Fast defect detection in homogeneous flat surface products

This paper introduces a novel hybrid approach for both defect detection and localization in homogeneous flat surface products. Real time defect detection in industrial products is a challenging problem. Fast production speeds and the variable nature of production defects complicate the process of automating the defect detection task. Speeding up the detection process is achieved in this paper by implementing a hybrid approach that is based on the statistical decision theory, multi-scale and multi-directional analysis and a neural network implementation of the optimal Bayesian classifier. The coefficient of variation is first used as a homogeneity measure for approximate defect localization. Second, features are extracted from the log Gabor filter bank response to accurately localize and detect the defect while reducing the complexity of Gabor based inspection approaches. A probabilistic neural network (PNN) is used for fast defect classification based on the maximum posterior probability of the Log-Gabor based statistical features. Experimental results show a major performance enhancement over existing defect detection approaches.     

Defect detection in textured materials using optimized filters

The problem of automated defect detection in textured materials is investigated. A new approach for defect detection using linear FIR filters with optimized energy separation is proposed. The performance of different feature separation criteria with reference to fabric defects has been evaluated. The issues relating to the design of optimal filters for supervised and unsupervised web inspection are addressed. A general web inspection system based on the optimal filters is proposed. The experiments on this new approach have yielded excellent results. The low computational requirement confirms the usefulness of the approach for industrial inspection.     

Web服务器性能评测

Web服务器性能评测是一种理解Web服务器对不同负载反应能力的方法，它对Web服务器的容量规划和性能增强有很大的帮助。讨论了Web服务器性能评测的原理、方法、难点及解决方案，介绍了基于Web负载的特点、ON／OFF源模型及浏览器／服务器体系结构，开发了一个Web服务器性能评测工具－WSBench。WSBench产生渐近自相似的HTTP请求序列，从静态文档、动态文档（没有数据库存取）、动态文档（有数据库存取）及前三者根据Zipf法则的组合4个层次来评测Web服务器的性能。性能测试结果表现为每秒请求数、每秒字节数和往返时间3个指标。最后讨论了Web服务器性能问题及使用WSBench测得的指标来建议Web服务器性能增强可以采用的方法。     

Deep learning-assisted real-time defect detection and closed-loop adjustment for additive manufacturing of continuous fiber-reinforced polymer composites

Real-time defect detection and closed-loop adjustment of additive manufacturing (AM) are essential to ensure the quality of as-fabricated products, especially for carbon fiber reinforced polymer (CFRP) composites via AM. Machine learning is typically limited to the application of online monitoring of AM systems due to a lack of accurate and accessible databases. In this work, a system is developed for real-time identification of defective regions, and closed-loop adjustment of process parameters for robot-based CFRP AM is validated. The main novelty is the development of a deep learning model for defect detection, classification, and evaluation in real-time with high accuracy. The proposed method is able to identify two types of CFRP defects (i.e., misalignment and abrasion). The combined deep learning with geometric analysis of the level of misalignment is applied to quantify the severity of individual defects. A deep learning approach is successfully developed for the online detection of defects, and the defects are effectively controlled by closed-loop adjustment of process parameters, which is never achievable in any conventional methods of composite fabrication.     

A novel ensemble decision tree based on under-sampling and clonal selection for web spam detection

Currently, web spamming is a serious problem for search engines. It not only degrades the quality of search results by intentionally boosting undesirable web pages to users, but also causes the search engine to waste a significant amount of computational and storage resources in manipulating useless information. In this paper, we present a novel ensemble classifier for web spam detection which combines the clonal selection algorithm for feature selection and under-sampling for data balancing. This web spam detection system is called USCS. The USCS ensemble classifiers can automatically sample and select sub-classifiers. First, the system will convert the imbalanced training dataset into several balanced datasets using the under-sampling method. Second, the system will automatically select several optimal feature subsets for each sub-classifier using a customized clonal selection algorithm. Third, the system will build several C4.5 decision tree sub-classifiers from these balanced datasets based on its specified features. Finally, these sub-classifiers will be used to construct an ensemble decision tree classifier which will be applied to classify the examples in the testing data. Experiments on WEBSPAM-UK2006 dataset on the web spam problem show that our proposed approach, the USCS ensemble web spam classifier, contributes significant classification performance compared to several baseline systems and state-of-the-art approaches.     

