Pub Date : 2019-03-01DOI: 10.1109/ICTEMSYS.2019.8695970
Pattaraporn Taptimtong, C. Mitsantisuk, Kanyakorn Sripattanaon, Chayanit Duangkaew, Nichakul Pewleungsawat
in this paper, we proposed the methods of object detection and object classification to obtain the location information of each objects on the placement mat through the state diagram process using Vision Builder for Automated inspection (AI). By using the state diagram design detect and classify object on placement mat found that the state diagram can detect and classify almost it objects, both objects with similar surface pattern and objects with similar size. The location of the objects data can be detected and classified have the accuracy is about ±0.5 millimeter. And after using this object’s location data with the automation system, it was found that the robot moved to the position of the object correctly and was able to pick the object for assembly.
在本文中，我们提出了物体检测和物体分类的方法，利用Vision Builder for Automated inspection (AI)通过状态图处理获得放置垫上每个物体的位置信息。通过使用状态图设计对放置垫上的物体进行检测和分类，发现状态图几乎可以对表面图案相似的物体和尺寸相似的物体进行检测和分类。对物体的位置数据进行检测和分类，精度约为±0.5毫米。将该物体的位置数据与自动化系统结合使用后，发现机器人能够正确地移动到物体的位置，并能够选择物体进行组装。
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Pub Date : 2019-03-01DOI: 10.1109/ICTEMSYS.2019.8695956
E. Phaisangittisagul, Y. Koobkrabee, K. Wirojborisuth, T. Ratanasrimetha, S. Aummaro
In recent years, there has been a great interest in online advertising not only to promote products and services but to build a brand of the company as well. To satisfy customer needs, some businesses apply intelligent technology to advertise their products and services based on customer interests. Other advertisers allow customers or members to upload their promotions using image and/or message to advertise their businesses and services. However, filtering of promotional advertising is an essential part to detect improper information before posting on the websites and social media. As a result, a model to classify promotional advertising is proposed to identify whether relevant promotion content for a specific business or service in order to meet precise customers’ attention. The proposed algorithm in this study based on deep learning is designed to handle promotional image and message in competition with the 2nd KU Data Science Boot Camp 2018. Its performance is evaluated on the promotional advertising data provided by Wongnai. Finally, the accuracy of the proposed method can achieve satisfactory performance of 82.95% in testing data.
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Pub Date : 2019-03-01DOI: 10.1109/ICTEMSYS.2019.8695955
Nopphon Phringmongkol, P. Ratanaworabhan
At present, antivirus software backed by database of virus signatures is the most popular solution to malware detection problem. Even though its shortfalls are well-known - it requires large database that needs to be updated constantly and it is vulnerable to zero-day exploit - the security community has not successfully come up with better alternatives to it. However, the advent of multicores allows us to revisit this problem and look for alternatives that were deemed inefficient with previous generations of hardware.This paper proposes a lightweight dynamic analysis scheme that scans and executes objects allocated in the main memory. Our scheme looks for the presence of NOP sleds, which signals the existence of malware. Separate threads are spawn or woken up to perform object execution in sandboxed environment. This action takes place whenever applications allocate objects in memory. Extra CPU cores can execute these threads independently in parallel, providing close to ideal speedup. Our solution obviates the need for the virus database and can protect against zero-day exploit. We show that our dynamic analysis approach incurs low overhead, offers attractive false positive rate, and maintains zero false negative rate by design.
