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Sample records for machine vision algorithm

  1. Machine vision theory, algorithms, practicalities

    CERN Document Server

    Davies, E R

    2005-01-01

    In the last 40 years, machine vision has evolved into a mature field embracing a wide range of applications including surveillance, automated inspection, robot assembly, vehicle guidance, traffic monitoring and control, signature verification, biometric measurement, and analysis of remotely sensed images. While researchers and industry specialists continue to document their work in this area, it has become increasingly difficult for professionals and graduate students to understand the essential theory and practicalities well enough to design their own algorithms and systems. This book directl

  2. Computer and machine vision theory, algorithms, practicalities

    CERN Document Server

    Davies, E R

    2012-01-01

    Computer and Machine Vision: Theory, Algorithms, Practicalities (previously entitled Machine Vision) clearly and systematically presents the basic methodology of computer and machine vision, covering the essential elements of the theory while emphasizing algorithmic and practical design constraints. This fully revised fourth edition has brought in more of the concepts and applications of computer vision, making it a very comprehensive and up-to-date tutorial text suitable for graduate students, researchers and R&D engineers working in this vibrant subject. Key features include: Practical examples and case studies give the 'ins and outs' of developing real-world vision systems, giving engineers the realities of implementing the principles in practice New chapters containing case studies on surveillance and driver assistance systems give practical methods on these cutting-edge applications in computer vision Necessary mathematics and essential theory are made approachable by careful explanations and well-il...

  3. Application of Machine Vision to Vehicle Automatic Collision Warning Algorithm

    Institute of Scientific and Technical Information of China (English)

    WANG Jiang-feng; GAO Feng; XU Guo-yan; YAO Sheng-zhuo

    2008-01-01

    Using the new technologies such as information technology, communication technology and electronic control technology, vehicle collision warning system(CWS) can acquire road condition, adjacent vehicle march condition as well as its dynamics performance continuously, then it can forecast the oncoming potential collision and give a warning. Based on the analysis of driver's driving behavior, algorithm's warning norms are determined. Based on warning norms adopting machine vision method, the cooperation collision warning algorithm(CWA) model with multi-input and multi-output is established which is used in supporting vehicle CWS. The CWA is tested using the actual data and the result shows that this algorithm can identify and carry out warning for vehicle collision efficiently, which has important meaning for improving the vehicle travel safety.

  4. Image processing with genetic algorithm in a raisin sorting system based on machine vision

    Science.gov (United States)

    Abbasgholipour, Mahdi; Alasti, Behzad Mohammadi; Abbasgholipour, Vahdi; Derakhshan, Ali; Abbasgholipour, Mohammad; Rahmatfam, Sharmin; Rahmatfam, Sheyda; Habibifar, Rahim

    2012-04-01

    This study was undertaken to develop machine vision-based raisin detection technology. Supervised color image segmentation using a Permutation-coded Genetic Algorithm (GA) identifying regions in Hue-Saturation-Intensity (HSI) color space (GAHSI) for desired and undesired raisin detection was successfully implemented. Images were captured to explore the possibility of using GAHSI to locate desired raisin and undesired raisin regions in color space simultaneously. In this research, images were processed separately using three segmentation method, K-Means clustering in L*a*b* color space and GAHSI for single image, GA for single image in Red-Green-Blue (RGB) color space (GARGB). The GAHSI results provided evidence for the existence and separability of such regions. When compared with cluster analysis-based segmentation results, the GAHSI method showed no significant difference.

  5. Machine Vision Handbook

    CERN Document Server

    2012-01-01

    The automation of visual inspection is becoming more and more important in modern industry as a consistent, reliable means of judging the quality of raw materials and manufactured goods . The Machine Vision Handbook  equips the reader with the practical details required to engineer integrated mechanical-optical-electronic-software systems. Machine vision is first set in the context of basic information on light, natural vision, colour sensing and optics. The physical apparatus required for mechanized image capture – lenses, cameras, scanners and light sources – are discussed followed by detailed treatment of various image-processing methods including an introduction to the QT image processing system. QT is unique to this book, and provides an example of a practical machine vision system along with extensive libraries of useful commands, functions and images which can be implemented by the reader. The main text of the book is completed by studies of a wide variety of applications of machine vision in insp...