An In-Camera Data Stream Processing System for Defect Detection in Web Inspection Tasks

One of the aims of industrial machine vision is to develop computer and electronic systems to replace human vision in quality control of industrial production. Traditionally these systems consist of a line scan camera, host computer, frame grabber and one or more dedicated processing boards. In this paper we discuss the development of a new integrated design environment, developed for real-time defect detection, that eliminates the need for an external frame grabber and other associated host computer peripheral systems. The processing board contains a reconfigurable field programmable gate array FPGA inside a DALSA CCD camera. The FPGA is directly connected to the video data-stream and outputs data to a low bandwidth output bus. The system is targeted for web inspection but has the potential for broader application areas. We describe and show test results of the in-camera prototype system board and discuss some of the algorithms currently simulated and implemented for web inspection applications.     

基于Log-Gabor滤波的指纹图像增强

自动指纹识别系统的性能在很大程度上依赖于指纹图像的采集效果。指纹图像增强用于改善原始指纹图像的质量,以保证指纹特征提取的准确性,是十分必要的。Gabor滤波是比较有效的指纹图像增强方法,但是仍存在一定的局限性。与传统的Gabor滤波器相比,Log-Gabor滤波器可以在取得最佳空间定位的同时具有更宽广的频带,有利于改善指纹图像的滤波效果。本文提出采用Log-Gahor滤波器来实现指纹图像增强,先利用加窗傅里叶变换来提取指纹图像的局部频谱信息,再在频域进行滤波。在详述了滤波器的设计方法之后,给出具体的图像滤波方案,并与传统方法作了比较。实验结果表明,所提出的算法能有效改善指纹图像的质量和提高指纹识别的可靠性。     

A model-based aspect-oriented framework for building intrusion-aware software systems

Security is a critical issue for software systems, especially for those systems which are connected to networks and the Internet, since most of them suffer from various malicious attacks. Intrusion detection is an approach to protect software against such attacks. However, security vulnerabilities that are exploited by intruders cut across multiple modules in software systems and are difficult to address and monitor. These kinds of concerns, called cross-cutting concerns, can be handled by aspect-oriented software development (AOSD) for better modularization. A number of works have utilized AOSD to address security issues of software systems, but none of them has employed AOSD for intrusion detection. In this paper, we propose a model-based aspect-oriented framework for building intrusion-aware software systems. We model attack scenarios and intrusion detection aspects using an aspect-oriented Unified Modeling Language (UML) profile. Based on the UML model, the intrusion detection aspects are implemented and woven into the target system. The resulting target system has the ability to detect the intrusions automatically. We present an experimental evaluation by applying this framework for some of the most common attacks included in the Web Application Security Consortium (WASC) web security threat classification. The experimental results demonstrate that the framework is effective in specifying and implementing intrusion detection and can be applied for a wide range of attacks.     

Toward online education for fingerprint recognition: A proof-of-concept web platform

Recent advances in information and communication technology (ICT) have pushed education systems into the online paradigm. Although fingerprint recognition has received considerable attention from researchers, the concept of creating online or distance education systems for fingerprint recognition systems is a valid problem that requires further investigation. This article addresses the problem and presents a web platform for online education of a fingerprint recognition system. This study focused on the technical design and implementation of the web platform solely as a proof of concept to illustrate the possibility of creating distance education for fingerprint recognition. The developed platform offers students a way to gain knowledge about the full range of fingerprint recognition system technology. The significance of using a web platform stems from the need to supplement students’ theoretical knowledge with practical hands-on experience with information security in general and with fingerprint recognition in particular. Demonstrations such as fingerprint image enhancement and fingerprint feature extraction are included in the current platform prototype. Plots of fingerprint recognition performance curves such as false match rate (FMR) and false non-match rate (FNMR) as functions of the matching threshold curve, the receiver operating characteristic (ROC) curve, and the detection error trade-off (DET) curve are plotted as part of the platform. From a technical viewpoint, the platform was successfully implemented; however, its educational impact needs further evaluation.     

