and management of 6G networks, calling for revolutionary theories and technological innovations. To this end, we propose a new route to boost network capabilities toward a wisdom-evolutionary and primitive-concise network (WePCN) vision for the Ubiquitous-X 6G network. In particular, we aim to concretize the evolution path toward the WePCN by first conceiving a new semantic representation framework, namely semantic base, and then establishing an intelligent and efficient semantic communication (IE-SC) network architecture. In the IE-SC architecture, a semantic intelligence plane is employed to interconnect the semantic-empowered physical-bearing layer, network protocol layer, and application-intent layer via semantic information flows. The proposed architecture integrates artificial intelligence and network technologies to enable intelligent interactions among various communication objects in 6G. It features a lower bandwidth requirement, less redundancy, and more accurate intent identification. We also present a brief review of recent advances in semantic communications and highlight potential use cases, complemented by a range of open challenges for 6G.

Internet of Things Intelligent Interaction Technology Using Deep Learning in Public Interaction Design

Internet of Things Intelligent Interaction Technology Using Deep Learning in Public Interaction Design

DLD: An Optimized Chinese Speech Recognition Model Based on Deep Learning

Speech recognition technology has played an indispensable role in realizing human‐computer intelligent interaction. However, most of the current Chinese speech recognition systems are provided online or offline models with low accuracy and poor performance. To improve the performance of offline Chinese speech recognition, we propose a hybrid acoustic model of deep convolutional neural network, long short‐term memory, and deep neural network (DCNN‐LSTM‐DNN, DLD). This model utilizes DCNN to reduce frequency variation and adds a batch normalization (BN) layer after its convolutional layer to ensure the stability of data distribution, and then use LSTM to effectively solve the gradient vanishing problem. Finally, the fully connected structure of DNN is utilized to efficiently map the input features into a separable space, which is helpful for data classification. Therefore, leveraging the strengths of DCNN, LSTM, and DNN by combining them into a unified architecture can effectively improve speech recognition performance. Our model was tested on the open Chinese speech database THCHS‐30 released by the Center for Speech and Language Technology (CSLT) of Tsinghua University, and it was concluded that the DLD model with 3 layers of LSTM and 3 layers of DNN had the best performance, reaching 13.49% of words error rate (WER).

Editorial: On the “Human” in Human-Artificial Intelligence Interaction

Editorial: On the “Human” in Human-Artificial Intelligence Interaction

Sensors and Artificial Intelligence Methods and Algorithms for Human-Computer Intelligent Interaction: A Systematic Mapping Study.

To equip computers with human communication skills and to enable natural interaction between the computer and a human, intelligent solutions are required based on artificial intelligence (AI) methods, algorithms, and sensor technology. This study aimed at identifying and analyzing the state-of-the-art AI methods and algorithms and sensors technology in existing human–computer intelligent interaction (HCII) research to explore trends in HCII research, categorize existing evidence, and identify potential directions for future research. We conduct a systematic mapping study of the HCII body of research. Four hundred fifty-four studies published in various journals and conferences between 2010 and 2021 were identified and analyzed. Studies in the HCII and IUI fields have primarily been focused on intelligent recognition of emotion, gestures, and facial expressions using sensors technology, such as the camera, EEG, Kinect, wearable sensors, eye tracker, gyroscope, and others. Researchers most often apply deep-learning and instance-based AI methods and algorithms. The support sector machine (SVM) is the most widely used algorithm for various kinds of recognition, primarily an emotion, facial expression, and gesture. The convolutional neural network (CNN) is the often-used deep-learning algorithm for emotion recognition, facial recognition, and gesture recognition solutions.

Personality trait by intelligence interaction effects on grades tend to be synergistic

BackgroundEarlier research has identified both synergistic and compensatory personality traits by intelligence interaction effects on academic performance.MethodsThe present study employed data on intelligence, personality traits, and academic performance in the 1997 National Longitudinal Survey of Youth (NLSY97, N = 8984).ResultsSome intelligence by personality trait interaction effects, mainly involving indicators of dependability, on high school grades were identified. The interaction effects tended to be synergistic, meaning that the association between the trait and grades tended to strengthen with increased intelligence. A positive association between intelligence and the reliability in the measurement of a dependability composite score accounted for a substantial portion of the synergistic dependability by intelligence interaction effect on academic performance.ConclusionsPersonality trait by intelligence interaction effects on academic performance tend to be synergistic and may, at least to some degree, be due to a positive association between intelligence and reliability in the measurement of personality traits.