NON-VERBAL HUMAN-ROBOT INTERACTION USING NEURAL NETWORK FOR THE APPLICATION OF SERVICE ROBOT

Service ro~ots; .re preVl'I.Iling in many industries to assis~ humans in c~nduc,ing repetitive task.;, which require a natural interaction called Human Robot Interaction (HRI). In particular, nonverbal HRI plays an important role in social interactions, which highlights the need to accurately detect the subject's attention by evaluating the programmed cues. 1n this paper, a conceptual attentiveness model algorithm called Attentive Recognition Model (ARM) is proposed to recognize a person's aLi:~ntivencss, which improves tne a-::Jtac~· of detection and subjective experience during nonverbal dRI using three combined detection models: face tracking, iris tracking and eye blin:.:ing. The face tracking model was trained using a Long Short-Term Memory (LSTM) neural network, which is based on deep learning. Meanwhile, the iris tracking and eye blinking use a mathematical model. The eye blinking model uses a random face landmark point to calculate the Eye Aspect Ratio (EAR), which is much more reliable compared to the prior method, which could detect a person blinking at a further distance even if the person was not blinking. The conducted e~ periments for face and iris tracking were able to detect direction up to 2 meters. Meanwhile, the tested eye blinking model gave an accuracy of 83.33% at up to 2 meters. The overall attentive accuracy of ARM was up to 85.7%. The experin1ents showed that the service robot was able to understand the programmed cues and hence perform certain tasks, such as approaching the interested person. Robot perkhidmatan lazim dalam banyak industri untuk membantu manusia menjalankan tugas berulang, yang memerlukan interaksi semula jadi yang dipanggil Interaksi Robot Manusia (HRI). Khususnya, HRI bukan lisan memainkan peranan penting dalam interaksi sosial, yang menonjolkan keperluan untuk mengesan perhatian subjek dengan tepat dengan menilai isyarat yang diprogramkan. Dalam makalah ini, algoritma model perhatian konseptual yang dipanggil Model Pengecaman Perhatian (ARM) dicadangkan untuk mengenali perhatian seseorang, yang meningkatkan ketepatan pengesanan dan pengalaman subjektif semasa HRI bukan lisan menggunakan tiga model pengesanan gabungan: pengesanan muka, pengesanan iris dan mata berkedip .. Model penjejakan muka telah dilatih menggunakan rangkaian saraf Memori Jangka Pendek Panjang (LSTM), yang berdasarkan pembelajaran mendalam. Manakala, pengesanan iris dan mata berkelip menggunakan model matematik. Model mata berkelip menggunakan titik mercu tanda muka rawak untuk mengira Nisbah Aspek Mata (EAR), yangjauh lebih dipercayai berbanding kaedah sebelumnya, yang boleh mengesan seseorang berkelip pada jarak yang lebih jauh walaupun orang itu tidak berkelip. Eksperin1en yang dijalankan untuk pengesanan muka dan iris dapat mengesan arah sehingga 2 meter. Sementara itu, model berkelip mata yang diuji memberikan ketepata

New design of tuned vibration absorber for wide frequency range application

Uncontrolled vibrations can leave a bad impression to the machine, structure, and human. For example, vibration on machine can damage the equipment, decrease the machine lifetime and also causing the safety factor problems. Therefore, a vibration absorber is obliged to reduce these vibrations. The present paper investigated a new design of tuned vibration absorber (TVA). The proposed TVA is: (1) light in weight and small in scale, which suitable for mobility purpose, and (2) can addressed a broad frequency range of application. However in this paper, the effectiveness of new design of TVA to reduce the vibration is not covered since the TVA has been proved in the previous study being able to reduce the vibration significantly. The frequency range of absorber was determined through finite element analysis (FEA) and validated with the experimental result. The aim is to enhance the range of frequency that TVA can tune from 0 to 1000 Hz. In order to generate the result, SolidWorks® software was used in the finite element analysis and DEWEsoft equipment was used in experiment. The results in the finite element analysis showed that the maximum frequency that TVA can tune is 800 Hz while from experiment is 980 Hz in the experimental analysis. Although there is an error about 18% between FEA and experiment, the findings are still significant because the maximum frequency of the new design TVA can reach is approximate to 1000 Hz. This study concludes that the new design of TVA which is small in scale and light in weight is able to reduce the structural vibration extensively if it is tuned correctly to the targeted frequency range of 0 to 980 Hz