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Journal articles on the topic 'Label inspection'
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1
Mirmehdi, Majid. "Product label inspection using transputers." Concurrency: Practice and Experience 3, no. 4 (August 1991): 265–73. http://dx.doi.org/10.1002/cpe.4330030404.
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Mirmehdi, M., GAW West, and GR Dowling. "Label inspection using the Hough transform on transputer networks." Microprocessors and Microsystems 15, no. 3 (April 1991): 167–73. http://dx.doi.org/10.1016/0141-9331(91)90137-5.
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Xiao, Xing, Xinhai Li, Heng Zhou, Jingming Liang, and Man Xia. "Substation Indoor Wheeled Robot Inspection Visual Blind Pressure Plate Recognition." E3S Web of Conferences 299 (2021): 01004. http://dx.doi.org/10.1051/e3sconf/202129901004.
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In order to solve the problem of wheeled robots in the main control room of the substation due to the height of the robot and the shooting distance, the main camera cannot take a complete picture of the bottom pressure plate of the relay protection pressure plate cabinet during the inspection process and perform open/close state and label image recognition. Propose install a wide-angle secondary camera on the side of wheeled robot, and targeted proposal for the correction of distorted images taken with a wide-angle lens using a division model, improving the accuracy of decompression plate checks. Experiments have shown that, the proposed approach is practical and feasible, effective solution for indoor wheeled robot relay panel cabinet inspection visual blind spot shooting problem, enhanced the effectiveness of wheeled robotic inspections in the main control room of the substation, improved platen verification accuracy.
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Yao, Shiqing, and Kaijie Zhu. "Combating product label misconduct: The role of traceability and market inspection." European Journal of Operational Research 282, no. 2 (April 2020): 559–68. http://dx.doi.org/10.1016/j.ejor.2019.09.031.
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Shiau, Yau-Ren, Fang-Yi Lo, and Po-Cheng Ko. "Early Intervention Mechanism for Preventing Electrical Shocks During Construction Projects: Portable Electrical Equipment." E3S Web of Conferences 186 (2020): 03004. http://dx.doi.org/10.1051/e3sconf/202018603004.
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To prevent electrical shock accidents in construction sites, the present researchers used portable electrical equipment as an example to plan a preconstruction early intervention mechanism that can be used to conduct various inspections of portable electrical devices. This study used narrative text analysis for data collection and compilation. The researchers analyzed 41 real electrocution death cases involving portable electric equipment as the electrocution medium in the Taiwanese construction industry and identified hazard factors that cause electrocution from the case summaries. Then, the IDEF3 was used to integrate and construct a model for the portable electrical equipment inspection flowchart of the early intervention mechanism as a safety inspection system to prevent electrocution in construction engineering units. This study revealed hazard factors and management omissions related to electrocutions caused by portable electrical equipment. To protect workers and strengthen the safety of the construction site, this study proposed of an electric shock prevention early intervention mechanism for portable electric equipment in construction projects. Various inspections should be conducted before equipment is brought on site for construction operations to ensure the safety of electrical equipment and reduce electrocution risks. This study also established a visualization mechanism for the visual qualification label of portable electrical equipment. This mechanism is conducive to strengthening safety management.
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Luo, Chao, Le Song, Mei Rong Zhao, Yu Chi Lin, and Jian Li. "Research on Real-Time Vision Detection Method for Sanitary Labels." Applied Mechanics and Materials 300-301 (February 2013): 484–89. http://dx.doi.org/10.4028/www.scientific.net/amm.300-301.484.
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Taking diaper which is a representative production of sanitary supplies as an example, a real-time detection method for diaper label based on machine vision is developed. To identify the location of diaper surface label position rapidly, a visual inspection system platform applies to production line is built. Images are captured with high-resolution colorful CCD industrial camera and NC template matching method is adopted as the surface label detection algorithm. Meanwhile, the comparative experiments results among NC, ABS method and Moment Matching method are presented. Experimental results show that this label detection system can realize accurate identification on the condition of different light, whose recognition rate can reach up to 97% and detection algorithm is of preferable instantaneity and stability.
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Balzategui, Julen, Luka Eciolaza, and Daniel Maestro-Watson. "Anomaly Detection and Automatic Labeling for Solar Cell Quality Inspection Based on Generative Adversarial Network." Sensors 21, no. 13 (June 25, 2021): 4361. http://dx.doi.org/10.3390/s21134361.
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Quality inspection applications in industry are required to move towards a zero-defect manufacturing scenario, with non-destructive inspection and traceability of 100% of produced parts. Developing robust fault detection and classification models from the start-up of the lines is challenging due to the difficulty in getting enough representative samples of the faulty patterns and the need to manually label them. This work presents a methodology to develop a robust inspection system, targeting these peculiarities, in the context of solar cell manufacturing. The methodology is divided into two phases: In the first phase, an anomaly detection model based on a Generative Adversarial Network (GAN) is employed. This model enables the detection and localization of anomalous patterns within the solar cells from the beginning, using only non-defective samples for training and without any manual labeling involved. In a second stage, as defective samples arise, the detected anomalies will be used as automatically generated annotations for the supervised training of a Fully Convolutional Network that is capable of detecting multiple types of faults. The experimental results using 1873 Electroluminescence (EL) images of monocrystalline cells show that (a) the anomaly detection scheme can be used to start detecting features with very little available data, (b) the anomaly detection may serve as automatic labeling in order to train a supervised model, and (c) segmentation and classification results of supervised models trained with automatic labels are comparable to the ones obtained from the models trained with manual labels.
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Wang, Ting, Chen Yao, Jun Liu, Long Zhang Chao, Gang Hu Shao, Hong Gu Qun, and Yue Ja Ming. "Research on Automatic Identification of Substation Circuit Breaker Based on Shape Priors." Applied Mechanics and Materials 511-512 (February 2014): 923–26. http://dx.doi.org/10.4028/www.scientific.net/amm.511-512.923.
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High-pressure circuit breakers are very important and it undertakes the disconnection and connection control of high voltage transmission lines. It is one of the equipment of substation daily inspection. State of breakers are judged by open and close characters label, so a shape-prior active contour model to realize state automatic recognition of breaker images collected by inspection robot is presented in this paper. Shape-prior active contour model combines the shape information with CV model to build energy functional model, then set up initial position curve by a priori knowledge and drives the curve evolution in minimize energy functional process, the curve position is the character label contour when energy functional shows minimum. We do experiment for the algorithm on different images, demonstrate that the algorithm based on known character contour, have good segmentation results of circuit breaker in the image character recognition accuracy and applicability when the circuit breaker character is actually partial occlusion, local deformation, scale changes.
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Dupré, Ruth. "Regulating the Quebec Dairy Industry, 1905–1921: Peeling Off the Joseph Label." Journal of Economic History 50, no. 2 (June 1990): 339–48. http://dx.doi.org/10.1017/s0022050700036470.
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From its beginning the Quebec dairy industry was characterized by many small factories producing butter and cheese of such a low quality that the British called all bad cheese coming from Canada “Joseph.” The Société d'industrie laitière, created in 1882 to promote the industry, asked the government to impose compulsory inspection, licensing, and grading. Between 1905 and 1921 the government finally but reluctantly responded.
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Cai, Nian, Yuchao Chen, Gen Liu, Guandong Cen, Han Wang, and Xindu Chen. "A vision-based character inspection system for tire mold." Assembly Automation 37, no. 2 (April 3, 2017): 230–37. http://dx.doi.org/10.1108/aa-07-2016-066.
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Purpose This paper aims to design an automatic inspection system for the characters on tire molds, which involves a vision-based inspection method for the characters on tire molds. Design/methodology/approach An automatic inspection equipment is designed according to the features of the tire mold. To implement the inspection task, the corresponding image processing methods are designed, including image preprocessing, image mosaic, image locating and character inspection. Image preprocessing mainly contains fitting the contours of the acquired tire mold images and those of the computer aided design (CAD) as the arcs of two circles and polar transformation of the acquired images and the CAD. Then, the authors propose a novel framework to locate the acquired images into the corresponding mosaicked tire mold image. Finally, a character inspection scheme is proposed by combining an support-vector-machine-based character recognition method and a string matching approach. At the stages of image locating and character inspection, image mosaic is simultaneously used to label the defects in the mosaicked tire mold image, which is based on histograms-of-gradients features. Findings The experimental results indicate that the designed automatic inspection system can inspect the characters on the tire mold with a high accuracy at a reasonable time consumption. Practical implications The designed automatic inspection system can detect the carving faults for the characters on the tire molds, which are the cases that the characters are wrongly added, deleted or modified on the tire mold. Originality/value To the best of the authors’ knowledge, this is the first automatic vision-based inspection system for the characters on tire molds. An inspection equipment is designed and many novel image processing methods are proposed to implement the inspection task. The designed system can be widely applied in the industry.
