Man-machine Communication Research Articles

IMPLICATIONS OF SPEECH RECOGNITION THROUGH NLP: AN OVERVIEW

In the current state of digitalization researchers have found a leading-edge technology that delivers information to users from the digital machine and vice-versa and i.e. Natural Language Processing (NLP), one of an AI branch that made a difference. NLP is a computational representation and analysis of human language, which globally aims to facilitate naturally occurring interaction between computers and individuals whether phonically, calligraphically or textual. NLP has various booming sides in advanced world and has expands its applications in different domains. One of its application Speech recognition is a large field of research for modern-day researchers. Speech recognition, also known as automatic speech recognition (ASR) or speech-to-text, is capable of handling human speech in a written format. The main focus is on development of recognition system which enhances human-to-human communication by permitting man-machine communication through the processing of texts or speeches. It is a technology that makes it possible for people to communicate with devices. Several applications of voice recognition systems are in place and all understand diverse scrutiny challenges.This paper captures the necessity of how speech recognition actually functions, types of model, resolving challenges of ASR. Deep study on functioning of Alexa and Siri.

Implementation of a Hybrid Intelligence System Enabling the Effectiveness Assessment of Interaction Channels Use in HMI.

The article presents a novel idea of Interaction Quality Sensor (IQS), introduced in the complete solution of Hybrid INTelligence (HINT) architecture for intelligent control systems. The proposed system is designed to use and prioritize multiple information channels (speech, images, videos) in order to optimize the information flow efficiency of interaction in HMI systems. The proposed architecture is implemented and validated in a real-world application of training unskilled workers-new employees (with lower competencies and/or a language barrier). With the help of the HINT system, the man-machine communication information channels are deliberately chosen based on IQS readouts to enable an untrained, inexperienced, foreign employee candidate to become a good worker, while not requiring the presence of either an interpreter or an expert during training. The proposed implementation is in line with the labor market trend, which displays significant fluctuations. The HINT system is designed to activate human resources and support organizations/enterprises in the effective assimilation of employees to the tasks performed on the production assembly line. The market need of solving this noticeable problem was caused by a large migration of employees within (and between) enterprises. The research results presented in the work show significant benefits of the methods used, while supporting multilingualism and optimizing the preselection of information channels.

Construction of Intelligent Information Exchange Platform for Cultural and Creative Industry Based on Cloud Computing

The main task and purpose of information exchange platform construction is to comprehensively build an intelligent, competitive and large-scale technology transfer innovative information exchange ecological platform system based on cloud computing, and to actively promote economic efficient and high-quality development based on the artistic and aesthetic advantages of the cultural and creative industry. Digital information, cloud computing, large-scale space, time-travel, intelligent thinking, advanced technology and big data analysis are covering more and more media in the iterative advancement of cultural and creative industry, gradually increasing the audience's intelligent and immersive virtual experience effect. Intelligent communication platform design of cloud computing visual elements has been an important element of cultural and creative industry creation in recent years. Based on the iterative innovative design of cloud computing visual cultural elements, this paper analyzes the current situation and development of cultural and creative industry relying on new media interaction design from three perspectives of "intelligence", "experience" and "interaction". It proposes the design framework of flexible intelligent progressive experience and communication platform, focuses on the analysis of the innovative and integrated expression of cloud computing visual element technology and art, and encourages the audience to actively participate in the interaction design and construction of cultural and creative industries from the perspective of art cognition. Further realize the intelligent "cloud" media to collect information, analyze data and exchange feedback, so as to provide flexible, high adaptability, high interoperability of contact-free intelligent man-machine communication platform, art cognitive communication platform and humanized interactive services.

SME 4.0: Machine learning framework for real-time machine health monitoring system

Over the past ten years, manufacturers and consumers have become increasingly interested in the applications of smart, sustainable, and autonomous systems in the industry and in everyday life. Due to the recent industrial revolution (Industry 4.0), most of the existing Small and Medium-sized Enterprises (SMEs) also want to adapt their work environment into the smart system by the applications of these technologies such as the Industrial Internet of Things (IIoT), Artificial Intelligence (AI) techniques, Machine Learning Algorithm (MLA), Internet Communication Technology (ICT), and Cyber-Physical System (CPS). Because they are very much interested in maximizing productivity, machine availability, reliability, and customer satisfaction in this competitive industrial world. This research study particularly focuses on the Predictive Maintenance (PdM) activity of critical machines and their components in the SME based on the maintenance history dataset through the application of the supervised machine learning algorithm such as Logistics Regression (LR) and K-Means (K-Nearest Neighbor) approaches. In accordance, the real-time case study is presented in SMEs in the southern region of Tamil Nadu, India with two-phase activities. Initially, the optimal failure rate of the machines is predicted by the utilization of LR trained models. Then trigger the man-machine communication and suitable decision-making process of service and maintenance activity through the application of the K-Mean approaches. The main objective of this research study is to organize the smart PdM activity of the smart factory systems in SMEs with the application of MLA based on the real-time maintenance history dataset.