  6. Machine Learning for Computer Vision

    CERN Document Server

    Battiato, Sebastiano; Farinella, Giovanni

    2013-01-01

    Computer vision is the science and technology of making machines that see. It is concerned with the theory, design and implementation of algorithms that can automatically process visual data to recognize objects, track and recover their shape and spatial layout. The International Computer Vision Summer School - ICVSS was established in 2007 to provide both an objective and clear overview and an in-depth analysis of the state-of-the-art research in Computer Vision. The courses are delivered by world renowned experts in the field, from both academia and industry, and cover both theoretical and practical aspects of real Computer Vision problems. The school is organized every year by University of Cambridge (Computer Vision and Robotics Group) and University of Catania (Image Processing Lab). Different topics are covered each year. A summary of the past Computer Vision Summer Schools can be found at: http://www.dmi.unict.it/icvss This edited volume contains a selection of articles covering some of the talks and t...

  7. Understanding and applying machine vision

    CERN Document Server

    Zeuch, Nello

    2000-01-01

    A discussion of applications of machine vision technology in the semiconductor, electronic, automotive, wood, food, pharmaceutical, printing, and container industries. It describes systems that enable projects to move forward swiftly and efficiently, and focuses on the nuances of the engineering and system integration of machine vision technology.

  8. Parallel Algorithms for Computer Vision.

    Science.gov (United States)

    1989-01-01

    developed algorithms for sev- stage at which they are used, for example by a eral early vision processes, such as edge detection, stere - navigation...system operates by receiving a stream of instructions from its front end computer. A microcontroller receives the instructions, expands each of them...instructions flow into the Connection Machine hardware from the front end. These I macro-instructions are sent to a microcontroller , which expands them

  9. Machine vision is not computer vision

    Science.gov (United States)

    Batchelor, Bruce G.; Charlier, Jean-Ray

    1998-10-01

    The identity of Machine Vision as an academic and practical subject of study is asserted. In particular, the distinction between Machine Vision on the one hand and Computer Vision, Digital Image Processing, Pattern Recognition and Artificial Intelligence on the other is emphasized. The article demonstrates through four cases studies that the active involvement of a person who is sensitive to the broad aspects of vision system design can avoid disaster and can often achieve a successful machine that would not otherwise have been possible. This article is a transcript of the key- note address presented at the conference. Since the proceedings are prepared and printed before the conference, it is not possible to include a record of the response to this paper made by the delegates during the round-table discussion. It is hoped to collate and disseminate these via the World Wide Web after the event. (A link will be provided at http://bruce.cs.cf.ac.uk/bruce/index.html.).

  10. Detection algorithm for glass bottle mouth defect by continuous wavelet transform based on machine vision

    Science.gov (United States)

    Qian, Jinfang; Zhang, Changjiang

    2014-11-01

    An efficient algorithm based on continuous wavelet transform combining with pre-knowledge, which can be used to detect the defect of glass bottle mouth, is proposed. Firstly, under the condition of ball integral light source, a perfect glass bottle mouth image is obtained by Japanese Computar camera through the interface of IEEE-1394b. A single threshold method based on gray level histogram is used to obtain the binary image of the glass bottle mouth. In order to efficiently suppress noise, moving average filter is employed to smooth the histogram of original glass bottle mouth image. And then continuous wavelet transform is done to accurately determine the segmentation threshold. Mathematical morphology operations are used to get normal binary bottle mouth mask. A glass bottle to be detected is moving to the detection zone by conveyor belt. Both bottle mouth image and binary image are obtained by above method. The binary image is multiplied with normal bottle mask and a region of interest is got. Four parameters (number of connected regions, coordinate of centroid position, diameter of inner cycle, and area of annular region) can be computed based on the region of interest. Glass bottle mouth detection rules are designed by above four parameters so as to accurately detect and identify the defect conditions of glass bottle. Finally, the glass bottles of Coca-Cola Company are used to verify the proposed algorithm. The experimental results show that the proposed algorithm can accurately detect the defect conditions of the glass bottles and have 98% detecting accuracy.