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Jia, Min, Shenmiao Li, Liguo Zang, Xiaonan Lu, and Hongyan Zhang. "Analysis of Biomolecules Based on the Surface Enhanced Raman Spectroscopy." Nanomaterials 8, no. 9 (September 15, 2018): 730. http://dx.doi.org/10.3390/nano8090730.
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Analyzing biomolecules is essential for disease diagnostics, food safety inspection, environmental monitoring and pharmaceutical development. Surface-enhanced Raman spectroscopy (SERS) is a powerful tool for detecting biomolecules due to its high sensitivity, rapidness and specificity in identifying molecular structures. This review focuses on the SERS analysis of biomolecules originated from humans, animals, plants and microorganisms, combined with nanomaterials as SERS substrates and nanotags. Recent advances in SERS detection of target molecules were summarized with different detection strategies including label-free and label-mediated types. This comprehensive and critical summary of SERS analysis of biomolecules might help researchers from different scientific backgrounds spark new ideas and proposals.
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Kim, Jung Jin, Ah-Ram Kim, and Seong-Won Lee. "Artificial Neural Network-Based Automated Crack Detection and Analysis for the Inspection of Concrete Structures." Applied Sciences 10, no. 22 (November 16, 2020): 8105. http://dx.doi.org/10.3390/app10228105.
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The damage investigation and inspection methods for infrastructures performed in small-scale (type III) facilities usually involve a visual examination by an inspector using surveying tools (e.g., cracking, crack microscope, etc.) in the field. These methods can interfere with the subjectivity of the inspector, which may reduce the objectivity and reliability of the record. Therefore, a new image analysis technique is needed to automatically detect cracks and analyze the characteristics of the cracks objectively. In this study, an image analysis technique using deep learning is developed to detect cracks and analyze characteristics (e.g., length, and width) in images for small-scale facilities. Three stages of image processing pipeline are proposed to obtain crack detection and its characteristics. In the first and second stages, two-dimensional convolutional neural networks are used for crack image detection (e.g., classification and segmentation). Based on convolution neural network for the detection, hierarchical feature learning architecture is applied into our deep learning network. After deep learning-based detection, in the third stage, thinning and tracking algorithms are applied to analyze length and width of crack in the image. The performance of the proposed method was tested using various crack images with label and the results showed good performance of crack detection and its measurement.
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Adibhatla, Venkat Anil, Huan-Chuang Chih, Chi-Chang Hsu, Joseph Cheng, Maysam F. Abbod, and Jiann-Shing Shieh. "Defect Detection in Printed Circuit Boards Using You-Only-Look-Once Convolutional Neural Networks." Electronics 9, no. 9 (September 22, 2020): 1547. http://dx.doi.org/10.3390/electronics9091547.
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In this study, a deep learning algorithm based on the you-only-look-once (YOLO) approach is proposed for the quality inspection of printed circuit boards (PCBs). The high accuracy and efficiency of deep learning algorithms has resulted in their increased adoption in every field. Similarly, accurate detection of defects in PCBs by using deep learning algorithms, such as convolutional neural networks (CNNs), has garnered considerable attention. In the proposed method, highly skilled quality inspection engineers first use an interface to record and label defective PCBs. The data are then used to train a YOLO/CNN model to detect defects in PCBs. In this study, 11,000 images and a network of 24 convolutional layers and 2 fully connected layers were used. The proposed model achieved a defect detection accuracy of 98.79% in PCBs with a batch size of 32.
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Xiao, Yun Hua. "The Design and Application of the Intelligent Vehicle Inspection System Based on RFID." Applied Mechanics and Materials 687-691 (November 2014): 823–26. http://dx.doi.org/10.4028/www.scientific.net/amm.687-691.823.
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Our country has many serious problems such as fake-licensed vehicle, stolen vehicle, overloaded vehicle, over speed vehicle and so on at present. This paper designs and realizes a kind of intelligent vehicle inspection system based on RFID technology in order to solve these problems. This system obtain the all kinds of vehicle information by recognizing electronic label card and deal with all kinds of information in the background in order to manage vehicle. The system has changed the traditional manual vehicle management mode and realized automated inspection without parking. It can reduce the work intensity of law-enforcement personal and take the initiative to call the police when meet all kinds of illegal vehicle, stolen vehicle etc. It can eliminate the crime motive and behavior. It provides the great help for social and economic development.
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Egorova, V. V., and I. L. Kazantseva. "The Canned Vegetables Research in the Forensic Commodity Examination." Theory and Practice of Forensic Science 14, no. 4 (January 8, 2020): 117–24. http://dx.doi.org/10.30764//1819-2785-2019-14-4-117-124.
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The course of a forensic analysis of long-term storage food products (canned vegetables) has been reviewed. The significance of the stage of external inspection, examination of the label is shown. The expertise objects are finished products of the canning industry in factory package (vegetable marinades and first courses). The objects were examined for compliance with the appropriate GOST requirements and technical specifications for organoleptic characteristics, net weight, mass fraction of ingredients.As a result of determining the organoleptic properties of canned vegetables “Pickled beetroot” the presence of individual pieces with black firm beet tissue has been detected which indicates violation of technological patterns of production. The presence of a particle of an outside impurity has also been detected that is a particle of paint coating based on alkyd binder and containing calcium carbonate as a filler which is unacceptable. The deviation of the canned vegetables net contents from the nominal amount indicated on the label meets the requirements of the regulations. For individual cans from the sample provided for analysis the experts have found non-compliance with the technical specifications for the rate “Mass fraction of vegetables from the total mass of canned goods”.When examining the canned good “First courses. Borsch with fresh cabbage” it has been identified that the samples have various labels on the consumer containers which indicates the presence of samples from different shipments.
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Hemalatha, S., and S. Margret Anouncia. "Unsupervised Segmentation of Remote Sensing Images using FD Based Texture Analysis Model and ISODATA." International Journal of Ambient Computing and Intelligence 8, no. 3 (July 2017): 58–75. http://dx.doi.org/10.4018/ijaci.2017070104.
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In this paper, an unsupervised segmentation methodology is proposed for remotely sensed images by using Fractional Differential (FD) based texture analysis model and Iterative Self-Organizing Data Analysis Technique Algorithm (ISODATA). Essentially, image segmentation is used to assign unique class labels to different regions of an image. In this work, it is transformed into texture segmentation by signifying each class label as a unique texture class. The FD based texture analysis model is suggested for texture feature extraction from images and ISODATA is used for segmentation. The proposed methodology was first implemented on artificial target images and then on remote sensing images from Google Earth. The results of the proposed methodology are compared with those of the other texture analysis methods such as LBP (Local Binary Pattern) and NBP (Neighbors based Binary Pattern) by visual inspection as well as using classification measures derived from confusion matrix. It is justified that the proposed methodology outperforms LBP and NBP methods.
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TROKHYMCHUK, ANATOLIY, CHERYL WALDNER, SHERYL GOW, BONNIE CHABAN, and JANET E. HILL. "Comparison of Baseline Bacterial Levels in Retail Ground Beef Originating from Different Regulatory, Processing, and Packaging Environments." Journal of Food Protection 77, no. 3 (March 1, 2014): 404–11. http://dx.doi.org/10.4315/0362-028x.jfp-13-331.
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The objectives of this study were to collect baseline measures of bacteria present in retail ground beef offered for sale in Saskatchewan and to assess differences associated with the licensing or regulatory environment of the packaging and processing facilities as indicated by package labeling. Packages of ground beef (n = 309) were purchased from May 2011 to May 2012. Retail samples were categorized as originating from facilities regulated by the federal government or licensed by the provincial government (n = 126), originating from facilities licensed by local health regions (n = 80), or having no inspection or source information on the package label (n = 103). Total aerobic plate counts and total Escherichia coli plate counts were determined using 3M Petrifilm methods. Total bacterial load was estimated using real-time quantitative PCR. The data were analyzed on a log scale using multivariable linear regression, accounting for season and whether the samples were fresh or frozen at purchase. Total aerobic plate counts and Escherichia coli plate counts were lower in samples from federally regulated or provincially licensed facilities than in samples from locally licensed facilities (P < 0.001 and P = 0.002, respectively) or in samples with no inspection information on the label (P < 0.001 and P = 0.011, respectively). Frozen ground beef from federally regulated or provincially licensed facilities had the lowest total bacterial load. Samples clearly labeled as packaged at federally regulated or provincially licensed facilities consistently had the lowest estimated bacterial levels.