Creation of speech corpus for emotion analysis in Gujarati language and its evaluation by various speech parameters

In the last couple of years emotion recognition has proven its significance in the area of artificial intelligence and man machine communication. Emotion recognition can be done using speech and image (facial expression), this paper deals with SER (speech emotion recognition) only. For emotion recognition emotional speech database is essential. In this paper we have proposed emotional database which is developed in Gujarati language, one of the official’s language of India. The proposed speech corpus bifurcate six emotional states as: sadness, surprise, anger, disgust, fear, happiness. To observe effect of different emotions, analysis of proposed Gujarati speech database is carried out using efficient speech parameters like pitch, energy and MFCC using MATLAB Software.

Investigation of multilingual and mixed-lingual emotion recognition using enhanced cues with data augmentation

In the past decade, research for improving man–machine communication has focused on emotion recognition using audio cues. Several effective monolingual, multilingual, and cross-corpus speech emotion recognition (SER) systems have been developed; however, they are limited to recognizing emotions from databases of monolingual discourse, primarily in either categorical or dimensional emotion space. For multilingual countries and federations such as India, Russia, and the European Union, these limitations can be problematic. Furthermore, in an environment of mixed diversified languages, the performance of existing models is unclear. To address these issues, we propose an innovative mixed-lingual SER system that considers five diverse languages, including dialect variability. Mixed-lingual corpora are developed from available standard speech emotion databases. Furthermore, a compact feature set having a unique set of speech feature functionals with a distinctive set of enhanced perceptual features and modified H-coefficients is proposed. Against existing large feature sets, the proposed compact feature set is robust and effective to perform the dual task of significantly recognizing different emotions from multilingual SER systems also, along with the mixed-lingual SER systems in both emotion spaces of categorical and dimensional. In the proposed SER system, to overcome the skewness of SER system performance for recognizing certain emotions, a data augmentation method is then incorporated. Furthermore, the proposed SER system is designed to efficiently recognize even the extreme emotions of boredom, disgust, sadness, and surprise in both emotion spaces. The proposed SER system is compared to existing SER systems, and the comparison results demonstrate that the proposed system outperformed existing systems.

Emotion Recognition of Manipuri Speech using Convolution Neural Network

over the recent years much advancement are made in terms of artificial intelligence, machine learning, human-machine interaction etc. Voice interaction with the machine or giving command to it to perform a specific task is increasingly popular. Many consumer electronics are integrated with SIRI, Alexa, cortana, Google assist etc. But machines have limitation that they cannot interact with a person like a human conversational partner. It cannot recognize Human Emotion and react to them. Emotion Recognition from speech is a cutting edge research topic in the Human machines Interaction field. There is a demand to design a more rugged man-machine communication system, as machines are indispensable to our lives. Many researchers are working currently on speech emotion recognition(SER) to improve the man machines interaction. To achieve this goal, a computer should be able to recognize emotional states and react to them in the same way as we humans do. The effectiveness of the speech emotion recognition(SER) system depends on quality of extracted features and the type of classifiers used . In this paper we tried to identify four basic emotions: anger, sadness, neutral, happiness from speech. Here we used audio file of short Manipuri speech taken from movies as training and testing dataset . This paper use CNN to identify four different emotions using MFCC (Mel Frequency Cepstral Coefficient )as features extraction technique from speech.

Grassmann manifold based dynamic hand gesture recognition using depth data

In this paper, we propose a novel Grassmann manifold based framework for dynamic hand gesture recognition from depth data. Automated dynamic hand gesture recognition is important for improving man-machine communication and understanding human behavior. It finds various applications such as human computer interaction, ambient assisted living, automated driver assisted systems. We use depth data or skeleton information to detect the fingertip and store the fingertip points to create the trajectory. In fingertip detection using depth data first we detect the hand using the depth data and used hand shape properties such as finger thickness, finger length, finger width and finger orientation angle to find the shape of the hand. If skeleton data is available we use skeleton information to detect the fingertip in each frame. Then geometrical features are extracted and a unique gesture subspaces created using SVD for each feature vector matrix of each gesture set. These gesture subspaces lie on a Grassmann manifold and capture the intra-class variations and increase the inter-class discriminatory power. We apply Grassmann manifold based discriminant analysis for recognizing each test gesture. We perform experiments on standard datasets and the results show that we have achieved recognition accuracy comparable to the state-of-the-art.

