NU-InNet: Thai Food Image Recognition Using Convolutional Neural Networks on Smartphone

Currently, Convolutional Neural Networks (CNN) have been widely used in many applications. Image recognition is one of the applications utilizing CNN. For most of the research in this field, CNN is used mainly to increase the effectiveness of the recognition. However, the processing time and the amount of the parameters (or model size) are not taken into account as the main factors. In this paper, the image recognition for Thai food using a smartphone is studied. The processing time and the model size are reduced so that they can be properly used with smartphones. A new network called NUInNet (Naresuan University Inception Network) that adopts the concept of Inception module used in GoogLeNet is proposed in the paper. It is applied and tested with Thai food database called THFOOD-50, which contains 50 kinds of famousThai food. It is found that NU-InNet can reduce the processing time and the model size by the factors of 2 and 10, respectively, comparing to those obtained from GoogLeNet while maintaining the recognition precision to the same level as GoogLeNet. This significant reduction in the processing time and the model size using the proposed network can certainly satisfy users for Thai-food recognition application in a smartphone

Factors Affecting SMEs' Intention to Adopt a Mobile Travel Application based on the Unified Theory of Acceptance and Use of Technology (UTAUT-2)

This study is part of a government research project which aims to synthesise the current evidence on the factors affecting the intention of mobile application adoption called ‘Tripper Notifier Application’ (TNA) for the hospitality and tourism industrial sector in Thailand. The focus is on small and medium enterprises (SMEs), which emphasize restaurants, hotels, and attraction sites. The present article examines various factors influencing the intention to use such applications by employing the Unified Theory of Acceptance and Use of Technology 2 (UTAUT-2) as the theoretical underpinning of this research paradigm. Using 84 selected research papers in Scopus published between 2020 and 2022, A thematic analysis incorporating a grounded theory approach to systematically generate themes was conducted, and the findings found three main themes, including business transformation capabilities (BTC), digital transformation capabilities (DTC), and personal innovativeness (PI), as an extension of UTAUT-2 as mediator and moderator variables. To this end, the study fills the research gaps and extends the UTAUT-2 framework by including an initiative of twelve inside attributes-based lines, including performance expectancy, effort expectancy, social influence, facilitating conditions, hedonic motivation, price value, habit behavior, behavioral intention, and use behavior, together with three moderators: age, gender, and experience. Finally, the context dimensions of the UTAUT-2 extensions were mapped to highlight all the constructs of the TNA adoption framework for future research directions. The novel contribution of this study is to fill the gap with both theoretical and practical knowledge. On the theoretical level, this study constitutes constructs based on UTAUT-2 theory as a research-based setting to fill a gap in research. On the practical level, it provides insights and information about new capabilities that SME owners, managers, and practitioners should consider in order to differentiate their own capabilities. Doi: 10.28991/esj-2021-SP1-014 Full Text: PD

Reducing False Detection during Inspection of HDD using Super Resolution Image Processing and Deep Learning

High false detection rates are a key reliability challenge in the Hard Disk Drive (HDD) industry. Therefore, automatic visual inspection is increasingly employed for HDD inspection. In order to improve the quality and reliability of HDD products, the false detection rate must be reduced. This paper presents a super-resolution image-based method for improving the performance of Head Gimbals Assembly (HGA) inspection. The experimental results confirm the efficiency of the super-resolution image processing for improving automatic inspection of defects such as pad burning and micro contaminations. Moreover, combining super resolution image processing with deep learning reduces the false detection rate and improves the accuracy of HGA inspection

Image based contamination detection on hard disk head gimbal assembly

Contamination may appear in various stages of hard disk manufacturing including the head gimbal assembly. Currently, the detection of contamination requires manual intervention. An image based automatic contamination detection strategy is therefore presented. After a preprocessing step, the contamination detection algorithm first detects potential areas of contamination using circle detection. Then, each of the contamination contenders are classified as either a contamination or noncontamination using a set of specific rules. The algorithm has been tested on 1,050 head gimbal assembly images of which 313 depicted contaminations. Our preliminary results yields an accuracy of 73.8% with a false negative rate of 34.8% and a false positive rate of 23.6%. Future work includes finetuning the contamination classification rules

Detection of micro contamination in hard disk drives using maximum likelihood estimation and angle detection

Micro contamination is one of the critical defects that occur on the head gimbal assembly (HGA). The HGA is a key component of the read/write assembly of a hard disk drive. This paper presents an image-based automatic inspection method for micro-contamination detection. Maximum likelihood estimation combined with angle measurements are proposed for identifying defects. The performance of the proposed maximum likelihood estimation and angle measurement method is compared to previous angle measurement and intensity thresholding methods. The experimental results show that the fusion of maximum likelihood estimation and angle measurements outperforms the angle measurement and intensity thresholding method with an accuracy of 87.9 % compared the accuracy of 80.1% reported in previous work

An Area-Based Prior Value Method for Detectionof Micro Contamination in Hard Disk Drives

This paper presents a new area-based prior value technique for the improvement of automatic visual inspection in hard disk drive manufacturing. Micro-contaminations are detected on the air-bearing surface of the head gimbal assembly. The new area-based prior value technique uses the locations of contaminations that appear in the inspection area. The experimental results validate the efficiency of the new detection method on low-resolution images, as the proposed method yielded 93.1% accuracy

Reducing False Detection during Inspection of HDD using Super Resolution Image Processing and Deep Learning

High false detection rates are a key reliability challenge in the Hard Disk Drive (HDD) industry. Therefore, automatic visual inspection is increasingly employed for HDD inspection. In order to improve the quality and reliability of HDD products, the false detection rate must be reduced. This paper presents a super - resolution image - based method for improving the performance of Head Gimbals Assembly (HGA) inspectio n. The experimental results confirm the efficiency of the super - resolution image processing for improving automatic inspection of defects such as pad burning and micro contaminations. Moreover, combining super resolution image processing with deep learning reduces the false detection rate and improves the accuracy of HGA inspection

Detection of micro contamination in hard disk drives using angle measurements and Bayesian classification

Micro Contamination has been a major critical defects peculiar to the hard disk drive assembly particularly to head gimbal assembly compartment and it occurs mainly on the airbearing surface. Consequently, reliable measures to improve the read/write process in a defect of this nature requires an inspection process to check and verify the hard disk components. Therefore, we proposed an image-based automatic inspection method for micro-contamination detection. This proposed inspection approach leverage on angle measurements and Bayesian classification for identifying detects. However, a comparative analysis between this technique and previous angle measurement and intensity threshold methods was carried out to ascertain the performance improvement. The experimental results showed that this method is robust and outperformed the existing methods in literature. Meanwhile, the improvement recorded showed that this method offers a minimal false detection rate for real-time practical applications