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Wulansari, Hendrian, and Anna Maria Tri Anggraini. "PERLINDUNGAN KONSUMEN TERHADAP KETIADAAN LABEL HALAL PADA PRODUK FARMASI MENURUT UNDANG-UNDANG NOMOR 33 TAHUN 2014 TENTANG JAMINAN PRODUK HALAL." Jurnal Hukum Adigama 1, no. 1 (July 23, 2018): 1062. http://dx.doi.org/10.24912/adigama.v1i1.2186.
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This study discusses consumer protection against the absence of halal label on pharmaceutical products according to Law No. 33 of 2014 on Halal Product Guarantee. Based on the existing findings that the presence of medicinal products are still many who use materials that are not kosher. Therefore, an effort is needed to provide consumer rights protection to the certification and labeling of halal products. The problem why in the pharmaceutical products are not included halal label? How about consumer protection against pharmaceutical products that do not include halal label? Ingi research method using normative legal research methods. The result data showed that the absence of halal label on pharmaceutical products due to the factors that influence it is because of the difficulty of finding replacement materials for medicines because 90 percent of the drugs are imported from other countries where there is no guarantee of halal. In the near future the producers of drugs are also experiencing constraints because they have to conduct research on raw materials because of the difficulty of the origin of the material also has its own complexity so it takes a long time. On the other hand, the equipment used for the production process, production site, processing, storage, packaging and sales and presentation should be made separately between halal and non-halal certified products / products. Consumer protection against pharmaceutical products that do not include halal labels should have 3 (three) supervisory systems that are preventive by looking at enrollment activities, with special supervision of food cases, medicines and kosher cosmetics that can result in widespread impact and incidental surveillance systems namely the process of supervision by law enforcers on halal food safety and safety done by means of sudden inspection (sidak). There needs to be a systematic joint effort of the government and relevant stakeholders to encourage the maker of halal drug products and raise awareness of the Muslim community about the importance of halal products. It aims to protect Muslims from consuming unlawful products.
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Ronchieri, Elisabetta, Marco Canaparo, Mauro Belgiovine, Davide Salomoni, and Barbara Martelli. "Lessons Learned from the Assessment of Software Defect Prediction on WLCG Software: A Study with Unlabelled Datasets and Machine Learning Techniques." EPJ Web of Conferences 245 (2020): 05041. http://dx.doi.org/10.1051/epjconf/202024505041.
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Software defect prediction is an activity that aims at narrowing down the most likely defect-prone software modules and helping developers and testers to prioritize inspection and testing. This activity can be addressed by using Machine Learning techniques applied to software metrics datasets that are usually unlabelled, i.e. they lack modules classification in terms of defectiveness. To overcome this limitation, in addition to the usual data pre-processing operations to manage mission values and/or to remove inconsistencies, researches have to adopt an approach to label their unlabelled software datasets. The extraction of defectiveness data to label all the instances of the datasets is an extremely time and effort consuming operation. In literature, many studies have introduced approaches to build a defect prediction models on unlabelled datasets. In this paper, we describe the analysis of new unlabelled datasets from WLCG software, coming from HEP-related experiments and middleware, by using Machine Learning techniques. We have experimented new approaches to label the various modules due to the heterogeneity of software metrics distribution. We discuss a number of lessons learned from conducting these activities, what has worked, what has not and how our research can be improved.
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Connolly, Christine. "Imaging developments benefit medical applications." Sensor Review 25, no. 4 (December 1, 2005): 246–48. http://dx.doi.org/10.1108/02602280510620079.
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PurposeReviews some of the improvements in image sensor technology that are yielding applications in the medical field.Design/methodology/approachDiscusses the characteristics and gives examples of cameras and imaging sensors used in endoscopy, microscopy, pharmaceutical label inspection and X‐radiography. Reviews some innovative camera‐based products for endoscopy, skin imaging and health monitoring.FindingsImprovements in camera resolution, miniaturisation and interfacing are widening the applications in medical imaging and enabling the development of some exciting new products addressing the needs of patients and medical staff.Originality/valueIdentifies some suppliers of medical imaging devices and their applications.
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Saur, Günter, and Wolfgang Krüger. "CHANGE DETECTION IN UAV VIDEO MOSAICS COMBINING A FEATURE BASED APPROACH AND EXTENDED IMAGE DIFFERENCING." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B7 (June 21, 2016): 557–62. http://dx.doi.org/10.5194/isprsarchives-xli-b7-557-2016.
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Change detection is an important task when using unmanned aerial vehicles (UAV) for video surveillance. We address changes of short time scale using observations in time distances of a few hours. Each observation (previous and current) is a short video sequence acquired by UAV in near-Nadir view. Relevant changes are, e.g., recently parked or moved vehicles. Examples for non-relevant changes are parallaxes caused by 3D structures of the scene, shadow and illumination changes, and compression or transmission artifacts. In this paper we present (1) a new feature based approach to change detection, (2) a combination with extended image differencing (Saur et al., 2014), and (3) the application to video sequences using temporal filtering. In the feature based approach, information about local image features, e.g., corners, is extracted in both images. The label “new object” is generated at image points, where features occur in the current image and no or weaker features are present in the previous image. The label “vanished object” corresponds to missing or weaker features in the current image and present features in the previous image. This leads to two “directed” change masks and differs from image differencing where only one “undirected” change mask is extracted which combines both label types to the single label “changed object”. The combination of both algorithms is performed by merging the change masks of both approaches. A color mask showing the different contributions is used for visual inspection by a human image interpreter.
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Saur, Günter, and Wolfgang Krüger. "CHANGE DETECTION IN UAV VIDEO MOSAICS COMBINING A FEATURE BASED APPROACH AND EXTENDED IMAGE DIFFERENCING." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B7 (June 21, 2016): 557–62. http://dx.doi.org/10.5194/isprs-archives-xli-b7-557-2016.
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Change detection is an important task when using unmanned aerial vehicles (UAV) for video surveillance. We address changes of short time scale using observations in time distances of a few hours. Each observation (previous and current) is a short video sequence acquired by UAV in near-Nadir view. Relevant changes are, e.g., recently parked or moved vehicles. Examples for non-relevant changes are parallaxes caused by 3D structures of the scene, shadow and illumination changes, and compression or transmission artifacts. In this paper we present (1) a new feature based approach to change detection, (2) a combination with extended image differencing (Saur et al., 2014), and (3) the application to video sequences using temporal filtering. In the feature based approach, information about local image features, e.g., corners, is extracted in both images. The label “new object” is generated at image points, where features occur in the current image and no or weaker features are present in the previous image. The label “vanished object” corresponds to missing or weaker features in the current image and present features in the previous image. This leads to two “directed” change masks and differs from image differencing where only one “undirected” change mask is extracted which combines both label types to the single label “changed object”. The combination of both algorithms is performed by merging the change masks of both approaches. A color mask showing the different contributions is used for visual inspection by a human image interpreter.
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Xu, Xiaohang, Hong Zheng, Zhongyuan Guo, Xiongbin Wu, and Zhaohui Zheng. "SDD-CNN: Small Data-Driven Convolution Neural Networks for Subtle Roller Defect Inspection." Applied Sciences 9, no. 7 (March 31, 2019): 1364. http://dx.doi.org/10.3390/app9071364.