An Integrated Management System of Man-machine Communication for Tailings Pond Based on RSCIC

Tailings pond plays a very important role in the construction of mine engineering. At the same time, tailings pond also produces huge pollution to the environment. If dam break occurs, it will cause loss of people and property. In this paper, a tailings pond management system based on RSCIC is studied, the monitoring content of the system is determined, the more innovative system and hardware are emphasized, the program flow chart of digital processing is put forward. Adopting scientific and effective means to manage the tailing dam automatically can not only strengthen the safety protection of the tailing dam, but also provide a certain theoretical basis for the construction of other digital tailings dam projects.

Facial Expression Recognition Using Convolutional Adaptive Neuro-Fuzzy Inference System (CANFIS)

Facial Expression gives significant information about the emotion of a person. In the research area of computer vision, automated facial expression recognition is an important area because of its significance in Human Computer Interaction (HCI). To improve the level of interaction in man machine communication systems, extraction and validation of emotional information by facial expression analysis plays a major role. The proposed method in facial expression recognition is based on extracted features like mean, mode, minimum intensity, maximum intenstiy, Perimeter, Standard Deviation (S.D), orientation (about X and Y axis) and centroid (about X and Y axis). A random mask is generated to find the convolution of the feature set from the pooling layer to the fully connected layers of the Feed Forward (FF) Artificial Neural Network (ANN). A neuro-fuzzy based system called Adaptive Neuro Fuzzy Inference System (ANFIS) is used for classification by tuning the weight vectors in the fully connected layers of ANN which produces a robust algorithm called the Convolutional Adaptive Neuro-Fuzzy (CANFIS). The performance of this method is evaluated using Recognition Efficiency (RE) which yields an effective recognition system in comparison with the conventional ANFIS structure.

Terminologia kohtaa käytettävyyden. Terminologisen käytettävyyden ydintä rakentamassa

In this paper the principles of usability and user interface design and those of terminology work are brought together in order to introduce the concept of “terminological usability”. The concept forms a basis for developing an additional method for usability research. As long as there has been technology for man-machine communication, there has also been a variety of design rules and design guidelines to assess the usability of user interfaces. We discuss those rules and guidelines that concern linguistic elements on computer and software user interfaces and contrast them with the principles of a “good term” that can be found in manuals and textbooks for terminology work. Acceptability, consistency and transparency that are shared with both of these approaches are scrutinized more detailed. This paper is part of an ongoing research project that focuses on the usability of user interfaces from terminological perspective.

Feature Reduction Using Genetic Algorithm for Cognitive Man-Machine Communication

Electroencephalographic (EEG) signals are usually comprised of high-dimensional feature space. This work aims to assess the effect of reducing the number of features extracted from EEG recordings. A methodology is proposed that combines brain imaging and machine learning techniques to predict the cognitive state of the subjects whether they are feeling themselves in a safe or dangerous environment. The changes in the brain state are correlated with power modulations of oscillatory rhythms in recorded EEG signals called ERD / ERS (Event-related De-synchronization / Synchronization). In order to determine the optimized number of features, Genetic Algorithm (GA) will be used. GA has played instrumental role in solving optimization problems from diverse fields. In various studies and researches for Cognitive Man-Machine Communication, the algorithm has been used as an effective method to extract an optimal set of features.

Pictorial-verbal Modality of Communication

Natural languages are introspective, and here is an obvious discrepancy between feeling the language and theorizing about it by the means of a traditional linguistic metalanguage. This discrepancy conceals from our eyes some important issues. The aim consists in considering one of them and discussing the possibility of using pictorial-verbal modality for simulating introspective verbal language within man-machine communication. Principles of realization and general challenges of the approach are also discussed.

Ultrasonic Welding Control System Based on Embedded Multitasking Research

To realize RTX, a 32-bit ARM processor platform and Keil was used to provide a graphical interface. Through real-time task allocation and Keil provided by TCP/IP interface, an embedded ultrasonic welding control system was realized. The ultrasonic welding control system designed for the testing of software and hardware, and by comparison with field work, proved that the application of multitasking can obviously improve the welding effect through welding control system, friendly man-machine interface, convenient communication and monitoring.