Multi-modal defect detection of residual oxide scale on a cold stainless steel strip

This work presents two initial approaches and a novel technique for the industrial inspection of residual oxide scale on a cold stainless steel strip. The research aims to develop real-time systems to detect 50-μm defects. Initially, a spectrophotometric analysis provides the wavelength regions where differences between stainless steel and residual oxide scale reflectance are highlighted. The multi-modal approach is based on laser techniques that comprise three different strategies to gradually achieve a robust stainless steel industrial inspection through the evaluation of their performance. First, an inspection system based on a single commercial laser has been designed with a dynamic threshold module. In the second approach, the inspection task is accomplished by volatilizing a reduced area of the stainless steel surface with short pulses of a high-power ultraviolet laser and then analyzing the generated plasma with an intensifier camera. The third technique consists of an innovative smart vision system for surface visual inspection based on laser diode diffuse illumination. This vision system can be configured to work with two laser illumination modes: the diffuse coaxial lighting and the diffuse bright-field lighting. These techniques aim to gradually improve surface defect detection of a cold stainless steel strip. Furthermore, some of the results of the defect detection level obtained with each approach are displayed and discussed.     

Comparison and combination of iris matchers for reliable personal authentication

The personal identification approaches using iris images are receiving increasing attention in the biometrics literature. Several methods have been presented in the literature and those based on the phase encoding of texture information are suggested to be the most promising. However, there has not been any attempt to combine these approaches to achieve further improvement in the performance. This paper presents a comparative study of the performance from the iris authentication using Log-Gabor, Haar wavelet, DCT and FFT based features. Our experimental results suggest that the performance from the Haar wavelet and Log-Gabor filter based phase encoding is the most promising among all the four approaches considered in this work. Therefore, the combination of these two matchers is most promising, both in terms of performance and the computational complexity. Our experimental results from the all 411 users (CASIA v3) and 224 users (IITD v1) database illustrate significant improvement in the performance which is not possible with either of these approaches individually.     

Characterization and Evaluation of Cache Hierarchies for Web Servers

As Internet usage continues to expand rapidly, careful attention needs to be paid to the design of Internet servers for achieving high performance and end-user satisfaction. Currently, the memory system continues to remain a significant performance bottleneck for Internet servers employing multi-GHz processors. In this paper, our aim is two-fold: (1) to characterize the cache/memory performance of web server workloads and (2) to propose and evaluate cache design alternatives for future web servers. We chose SPECweb99 as the representative web server workload and our entire characterization and evaluation methodology is based on our CASPER simulation framework. We begin by exploring the processor cache design space for single and dual-processor servers. Based on our observations, we then evaluate other cache hierarchy alternatives such as chipset caches, coherence filters and decompressed page stores. We show the sensitivity of these components to basic organization parameters such as cache size, line size and degree of associativity. We also present the performance implications of routing memory requests initiated by I/O devices through these caches. Based on detailed simulation data and its implications on system level performance, this paper shows that chipset caches have significant potential for improving future web server performance.     

FDF: Frequency detection-based filtering of scanning worms

In this paper, we propose a simple algorithm for detecting scanning worms with high detection rate and low false positive rate. The novelty of our algorithm is inspecting the frequency characteristic of scanning worms instead of counting the number of suspicious connections or packets from a monitored network. Its low complexity allows it to be used on any network-based intrusion detection system as a real-time detection module for high-speed networks.Our algorithm need not be adjusted to network status because its parameters depend on application types, which are generally and widely used in any networks such as web and P2P services. By using real traces, we evaluate the performance of our algorithm and compare it with that of SNORT. The results confirm that our algorithm outperforms SNORT with respect to detection rate and false positive rate.     

Deep learning based online metallic surface defect detection method for wire and arc additive manufacturing

Wire and arc additive manufacturing (WAAM) is an emerging manufacturing technology that is widely used in different manufacturing industries. To achieve fully automated production, WAAM requires a dependable, efficient, and automatic defect detection system. Although machine learning is dominant in the object detection domain, classic algorithms have defect detection difficulty in WAAM due to complex defect types and noisy detection environments. This paper presents a deep learning-based novel automatic defect detection solution, you only look once (YOLO)-attention, based on YOLOv4, which achieves both fast and accurate defect detection for WAAM. YOLO-attention makes improvements on three existing object detection models: the channel-wise attention mechanism, multiple spatial pyramid pooling, and exponential moving average. The evaluation on the WAAM defect dataset shows that our model obtains a 94.5 mean average precision (mAP) with at least 42 frames per second. This method has been applied to additive manufacturing of single-pass, multi-pass deposition and parts. It demonstrates its feasibility in practical industrial applications and has potential as a vision-based methodology that can be implemented in real-time defect detection systems.     