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Roller bearings are some of the most critical and widely used components in rotating machinery. Appearance defect inspection plays a key role in bearing quality control. However, in real industries, bearing defects are usually extremely subtle and have a low probability of occurrence. This leads to distribution discrepancies between the number of positive and negative samples, which makes intelligent data-driven inspection methods difficult to develop and deploy. This paper presents a small data-driven convolution neural network (SDD-CNN) for roller subtle defect inspection via an ensemble method for small data preprocessing. First, label dilation (LD) is applied to solve the problem of an imbalance in class distribution. Second, a semi-supervised data augmentation (SSDA) method is proposed to extend the dataset in a more efficient and controlled way. In this method, a coarse CNN model is trained to generate ground truth class activation and guide the random cropping of images. Third, four variants of the CNN model, namely, SqueezeNet v1.1, Inception v3, VGG-16, and ResNet-18, are introduced and employed to inspect and classify the surface defects of rollers. Finally, a rich set of experiments and assessments is conducted, indicating that these SDD-CNN models, particularly the SDD-Inception v3 model, perform exceedingly well in the roller defect classification task with a top-1 accuracy reaching 99.56%. In addition, the convergence time and classification accuracy for an SDD-CNN model achieve significant improvement compared to that for the original CNN. Overall, using an SDD-CNN architecture, this paper provides a clear path toward a higher precision and efficiency for roller defect inspection in smart manufacturing.
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Prappacher, Nico, Markus Bullmann, Gunther Bohn, Frank Deinzer, and Andreas Linke. "Defect Detection on Rolling Element Surface Scans Using Neural Image Segmentation." Applied Sciences 10, no. 9 (May 9, 2020): 3290. http://dx.doi.org/10.3390/app10093290.
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The surface inspection of steel parts like rolling elements for roller bearings is an essential component of the quality assurance process in their production. Existing inspection systems require high maintenance cost and allow little flexibility. In this paper, we propose the use of a rapidly retrainable convolutional neural network. Our approach reduces the development and maintenance cost compared to a manually programmed classification system for steel surface defect detection. One of the main disadvantages of neural network approaches is their high demand for labeled training data. To bypass this, we propose the use of simulated defects. In the production of rolling elements, real defects are a rarity. Collecting a balanced dataset thus costs a lot of time and resources. Simulating defects reduces the time required for data collection. It also allows us to automatically label the dataset. This further eases the data collection process compared to existing approaches. Combined, this allows us to train our system faster and cheaper than existing systems. We will show that our system can be retrained in a matter of minutes, minimizing production downtime, while still allowing high accuracy in defect detection.
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Xu, Yingda, and Jianming Wei. "Deep Feature Fusion Based Dual Branch Network for X-ray Security Inspection Image Classification." Applied Sciences 11, no. 16 (August 14, 2021): 7485. http://dx.doi.org/10.3390/app11167485.
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Automatic computer security inspection of X-ray scanned images has an irresistible trend in modern life. Aiming to address the inconvenience of recognizing small-sized prohibited item objects, and the potential class imbalance within multi-label object classification of X-ray scanned images, this paper proposes a deep feature fusion model-based dual branch network architecture. Firstly, deep feature fusion is a method to fuse features extracted from several model layers. Specifically, it operates these features by upsampling and dimension reduction to match identical sizes, then fuses them by element-wise sum. In addition, this paper introduces focal loss to handle class imbalance. For balancing importance on samples of minority and majority class, it assigns weights to class predictions. Additionally, for distinguishing difficult samples from easy samples, it introduces modulating factor. Dual branch network adopts the two components above and integrates them in final loss calculation through the weighted sum. Experimental results illustrate that the proposed method outperforms baseline and state-of-art by a large margin on various positive/negative ratios of datasets. These demonstrate the competitivity of the proposed method in classification performance and its potential application under actual circumstances.
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Ko, Taehoon, Je Hyuk Lee, Hyunchang Cho, Sungzoon Cho, Wounjoo Lee, and Miji Lee. "Machine learning-based anomaly detection via integration of manufacturing, inspection and after-sales service data." Industrial Management & Data Systems 117, no. 5 (June 12, 2017): 927–45. http://dx.doi.org/10.1108/imds-06-2016-0195.
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Purpose Quality management of products is an important part of manufacturing process. One way to manage and assure product quality is to use machine learning algorithms based on relationship among various process steps. The purpose of this paper is to integrate manufacturing, inspection and after-sales service data to make full use of machine learning algorithms for estimating the products’ quality in a supervised fashion. Proposed frameworks and methods are applied to actual data associated with heavy machinery engines. Design/methodology/approach By following Lenzerini’s formula, manufacturing, inspection and after-sales service data from various sources are integrated. The after-sales service data are used to label each engine as normal or abnormal. In this study, one-class classification algorithms are used due to class imbalance problem. To address multi-dimensionality of time series data, the symbolic aggregate approximation algorithm is used for data segmentation. Then, binary genetic algorithm-based wrapper approach is applied to segmented data to find the optimal feature subset. Findings By employing machine learning-based anomaly detection models, an anomaly score for each engine is calculated. Experimental results show that the proposed method can detect defective engines with a high probability before they are shipped. Originality/value Through data integration, the actual customer-perceived quality from after-sales service is linked to data from manufacturing and inspection process. In terms of business application, data integration and machine learning-based anomaly detection can help manufacturers establish quality management policies that reflect the actual customer-perceived quality by predicting defective engines.
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Starobin, Shana, and Erika Weinthal. "The Search for Credible Information in Social and Environmental Global Governance: The Kosher Label." Business and Politics 12, no. 3 (October 2010): 1–35. http://dx.doi.org/10.2202/1469-3569.1322.
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Hundreds of “eco-labels” and “social labels” exist for consumer products. These labels claim to provide information about characteristics of these products, which consumers cannot directly observe but which many of them consider desirable, such as low environmental impact, good treatment of workers during production, and relatively high prices paid to the local producers of ingredients from developing countries. Third-party certifiers are supposed to solve the well-known problem that a producer's unilateral declarations lack credibility, given the producer's conflict of interest and the information asymmetries between producer and consumer. Much of the literature on global private regulation—through standards for environmental sustainability, corporate social responsibility, among others—assumes that third-party certification works (i.e., overcomes the problems of producer self-declaration). But closer inspection shows that many third-party certifiers lack credibility. This article examines why some third party certifiers are more credible than others. In doing so, we elucidate the ways in which social capital and trust bolster third party certifiers' credibility. The empirical analysis focuses primarily on Kosher food labels within the global food supply chain. We then explore the consequences of the credibility paradox for other third party certified labels that promote social and environmental values.
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Malone, Andrew S. "Cautious about Conatives." Novum Testamentum 62, no. 3 (June 18, 2020): 302–21. http://dx.doi.org/10.1163/15685365-12341668.
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Abstract It has long been recognized that the imperfect and present tenses can communicate a conative sense. The category is sufficiently established that New Testament commentaries can brusquely identify “a conative imperfect” or “imperfectum de conatu” as if (1) the terminology conveys a uniform meaning and (2) such meaning is established by the verb’s tense. A fresh inspection of the phenomenon suggests neither assumption is accurate. With worked examples we can observe that at least two competing nuances are understood by the label “conative” and that the verb’s tense is far from the determinative factor. Whether in generating claims about the conative sense or in digesting others’ analyses, interpreters need to be alert to the pitfalls associated with this category.
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Muhammad, Muhammad, Guohua Yao, Jie Zhong, Kuanglin Chao, Muhammad Hammad Aziz, and Qing Huang. "A facile and label-free SERS approach for inspection of fipronil in chicken eggs using SiO2@Au core/shell nanoparticles." Talanta 207 (January 2020): 120324. http://dx.doi.org/10.1016/j.talanta.2019.120324.
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Adam, Benjamin, and Stefan Tenbohlen. "Classification of Superimposed Partial Discharge Patterns." Energies 14, no. 8 (April 12, 2021): 2144. http://dx.doi.org/10.3390/en14082144.
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Phase resolved partial discharge patterns (PRPD) are routinely used to assess the condition of power transformers. In the past, classification systems have been developed in order to automate the fault identification task. Most of those systems work with the assumption that only one source is active. In reality, however, multiple PD sources can be active at the same time. Hence, PRPD patterns can overlap and cannot be separated easily, e.g., by visual inspection. Multiple PD sources in a single PRPD represent a multi-label classification problem. We present a system based on long short-term memory (LSTM) neural networks to resolve this task. The system is generally able to classify multiple overlapping PRPD by while only being trained by single class PD sources. The system achieves a single class accuracy of 99% and a mean multi-label accuracy of 43% for an imbalanced dataset. This method can be used with overlapping PRPD patterns to identify the main PD source and, depending on the data, also classify the second source. The method works with conventional electrical measuring devices. Within a detailed discussion of the presented approach, both its benefits but also its problems regarding different repetition rates of different PD sources are being evaluated.