Design Proposed Features Extraction Recognition System of Latin Handwritten Text Based on 3D-Discrete Multiwavelet Transform

On-line handwriting recognition is the task of determining what letters or words are present in handwritten text. It is of significant benefit to man-machine communication and can assist in the automatic processing of handwritten documents. It is a subtask of the Optical Character Recognition (OCR), whose domain can be machine-print only.The introduced system is a character-based recognition and it is a writer independent system. The recognition responsibility of the proposed system is for 52 character classes [uppercases (A-Z) and the lowercases (a-z)]. The suggested system includes the essential stages needed for most of the pattern recognition systems. These stages are the preprocessing stage, the features extraction stage, the pattern matching and classification stage and the postprocessing stage. The proposed method employs the 3 Dimensional Multiwavelet transform 3D-DMWTCS using multiresolution image decomposition techniques working together with multiple classification methods as a powerful classifier. The classification stage is designed by using a minimum distance classifier depending on Euclidean Distance which has a high speed performance. The system design also includes a modest postprocessing stage that makes a consistency between the recognized characters within the same word in relation to their upper and lower cases.The overall classification accuracy of proposed systems can be obtained are 95.305 percent with 3D-DMWTCS based on the Rimes database.

Towards Virtual Presence Based on Multimodal Man-Machine Communication: A Remote Operation Support System for Industrial Robots

Worldwide, the focus on industrial development is on achieving smaller and smaller batch sizes in production. This has led to the need for providing cost efficient support to robot users. In this study, a new method for the remote operation of industrial robot systems is introduced. The method is based upon multi-modal man-machine communication and sensor bridging to transfer huge amounts of information from the industrial robot cell to the brain of the remote operator.

Research on the Design and Realization of Electrical Training System Based on the Virtual Reality Technology

This paper is mainly about the process of design and realization of electrical training software based on the virtual reality technology. Some modern computer technique are used in this paper, such as, interface design technology, animation design and manufacture technology, 3D modeling and rendering technology, virtual reality technology, network technology, database technology and the Man-machine interface sensor and communication technology. The electrical technique skill education in occupation post analysis, training project and key information extraction, the analysis of the teaching object have been realized. The overall structure of the system design, media design, copy and script writing have been accomplished. The virtual reality simulation software for technique training introduced in this paper has the characteristics of strong professionalism, situations, procedural, interactivity and intelligence. It is more realistic, more manufacturability and more interactivity than the traditional simulation teaching software. Therefore, it can achieve better teaching training effects. Furthermore it has common hardware training equipment incomparable advantages.

Design of Automatic Control System for Intelligent Micro Drip Irrigation Based on PLC Technology

This paper analyzes the control requirements of drip irrigation system, designed automatic drip irrigation control system with IPC and PLC as control core, using WinCC as the man-machine communication window, combined with frequency, field bus and sensor technology. This paper focuses on the system hardware design, system control software and system monitoring software design. Through the field bus, the central management computer decide reasonable irrigation decision according to the soil moisture information, crop water requirement characteristics and predetermined irrigation scheme, and then issued a directive to the solenoid valve scattered, automatic carry out proper, required precision irrigation control. The practical application shows that: the system has stable working performance, friendly interface, improve labor productivity. The system can be expanded easily in terms of structure and function, can satisfy the application of different scale, demand, cost, have certain application prospect.

Classification of the Spoken Hindi Partially Reduplicated Words using Artificial Neural Network

The most ordinary way of information exchange is Speech. It provides an efficient way of man-machine communication using speech interfacing. Speech interfacing involves two process, speech synthesis and speech recognition. Speech recognition allows a computer to identify the words that a person speaks to a microphone or telephone. The two main mechanism, used in speech recognition, are signal processing mechanism at front-end and pattern matching mechanism at back-end. In this paper, a setup for recognition of Spoken Hindi Partially Reduplicated Words (SHPRW), that uses Mel frequency cepstral coefficients at front-end and artificial neural networks at back-end has been developed to perform the experiment. General Terms Automatic Speech Recognition, Endpoint Detection, Feature Extraction, Pattern Recognition, Classification, Recognition Rate.

Study of Evaluation Method about the Presswork Quality of Digital Inkjet System

At present, the productivity benefits of inkjet printing technology with high speed shows a rapid expanding trends, however, the precondition of which is its high quality presswork. Therefore, one of the top priorities now is to find better way to evaluate the presswork quality scientifically and effectively. A new parameter, ink dot fidelity, was introduced as an evaluation criterion to study the presswork quality in this paper. The four evident effect factors that fluctuated more during the printing process were chosen, such as the paper transmission speed, the print head (nozzle) voltage, the ink cartridge pressure and the distance between the print head and paper, to make the scientific orthogonal experiment design. The fluctuation rules of the ink dot fidelity corresponding to each effecting factors were obtained through intuitively analyzing of the orthogonal tables and the effect curves. Based on the experiment, the test data were fitted and optimized by using the mathematical analysis software of MATLAB and the least-square method. And then the mathematical model for ink dot fidelity and these affecting factors were created. Finally, the user interface (GUI) design platform was developed to improve the man-machine communication and the ink dot fidelity, or the presswork quality.