一种基于统计分析的存储系统性能调优方法

计算机系统参数的合理配置能有效提升应用程序的性能。以NFS网络存储系统为例,提出了一种基于统计分析的存储系统性能调优方法,该方法分为关键系统参数识别和关键参数性能优化两个子阶段。阶段一采用方差分析(ANOVA)来建模系统参数的性能灵敏度,识别出对应用性能有显著影响的关键系统参数;然后在此基础上,阶段二采用响应面分析(RSM)来考察各关键参数对性能响应的影响,并综合前两个子阶段给出了性能调优算法,通过该算法找出系统的最优配置,从而最终达到性能调优的目的。最后,用实验评价了文中方法在Web, E-mail, Fileserver,Linux实用程序以及微基准测试等多种重要应用场景下的性能调优结果,实验结果证实了该调优方法的有效性和实用性。     

Self-maintaining web pages

Data-intensive web-based information systems usually employ database systems to store the contents forming the basis for web page construction. Generating web pages on the fly, especially in peak times, can lead to severe performance problems. Thus, pre-generation of web pages has been suggested to be ready for prime time, allowing to reliably deliver several hundred pre-generated pages per second. Maintaining the consistency of these web pages with respect to changes within the database in an efficient way, however, represents a major challenge. This paper presents a novel approach for “self-maintaining” web pages that is, different to previous approaches, characterized by a simple (and thus, easy to maintain) database-to-web page mapping and very low page re-generation costs. This is achieved by utilizing fragmentation techniques from distributed databases, by allocating parameterized fragment classes to web page classes (rather than individual fragments to single web pages), and using the Extensible Markup Language (XML) as an intermediate layer between the database and the final web pages.     

An SPN-Based Integrated Model for Web Prefetching and Caching

The World Wide Web has become the primary means for information dissemination. Due to the limited resources of the network bandwidth, users always suffer from long time waiting. Web prefetching and web caching are the primary approaches to reducing the user perceived access latency and improving the quality of services. In this paper, a Stochastic Petri Nets （SPN） based integrated web prefetching and caching model （IWPCM） is presented and the performance evaluation of IWPCM is made. The performance metrics, access latency, throughput, HR （hit ratio） and BHR （byte hit ratio） are analyzed and discussed. Simulations show that compared with caching only model （CM）, IWPCM can further improve the throughput, HR and BHR efficiently and reduce the access latency. The performance evaluation based on the SPN model can provide a basis for implementation of web prefetching and caching and the combination of web prefetching and caching holds the promise of improving the QoS of web systems.     

An empirical comparison of commercial and open-source web vulnerability scanners

Web vulnerability scanners (WVSs) are tools that can detect security vulnerabilities in web services. Although both commercial and open-source WVSs exist, their vulnerability detection capability and performance vary. In this article, we report on a comparative study to determine the vulnerability detection capabilities of eight WVSs (both open and commercial) using two vulnerable web applications: WebGoat and Damn vulnerable web application. The eight WVSs studied were: Acunetix; HP WebInspect; IBM AppScan; OWASP ZAP; Skipfish; Arachni; Vega; and Iron WASP. The performance was evaluated using multiple evaluation metrics: precision; recall; Youden index; OWASP web benchmark evaluation; and the web application security scanner evaluation criteria. The experimental results show that, while the commercial scanners are effective in detecting security vulnerabilities, some open-source scanners (such as ZAP and Skipfish) can also be effective. In summary, this study recommends improving the vulnerability detection capabilities of both the open-source and commercial scanners to enhance code coverage and the detection rate, and to reduce the number of false-positives.     

Expanding the Criteria for Evaluating Socio-Technical Software

This paper compares two evaluation criterion frameworks for sociotechnical software. Research on the technology acceptance model (TAM) confirms that perceived usefulness and perceived ease of use are relevant criteria for users evaluating organizational software. However, information technology has changed considerably since TAM's 1989 inception, so an upgraded evaluation framework may apply. The web of system performance (WOSP) model suggests eight evaluation criteria, based on a systems theory definition of performance. This paper compares WOSP and TAM criterion frameworks in a performance evaluation experiment using the analytic hierarchy process method. Subjects who used both TAM and WOSP criteria preferred the WOSP criteria, were more satisfied with its decision outcomes, and found the WOSP evaluation more accurate and complete. As sociotechnical software becomes more complex, users may need (or prefer) more comprehensive evaluation criterion frameworks.