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BLAIS, BURTON W., MARTINE GAUTHIER, MYLÈNE DESCHÊNES, and GEORGE HUSZCZYNSKI. "Polyester Cloth–Based Hybridization Array System for Identification of Enterohemorrhagic Escherichia coli Serogroups O26, O45, O103, O111, O121, O145, and O157." Journal of Food Protection 75, no. 9 (September 1, 2012): 1691–97. http://dx.doi.org/10.4315/0362-028x.jfp-12-116.
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A cloth-based hybridization array system (CHAS) was developed for the identification of foodborne colony isolates of seven priority enterohemorrhagic Escherichia coli (EHEC-7) serogroups targeted by U.S. food inspection programs. Gene sequences associated with intimin; Shiga-like toxins 1 and 2; and the antigenic markers O26, O45, O103, O111, O121, O145, and O157 were amplified in a multiplex PCR incorporating a digoxigenin label, and detected by hybridization of the PCR products with an array of specific oligonucleotide probes immobilized on a polyester cloth support, with subsequent immunoenzymatic assay of the captured amplicons. The EHEC-7 CHAS exhibited 100% inclusivity and 100% exclusivity characteristics with respect to detection of the various markers among 89 different E. coli strains, with various marker gene profiles and 15 different strains of non–E. coli bacteria.
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Drumheller, Bradley, Mohamed Amgad, Ahmed Aljudi, Elliott Burdette, Leila Kutob, Cameron Neely, Adam Perricone, Conrad Shebelut, and David Jaye. "Early Development of a Machine Learning Approach to Quantify MYC Immunohistochemical Staining in Lymphoma." American Journal of Clinical Pathology 154, Supplement_1 (October 2020): S19. http://dx.doi.org/10.1093/ajcp/aqaa137.034.
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Abstract Newer data suggest that double expression of MYC and BCL2 proteins (DE) evaluated by quantitative immunohistochemistry (qIHC) may be a powerful marker of worse prognosis in diffuse large B cell lymphoma (DLBCL). Testing for DE status, defined as >40% MYC+ and >50% BCL2+ tumor cells, is recommended in the WHO 2016 classification and clinical trials are using DE scoring to assign therapy arms. However, other data suggest that significant variability in manual DE scoring diminishes the predictive value. Error sources include high interobserver variability (IOV) associated with field choice, discrimination of tumor immunoreactivity from adjacent non-neoplastic cells, cell-to-cell variability in staining intensity, crush artifacts and necrosis. Thus, there is a need for standardized, reproducible approaches for DE scoring by qIHC. To address this need, we have begun developing a novel machine-learning approach to analyze IHC digital pathology whole-slide images, focusing initially on MYC IHC. Digital whole-slide images (400x) of 22 DLBCL cases were uploaded to a web-based annotation platform. Using all cases, one annotator created 138 regions of interest (ROIs) containing approximately 200 nucleated cells and representing a variety of tissue types. Eight pathologists were assigned the same 10 ROIs in which to annotate all nuclei from which ground-truth seed nucleus labels (location, classification) were created for a validation set. Nuclei were classified as “tumor-positive”, “tumor-negative”, “non-tumor-positive”, “non-tumor-negative”, or “unknown”. This generated a set of 15,792 annotations with 1974 +/- 272 (Avg+/-STD) labels/annotator. Agglomerative hierarchical clustering afforded the creation of 2299 ground-truth seed locations. A maximum diameter of 3 mm/cluster was set by visual inspection of annotations. Of these seed locations, 1041 (45%) were detected by 8/8 annotators and, on average, 6/8 agreed on class. 302 +/- 72 (Avg+/-STD) “tumor positive” labels per annotator generated 382 seeds locations, 178 (47%) of which were detected by 8/8 annotators, with an average of 7.5/8 agreeing on class. 286 +/- 168 (Avg+/-STD) “tumor-negative” labels per annotator generated 336 seeds, 195 (58%) of which were detected by 8/8 annotators, with an average of 5/8 agreeing on class. Among all classes, the “tumor-positive” label displayed best overall label agreement whereas the “tumor-negative“ label yielded similar localization rate, but lower class agreement. These promising early findings provide a novel basis for quantifying IOV and utilizing multi-observer agreement to create a ground-truth validation set for a supervised machine learning approach to qIHC. Future efforts will make use of these data to optimize the validation set by rationally determining the number of additional annotations required, optimizing the number of annotators per ROI required, devising an adaptive approach to nuclear clustering based on nuclear density, and utilizing the additional 31,422 annotations in hand from all annotators as a robust algorithm training set.
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Hawker, Charles D., William McCarthy, David Cleveland, and Bonnie L. Messinger. "Invention and Validation of an Automated Camera System That Uses Optical Character Recognition to Identify Patient Name Mislabeled Samples." Clinical Chemistry 60, no. 3 (March 1, 2014): 463–70. http://dx.doi.org/10.1373/clinchem.2013.215434.
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Abstract BACKGROUND Mislabeled samples are a serious problem in most clinical laboratories. Published error rates range from 0.39/1000 to as high as 1.12%. Standardization of bar codes and label formats has not yet achieved the needed improvement. The mislabel rate in our laboratory, although low compared with published rates, prompted us to seek a solution to achieve zero errors. METHODS To reduce or eliminate our mislabeled samples, we invented an automated device using 4 cameras to photograph the outside of a sample tube. The system uses optical character recognition (OCR) to look for discrepancies between the patient name in our laboratory information system (LIS) vs the patient name on the customer label. All discrepancies detected by the system's software then require human inspection. The system was installed on our automated track and validated with production samples. RESULTS We obtained 1 009 830 images during the validation period, and every image was reviewed. OCR passed approximately 75% of the samples, and no mislabeled samples were passed. The 25% failed by the system included 121 samples actually mislabeled by patient name and 148 samples with spelling discrepancies between the patient name on the customer label and the patient name in our LIS. Only 71 of the 121 mislabeled samples detected by OCR were found through our normal quality assurance process. CONCLUSIONS We have invented an automated camera system that uses OCR technology to identify potential mislabeled samples. We have validated this system using samples transported on our automated track. Full implementation of this technology offers the possibility of zero mislabeled samples in the preanalytic stage.
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Nicholas, Katrina, Mary Alt, and Ella Hauwiller. "Variability of input in preposition learning by preschoolers with developmental language disorder and typically-developing language." Child Language Teaching and Therapy 35, no. 1 (February 2019): 55–74. http://dx.doi.org/10.1177/0265659019830455.
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The purpose of this study was to investigate the role of variability in teaching prepositions to preschoolers with typical development (TD) and developmental language disorder (DLD). Input variability during teaching can enhance learning, but is target dependent. We hypothesized that high variability of objects would improve preposition learning. We also examined other characteristics (e.g. vocabulary skills) of children who responded to treatment. We used a case series design, repeated across children ( n = 18) to contrast how preschoolers learned prepositions in conditions that manipulated variability of objects and labels across three treatment sessions. We contrasted a high versus low variability condition for objects and labels for one group of typically-developing (TD) children ( n = 6). In other groups (TD, n = 6; DLD, n = 6), we contrasted high versus low object variability only. Visual inspection and descriptive statistics were used to characterize gains. Half ( n = 3) of TD participants showed a low variability advantage for the condition that combined object and label variability. In the condition that only contrasted object variability, the majority ( n = 4) of TD participants showed a high variability advantage, compared to only two participants with DLD. In the high object variability condition, high receptive vocabulary scores were significantly correlated with high performance of learning prepositions ( rs = 0.71, p < 0.05). Combining high variability for objects and labels when teaching prepositions was not effective. However, high variability for objects can create a learning advantage for learning prepositions for children with typically developing language, but not all learners. Characteristics of different learners (e.g. receptive vocabulary scores) and language status (impaired or unimpaired) should be taken into consideration for future studies.
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Chiodi, Elisa, Francesco Damin, Laura Sola, Lucia Ferraro, Dario Brambilla, M. Selim Ünlü, and Marcella Chiari. "A Reliable, Label Free Quality Control Method for the Production of DNA Microarrays with Clinical Applications." Polymers 13, no. 3 (January 21, 2021): 340. http://dx.doi.org/10.3390/polym13030340.