{"title":"Exploiting Extra CPU Cores to Detect NOP Sleds Using Sandboxed Execution","authors":"Nopphon Phringmongkol, P. Ratanaworabhan","doi":"10.1109/ICTEMSYS.2019.8695955","DOIUrl":"https://doi.org/10.1109/ICTEMSYS.2019.8695955","url":null,"abstract":"At present, antivirus software backed by database of virus signatures is the most popular solution to malware detection problem. Even though its shortfalls are well-known - it requires large database that needs to be updated constantly and it is vulnerable to zero-day exploit - the security community has not successfully come up with better alternatives to it. However, the advent of multicores allows us to revisit this problem and look for alternatives that were deemed inefficient with previous generations of hardware.This paper proposes a lightweight dynamic analysis scheme that scans and executes objects allocated in the main memory. Our scheme looks for the presence of NOP sleds, which signals the existence of malware. Separate threads are spawn or woken up to perform object execution in sandboxed environment. This action takes place whenever applications allocate objects in memory. Extra CPU cores can execute these threads independently in parallel, providing close to ideal speedup. Our solution obviates the need for the virus database and can protect against zero-day exploit. We show that our dynamic analysis approach incurs low overhead, offers attractive false positive rate, and maintains zero false negative rate by design.","PeriodicalId":220516,"journal":{"name":"2019 10th International Conference of Information and Communication Technology for Embedded Systems (IC-ICTES)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133479719","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-03-01DOI: 10.1109/ICTEMSYS.2019.8695928
Sanyapong Youkachen, M. Ruchanurucks, Teera Phatrapomnant, H. Kaneko
Defects on steel strip surface can long-term cause undesirable effects, since they make physical and/or chemical properties mismatched from steel's specification. Nowadays, automatic visual-based surface inspection is adopted, in order to detect the defects on steel strip surface after being produced. Moreover, since these defects appear in wide variety of forms and various classes, machine learning methods are generally involved to visual surface inspection for coping with these appearances. In this paper, we present a novel defect detection model to perform defect segmentation of hot-rolled steel strip surface, by using Convolutional Auto-Encoder (CAE) and sharpening process to extract the defect features of input image, then applied postprocessing for visualization. In the experiments, the NEU database, which provides six kinds of typical surface defects of hot-rolled steel strip, was applied to evaluate the efficiency of the proposed model. This database also provides difficulty challenges regarding diversity of intra-class and similarity of inter-class. The results show that the proposed model can perform defect segmentation in all kinds of defects in database, however the efficiency was compromised by illumination changes. Notable that, this segmentation is based on unsupervised learning with small training dataset and no labeling procedure, so it can be easily extended to the real world application. Eventually, this defect detection shall improve the productivity and reliability of steel strip's production process.
{"title":"Defect Segmentation of Hot-rolled Steel Strip Surface by using Convolutional Auto-Encoder and Conventional Image processing","authors":"Sanyapong Youkachen, M. Ruchanurucks, Teera Phatrapomnant, H. Kaneko","doi":"10.1109/ICTEMSYS.2019.8695928","DOIUrl":"https://doi.org/10.1109/ICTEMSYS.2019.8695928","url":null,"abstract":"Defects on steel strip surface can long-term cause undesirable effects, since they make physical and/or chemical properties mismatched from steel's specification. Nowadays, automatic visual-based surface inspection is adopted, in order to detect the defects on steel strip surface after being produced. Moreover, since these defects appear in wide variety of forms and various classes, machine learning methods are generally involved to visual surface inspection for coping with these appearances. In this paper, we present a novel defect detection model to perform defect segmentation of hot-rolled steel strip surface, by using Convolutional Auto-Encoder (CAE) and sharpening process to extract the defect features of input image, then applied postprocessing for visualization. In the experiments, the NEU database, which provides six kinds of typical surface defects of hot-rolled steel strip, was applied to evaluate the efficiency of the proposed model. This database also provides difficulty challenges regarding diversity of intra-class and similarity of inter-class. The results show that the proposed model can perform defect segmentation in all kinds of defects in database, however the efficiency was compromised by illumination changes. Notable that, this segmentation is based on unsupervised learning with small training dataset and no labeling procedure, so it can be easily extended to the real world application. Eventually, this defect detection shall improve the productivity and reliability of steel strip's production process.","PeriodicalId":220516,"journal":{"name":"2019 10th International Conference of Information and Communication Technology for Embedded Systems (IC-ICTES)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126018631","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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