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The manufacture of a very high-quality microarray support is essential for the adoption of this assay format in clinical routine. In fact, poorly surface-bound probes can affect the diagnostic sensitivity or, in worst cases, lead to false negative results. Here we report on a reliable and easy quality control method for the evaluation of spotted probe properties in a microarray test, based on the Interferometric Reflectance Imaging Sensor (IRIS) system, a high-resolution label free technique able to evaluate the variation of the mass bound to a surface. In particular, we demonstrated that the IRIS analysis of microarray chips immediately after probe immobilization can detect the absence of probes, which recognizably causes a lack of signal when performing a test, with clinical relevance, using fluorescence detection. Moreover, the use of the IRIS technique allowed also to determine the optimal concentration of the probe, that has to be immobilized on the surface, to maximize the target recognition, thus the signal, but to avoid crowding effects. Finally, through this preliminary quality inspection it is possible to highlight differences in the immobilization chemistries. In particular, we have compared NHS ester versus click chemistry reactions using two different surface coatings, demonstrating that, in the diagnostic case used as an example (colorectal cancer) a higher probe density does not reflect a higher binding signal, probably because of a crowding effect.
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Lebel, Paul, Rebekah Dial, Venkata N. P. Vemuri, Valentina Garcia, Joseph DeRisi, and Rafael Gómez-Sjöberg. "Label-free imaging and classification of live P. falciparum enables high performance parasitemia quantification without fixation or staining." PLOS Computational Biology 17, no. 8 (August 9, 2021): e1009257. http://dx.doi.org/10.1371/journal.pcbi.1009257.
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Manual microscopic inspection of fixed and stained blood smears has remained the gold standard for Plasmodium parasitemia analysis for over a century. Unfortunately, smear preparation consumes time and reagents, while manual microscopy is skill-dependent and labor-intensive. Here, we demonstrate that deep learning enables both life stage classification and accurate parasitemia quantification of ordinary brightfield microscopy images of live, unstained red blood cells. We tested our method using both a standard light microscope equipped with visible and near-ultraviolet (UV) illumination, and a custom-built microscope employing deep-UV illumination. While using deep-UV light achieved an overall four-category classification of Plasmodium falciparum blood stages of greater than 99% and a recall of 89.8% for ring-stage parasites, imaging with near-UV light on a standard microscope resulted in 96.8% overall accuracy and over 90% recall for ring-stage parasites. Both imaging systems were tested extrinsically by parasitemia titration, revealing superior performance over manually-scored Giemsa-stained smears, and a limit of detection below 0.1%. Our results establish that label-free parasitemia analysis of live cells is possible in a biomedical laboratory setting without the need for complex optical instrumentation. We anticipate future extensions of this work could enable label-free clinical diagnostic measurements, one day eliminating the need for conventional blood smear analysis.
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Udomsuk, Latiporn, and Siripong Siramon. "Visual Inspection of Non-Prescription Monthly Colored Contact Lenses: Safety Issues for Contact Lens Wearers." Siriraj Medical Journal 73, no. 9 (September 1, 2021): 587–93. http://dx.doi.org/10.33192/smj.2021.76.
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Objective: To survey the prevalence of non-prescription monthly colored contact lenses (CL) defects in an electronicmarketplace (e-marketplace) in Thailand using visual inspection (VI).Materials and Methods: This cross-sectional study included the 252 items listed from 23 online shops in ane-marketplace in Thailand during April 2021. Online customer reviews for each shop were examined and complaintsregarding colored CL product defects, delivery, services and abnormal symptoms after use were collected. Productpacking and visible internal and external product characteristics were visually inspected under sufficient light forthe prevalence of defects by three examiners.Results: Sixteen out of twenty-three online shops (69.57%) had customer complaints. Wrong delivery was the mostcommon complaint (60.87%). Five characteristics of product defects were described by customers, of which abnormalscratches/marks on CL (8.70%) was the most common. Abnormal symptoms after use were found in 43.84% of theshops. Two hundred and thirty-seven pairs of colored CL (94.05%), 470 vials and four blisters from 19 shops wereexamined. Defective products were found to be 8.02%. The most common visible external and internal productdefects were dirty products (3.80%) and foreign bodies in the original sealed manufacturer’s containers (1.90%),respectively. Other defects, e.g. scratched or peeling label, incompletely closed aluminum cap, surface wear of CL,abnormal scratches or marks on CL and immobility of CL in solution were also found.Conclusion: Non-prescription monthly colored CL in the e-marketplace have many visible defective characteristicsthat CL wearers should be concerned about. This study suggests that VI of CL products before use may be animportant potential safety factor for CL wearers.
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Pesco Koplowitz, Luana, Barry Koplowitz, Cheol Hee Park, and Arlo N. McGinn. "A phase I study to evaluate the pharmacokinetics of a new oncology nce, apatinib mesylate tablets, with and without food following single and multiple doses in healthy subjects." Journal of Clinical Oncology 35, no. 15_suppl (May 20, 2017): e14049-e14049. http://dx.doi.org/10.1200/jco.2017.35.15_suppl.e14049.
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e14049 Background: Apatinib Mesylate (YN968D1) is a selective inhibitor of VEGFR-2 being developed for the treatment of advanced gastric cancer. Objectives: 1. Evaluate single- and multiple-dose PKs 2. Evaluate food effect on bioavailability 3. Evaluate dose-proportionality 4. Determine CYP3A4 and 2C19 metabolic effects on the PKs Methods: Single ascending-dose (SAD), open-label, crossover of 2 oral doses of apatinib mesylate tablets (100 mg and 250 mg, [81 mg and 201 mg apatinib]) in healthy male and female volunteers with a minimum 3-day washout between dosing, plus a multiple ascending-dose (MAD), open-label, crossover study of the same doses. Both doses were administered with and without food in a crossover for the SAD and MAD parts. PK blood samples were collected for each dosing period. Subjects were genotyped for CYP3A4 and CYP2C19. WinNonlin 6.4 used for analysis. Results: 24 male and female subjects completed the SAD study section. They were extensive or intermediate metabolizers of CYP2C19, and 23/24 were normal metabolizers of CYP3A4. 22 male and female subjects completed the MAD study section. Most were extensive, intermediate or ultra-rapid metabolizers of CYP2C19; 21/22 subjects were normal metabolizers of CYP3A4. Conclusions: For the 100 mg dose in the SAD and MAD parts of the study, there was no significant food effect. For the 250 mg dose in SAD and MAD parts, food appeared to increase bioavailability by 20–30% in the SAD part, and 30–40% in the MAD part. Noncompartmental PK analysis of the SAD and MAD showed medium tmaxvalue delayed at 2 doses when apatinib was administered with food. Compartmental PK analysis showed food delayed initiation in absorption and reduced first order absorption rate constant. Dose proportionality was confirmed only for the AUC0-∞ value from the SAD-fasted cohort but inconclusive for Cmax and AUC parameters under other dosing regimens. Visual inspection of the effect of CYP2C19 genotype on the clearance of apatinib did not show correlation. Inspection of CYP3A4 genotype on the calculated clearance values was tenuous given the intermediate metabolizers (N = 1), compared to extensive metabolizers.
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Silva, Flávio Barbosa da, Bruna Ribeiro Arrais, Marcos Roberto Alves Ferreira, Iderval da Silva Júnior Sobrinho, Márcia Dias, and Cecília Nunes Moreira. "Microbiological quality of seasoned chicken cuts using Escherichia coli and Salmonella spp. as quality indicators." Research, Society and Development 9, no. 11 (November 18, 2020): e38091110013. http://dx.doi.org/10.33448/rsd-v9i11.10013.
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Enteropathogens such as Salmonella spp. and Escherichia coli are important health challenges in the poultry production chain, because when installed in the production chain, they impair the safety of food supply. The determination of the microbiological quality of chicken meat, especially when marketed in spiced form, is necessary, given that consumer prefer this type of processed protein. This work aims to evaluate the microbiological quality of the meat of seasoned chicken marketed in the municipality of Rio Verde, Goias, using Salmonella spp. and Escherichia coli as target microorganisms, and considering as variables the type of establishment, validity date and presence of municipal inspection seal. From 80 analyzed samples, 30% (24/80) of samples were positive for Salmonella spp. and 55% (44/80) for E. coli. Regarding the type of establishment, it was observed that 27.45% (14/51) of supermarket samples and 34.48% (10/29) of meat store samples were contaminated by Salmonella spp. Considering E. coli, 49.01% (25/51) of supermarket samples and 65.55% (19/29) of meat store samples were positive for this pathogen. 80% (64/80) of the samples had the municipal inspection seal (MIS), and 83.75% (67/80) contained the expiration date on the label. The legislation in its narrative guarantee’s protection for the consumer with regard to the presence of pathogenic serotypes of these agents, however, it is worth noting that the effective action of health surveillance, and the constant laboratory investigation of the products are necessary.
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Stamoulakatos, Anastasios, Javier Cardona, Chris McCaig, David Murray, Hein Filius, Robert Atkinson, Xavier Bellekens, et al. "Automatic Annotation of Subsea Pipelines Using Deep Learning." Sensors 20, no. 3 (January 26, 2020): 674. http://dx.doi.org/10.3390/s20030674.
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Regulatory requirements for sub-sea oil and gas operators mandates the frequent inspection of pipeline assets to ensure that their degradation and damage are maintained at acceptable levels. The inspection process is usually sub-contracted to surveyors who utilize sub-sea Remotely Operated Vehicles (ROVs), launched from a surface vessel and piloted over the pipeline. ROVs capture data from various sensors/instruments which are subsequently reviewed and interpreted by human operators, creating a log of event annotations; a slow, labor-intensive and costly process. The paper presents an automatic image annotation framework that identifies/classifies key events of interest in the video footage viz. exposure, burial, field joints, anodes, and free spans. The reported methodology utilizes transfer learning with a Deep Convolutional Neural Network (ResNet-50), fine-tuned on real-life, representative data from challenging sub-sea environments with low lighting conditions, sand agitation, sea-life and vegetation. The network outputs are configured to perform multi-label image classifications for critical events. The annotation performance varies between 95.1% and 99.7% in terms of accuracy and 90.4% and 99.4% in terms of F1-Score depending on event type. The performance results are on a per-frame basis and corroborate the potential of the algorithm to be the foundation for an intelligent decision support framework that automates the annotation process. The solution can execute annotations in real-time and is significantly more cost-effective than human-only approaches.
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Москаленко, В’ячеслав Васильович, Микола Олександрович Зарецький, Ярослав Юрійович Ковальський, and Сергій Сергійович Мартиненко. "МОДЕЛЬ І МЕТОД НАВЧАННЯ КЛАСИФІКАТОРА КОНТЕКСТІВ СПОСТЕРЕЖЕННЯ НА ЗОБРАЖЕННЯХ ВІДЕОІНСПЕКЦІЇ СТІЧНИХ ТРУБ." RADIOELECTRONIC AND COMPUTER SYSTEMS, no. 3 (September 28, 2020): 59–66. http://dx.doi.org/10.32620/reks.2020.3.06.
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Video inspection is often used to diagnose sewer pipe defects. To correctly encode founded defects according to existing standards, it is necessary to consider a lot of contextual information about the orientation and location of the camera from sewer pipe video inspection. A model for the classification of context on frames during observations in the video inspection of sewer pipes and a five-stage method of machine learning is proposed. The main idea of the proposed approach is to combine the methods of deep machine learning with the principles of information maximization and coding with self-correcting Hamming codes. The proposed model consists of a deep convolutional neural network with a sigmoid layer followed by the rounding output layer and information-extreme decision rules. The first stages of the method are data augmentation and training of the feature extractor in the Siamese model with softmax triplet loss function. The next steps involve calculating a binary code for each class of recognition that is used as a label in learning with a binary cross-entropy loss function to increase the compactness of the distribution of each class's observations in the Hamming binary space. At the last stage of the training method, it is supposed to optimize the parameters of radial-basis decision rules in the Hamming space for each class according to the existing information-extreme criterion. The information criterion, expressed as a logarithmic function of the accuracy characteristics of the decision rules, provides the maximum generalization and reliability of the model under the most difficult conditions in the statistical sense. The effectiveness of this approach was tested on data provided by Ace Pipe Cleaning (Kansas City, USA) and MPWiK (Wroclaw, Poland) by comparing learning results according to the proposed and traditional models and training schemes. The obtained model of the image frame classifier provides acceptable for practical use classification accuracy on the test sample, which is 96.8 % and exceeds the result of the traditional scheme of training with the softmax output layer by 6.8 %.
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Kim, Taehyeon, and Yoonsik Choe. "Deep Anomaly Detection via Morphological Transformations." Proceedings 67, no. 1 (November 11, 2020): 21. http://dx.doi.org/10.3390/asec2020-07887.
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The goal of deep anomaly detection is to identify abnormal data by utilizing a deep neural network trained by a normal training dataset. In general, industrial visual anomaly detection problems distinguish normal and abnormal data through small morphological differences, such as cracks and stains. Nevertheless, most existing algorithms focus on capturing not morphological features, but semantic features of normal data. Therefore, they yield poor performance on real-world visual inspection, even though they show their superiority in simulations with representative image classification datasets. To solve this problem, we propose a novel deep anomaly detection method that encourages understanding of salient morphological features of normal data. The main idea behind our algorithm is to train a multi-class model to classify between dozens of morphological transformations applied to all the given data. To this end, the proposed algorithm utilizes a self-supervised learning strategy, which makes unsupervised learning straightforward. Additionally, we present a kernel size loss to enhance the proposed neural networks’ morphological feature representation power. This objective function is defined as the loss between predicted kernel size and label kernel size via morphologically transformed images with the label kernel. In all experiments on the industrial dataset, the proposed method demonstrates superior performance. For instance, in the MVTec anomaly detection task, our model achieved an area under the receiver operating characteristic (AUROC) value of 72.92%, which is 8.74% higher than the semantic-feature-based deep anomaly detection.
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Uhl, Heidemarie. "Of Heroes and Victims: World War II in Austrian Memory." Austrian History Yearbook 42 (April 2011): 185–200. http://dx.doi.org/10.1017/s0067237811000117.
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In Tony Judt's historical essay on postwar Europe's political myths, Austria serves as a paradigmatic case for national cultures of commemoration that successfully suppressed their societies’ involvement in National Socialism. According to Judt, the label of “National Socialism's First Victim” was applied to a country that after the Anschluss of March 1938 had, in fact, been a real part of Nazi Germany. “IfAustriawas guiltless, then the distinctive responsibilities of non-German nationals in other lands were assuredly not open to close inspection,” notes Judt. When the postwar Austrian myth of victimhood finally disintegrated during the Waldheim debate, critics deemed the “historical lie” of the “first victim” to have been the basis for Austria's failure to confront and deal with its own Nazi past. Yet, one of the paradoxes of Austrian memory is the fact that soon after the end of the war, the victim thesis had already lost much of its relevance for many Austrians.
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Ward, Tracey L. Cash, John W. Moran, James M. Turner, and Mark R. Coleman. "Validation of a Method for the Determination of Narasin in the Edible Tissues of Chickens by Liquid Chromatography." Journal of AOAC INTERNATIONAL 88, no. 1 (January 1, 2005): 95–101. http://dx.doi.org/10.1093/jaoac/88.1.95.
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Abstract Maxiban® and Monteban® are 2 products marketed by Elanco Animal Health. They contain narasin and are used for the prevention of coccidiosis in chickens. Products used in the European market must be regularly re-registered with new data to support label claims. This study was undertaken as part of such a re-registration effort. A method for the determination of narasin in poultry tissue was previously registered with the authorities; however, a method with more environmentally friendly solvents was desired. The Canadian Food Inspection Agency accomplished this goal and published an improved method. In order to register the method with European authorities as the official Elanco method for determination of narasin, Elanco scientists were required to provide validation data for all edible poultry tissues. This paper shows the validation of the method to detect residues of narasin using solid-phase extraction followed by liquid chromatographic analysis utilizing post-column derivatization.
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Naspetti, Simona, and Raffaele Zanoli. "Consumatori e certificazione dei prodotti da agricoltura biologica. Un'analisi empirica." ECONOMIA AGRO-ALIMENTARE, no. 1 (May 2012): 195–215. http://dx.doi.org/10.3280/ecag2012-001009.
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According to the new organic (Regulation (ec) No 834/2007, a mandatory eu logo for organic food was introduced as well as new guidelines to label organic products. In the new labelling the indication of origin of the raw materials is compulsory: ‘eu Agriculture', ‘non-eu Agriculture' or ‘eu/non-eu Agriculture'. When all agricultural raw materials came from the same country, the terms ‘eu' and ‘non-eu' can be replaced or supplemented by the name of that country. The name of the Organic certifier can be also signalled to final consumers by the product labelling. In some eu countries (Denmark and Germany) the product label based on a third-party certification, private or public, make them trust the underlying certification scheme. Although consumers often lack knowledge on organic certification and organic farming practices in general, several studies highlight that scepticism and uncertainty towards organic logos and certification prevent consumers from buying more organic food. The present study analyses how consumers perceive some of the most important aspect of the new labelling regulation (the origin of raw materials and the organic certifier for organic food). Few studies exist on consumer views on organic labelling for organic food and willingness to pay for trust in the organic food quality (Burrell et al., 2006). The recommendations drawn from our findings can help stakeholders in the Italian organic sector. 415 consumers in three Italian locations (Ancona, Milano, Bari) participated to a survey in March 2010. The results show that the organic consumers prefer organic products from Europe and trust products certified by Italian (more than from foreign countries) and public certification bodies (more than private). These findings suggest the need for transparency of the complexity of the organic certification and accreditation system, unknown to most of the consumers. There is a need to make them clear what the new label characteristics stand for and remove consumer concerns of the standards and the trustworthiness of the inspection system.
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Szymanik, Barbara, Grzegorz Psuj, Maryam Hashemi, and Przemyslaw Lopato. "Detection and Identification of Defects in 3D-Printed Dielectric Structures via Thermographic Inspection and Deep Neural Networks." Materials 14, no. 15 (July 27, 2021): 4168. http://dx.doi.org/10.3390/ma14154168.
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In this paper, we propose a new method based on active infrared thermography (IRT) applied to assess the state of 3D-printed structures. The technique utilized here—active IRT—assumes the use of an external energy source to heat the tested material and to create a temperature difference between undamaged and defective areas, and this temperature difference is possible to observe with a thermal imaging camera. In the case of materials with a low value of thermal conductivity, such as the acrylonitrile butadiene styrene (ABS) plastic printout tested in the presented work, the obtained temperature differences are hardly measurable. Hence, the proposed novel IRT method is complemented by a dedicated algorithm for signal analysis and a multi-label classifier based on a deep convolutional neural network (DCNN). For the initial testing of the presented methodology, a 3D printout made in the shape of a cuboid was prepared. One type of defect was tested—surface breaking holes of various depths and diameters that were produced artificially by inclusion in the printout. As a result of examining the sample via the IRT method, a sequence of thermograms was obtained, which enabled the examination of the temporal representation of temperature variation over the examined region of the material. First, the obtained signals were analysed using a new algorithm to enhance the contrast between the background and the defect areas in the 3D print. In the second step, the DCNN was utilised to identify the chosen defect parameters. The experimental results show the high effectiveness of the proposed hybrid signal analysis method to visualise the inner structure of the sample and to determine the defect and size, including the depth and diameter.
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Choi, Youngjin, Jinhyuk Lee, and Jungsik Kong. "Performance Degradation Model for Concrete Deck of Bridge Using Pseudo-LSTM." Sustainability 12, no. 9 (May 8, 2020): 3848. http://dx.doi.org/10.3390/su12093848.
Full textAbstract:
The purpose of a bridge maintenance strategy is to make effective decisions by evaluating current performance and predicting future conditions of the bridge. The social cost because of the rapid increase in the number of decrepit bridges. The current bridge maintenance system relies on traditional man-power-based methods, which determine the bridge performance by employing a material deterioration model, and thus shows uncertainty in predicting the bridge performance. In this study, a new type of performance degradation model is developed using the actual concrete deck condition index (or grade) data of the general bridge inspection history database (1995–2017) on the national road bridge of the bridge management system in Korea. The developed model uses the long short-term memory algorithm, which is a type of recurrent neural network, as well as layer normalization and label smoothing to improve the applicability of basic data. This model can express the discrete historical degradation indices in continuous form according to the service life. In addition, it enables the prediction of bridge performance by using only basic information about new and existing bridges.
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Tayob, Shaheed. "‘O You who Believe, Eat of the Tayyibāt (pure and wholesome food) that We Have Provided You’—Producing Risk, Expertise and Certified Halal Consumption in South Africa." Journal of Religion in Africa 46, no. 1 (November 9, 2016): 67–91. http://dx.doi.org/10.1163/15700666-12340064.
Full textAbstract:
This article is an analysis of the development of halal consumption in South Africa. Research on the contemporary consumption of halal has argued for an articulation of Muslim identity in a variety of settings. What evades these scholarly analyses is the production of halal as a commodity. How is it that halal consumption, as defined by Islamic dietary law, has been produced into a separately identifiable product? This paper argues that in South Africa the production of certified halal has been produced through an extensive campaign that identified the power of the Muslim consumer, consumption as an Islamic imperative, and the contemporary risks to halal presented by food technology and cross-contamination. Communicating with the Muslim consumer and identifying risks to halal consumption established a particular form of halal-certification expertise. The result was an increase in the visibility of halal and the establishment of halal-certification organizations as necessary intermediaries for the proper practice of halal. In the process taqwa was recalibrated to mean vigilance against uncertified consumption as the inspection of a halal label was introduced into the determination of halal.
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Weiwei, Weng, Mahardhika Pratama, Andri Ashfahani, and Edward Yapp Kien Yee. "Online Semisupervised Learning Approach for Quality Monitoring of Complex Manufacturing Process." Complexity 2021 (September 1, 2021): 1–16. http://dx.doi.org/10.1155/2021/3005276.
Full textAbstract:
Data-driven quality monitoring is highly demanded in practice since it enables relieving manual quality inspection of the product quality. Conventional data-driven quality monitoring is constrained by its offline characteristic thus being unable to handle streaming nature of sensory data and nonstationary environments of machine operations. Recently, there have been pioneering works of online quality monitoring taking advantage of online learning concepts in the literature, but it is still far from realization of minimum operator intervention in the quality monitoring because it calls for full supervision in labelling data samples. This paper proposes Parsimonious Network++ (ParsNet++) as an online semisupervised learning approach being able to handle extreme label scarcity in the quality monitoring task. That is, it is capable of coping with varieties of semisupervised learning conditions including random access of ground truth and infinitely delayed access of ground truth. ParsNet++ features the one-pass learning approach to deal with streaming data while characterizing elastic structure to overcome rapidly changing data distributions. That is, it is capable of initiating its learning structure from scratch with the absence of a predefined network structure where its hidden nodes can be added and discarded on the fly in respect to drifting data distributions. Furthermore, it is equipped by a feature extraction layer in terms of 1D convolutional layer extracting natural features of multivariate time-series data samples of sensors and coping well with the many-to-one label relationship, a common problem of practical quality monitoring. Rigorous numerical evaluation has been carried out using the injection molding machine and the industrial transfer molding machine from our own projects. ParsNet++ delivers highly competitive performance even compared to fully supervised competitors.
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Porras, Juan Carlos, Mireia Bernuz, Jennifer Marfa, Arnau Pallares-Rusiñol, Mercè Martí, and María Isabel Pividori. "Comparative Study of Gold and Carbon Nanoparticles in Nucleic Acid Lateral Flow Assay." Nanomaterials 11, no. 3 (March 15, 2021): 741. http://dx.doi.org/10.3390/nano11030741.
Full textAbstract:
A lateral flow assay (LFA) is a paper-based, point-of-need test designed to detect a specific analyte in complex samples in low-resource settings. Although LFA has been successfully used in different applications, its use is still limited when high sensitivity is required, especially in the diagnosis of an early-stage condition. The limit of detection (LOD) is clearly related to the signal-generating system used to achieve the visual readout, in many cases involving nanoparticles coupled to a biomolecule, which, when combined, provides sensitivity and specificity, respectively. While colloidal gold is currently the most-used label, other detection systems are being developed. Carbon nanoparticles (CNPs) demonstrate outstanding features to improve the sensitivity of this technology by producing an increased contrast in the paper background. Based on the necessity of sensitivity improvement, the aim of this work is a comparative study, in terms of analytical performance, between commercial streptavidin gold nanoparticles (streptAv-AuNPs) and avidin carbon nanoparticles (Av-CNPs) in a nucleic acid lateral flow assay. The visual LOD of the method was calculated by serial dilution of the DNA template, ranging from 0.0 to 7 pg μL−1/1.5 × 104 CFU mL−1). The LFA achieved visual detection of as low as 2.2 × 10−2 pg μL−1 using Av-CNPs and 8.4 × 10−2 pg μL−1 using streptAv-AuNPs. These LODs could be obtained without the assistance of any instrumentation. The results demonstrate that CNPs showed an increased sensitivity, achieving the nanomolar range even by visual inspection. Furthermore, CNPs are the cheapest labels, and the suspensions are very stable and easy to modify.