  11. Parallel Algorithm for GPU Processing; for use in High Speed Machine Vision Sensing of Cotton Lint Trash

    Directory of Open Access Journals (Sweden)

    Mathew G. Pelletier

    2008-02-01

    Full Text Available One of the main hurdles standing in the way of optimal cleaning of cotton lint isthe lack of sensing systems that can react fast enough to provide the control system withreal-time information as to the level of trash contamination of the cotton lint. This researchexamines the use of programmable graphic processing units (GPU as an alternative to thePC’s traditional use of the central processing unit (CPU. The use of the GPU, as analternative computation platform, allowed for the machine vision system to gain asignificant improvement in processing time. By improving the processing time, thisresearch seeks to address the lack of availability of rapid trash sensing systems and thusalleviate a situation in which the current systems view the cotton lint either well before, orafter, the cotton is cleaned. This extended lag/lead time that is currently imposed on thecotton trash cleaning control systems, is what is responsible for system operators utilizing avery large dead-band safety buffer in order to ensure that the cotton lint is not undercleaned.Unfortunately, the utilization of a large dead-band buffer results in the majority ofthe cotton lint being over-cleaned which in turn causes lint fiber-damage as well assignificant losses of the valuable lint due to the excessive use of cleaning machinery. Thisresearch estimates that upwards of a 30% reduction in lint loss could be gained through theuse of a tightly coupled trash sensor to the cleaning machinery control systems. Thisresearch seeks to improve processing times through the development of a new algorithm forcotton trash sensing that allows for implementation on a highly parallel architecture.Additionally, by moving the new parallel algorithm onto an alternative computing platform,the graphic processing unit “GPU”, for processing of the cotton trash images, a speed up ofover 6.5 times, over optimized code running on the PC’s central processing

  12. 一种基于机器视觉的跑偏角估计算法%An algorithm of machine vision-based yaw-angle estimation

    Institute of Scientific and Technical Information of China (English)

    魏丽; 田克纯

    2011-01-01

    在移动机器人、汽车等自主行进过程中,由于受到道路情况等因素的影响,极易出现方向跑偏,导致其无法按照预定路径前进.为了防止出现跑偏等危险情况,研究了利用机器视觉估计预定运动方向和实际运动方向夹角即跑偏角的方法.介绍了利用机器视觉测量跑偏角的基本原理;研究了基于特征点跟踪的跑偏角估计算法.实验研究表明:该方法可以有效地在行进过程中计算跑偏角.%In the process of robot and automobile autonomous moving, vehicle cannot move along predestinate path due to road status and other factors. An algorithm of machine vision-based yaw angle computation for avoiding vehicle in danger is presented. The system constitutes of machine vision-based yaw angle computation is introduced. Algorithm of yaw angle computation which is built on feature points tracking is studied. Experimental results show that this method can compute yaw-angle in the process of vehicle moving effectively.

  13. Machine Vision Implementation in Rapid PCB Prototyping

    Directory of Open Access Journals (Sweden)

    Yosafat Surya Murijanto

    2012-03-01

    Full Text Available Image processing, the heart of machine vision, has proven itself to be an essential part of the industries today. Its application has opened new doorways, making more concepts in manufacturing processes viable. This paper presents an application of machine vision in designing a module with the ability to extract drills and route coordinates from an un-mounted or mounted printed circuit board (PCB. The algorithm comprises pre-capturing processes, image segmentation and filtering, edge and contour detection, coordinate extraction, and G-code creation. OpenCV libraries and Qt IDE are the main tools used. Throughout some testing and experiments, it is concluded that the algorithm is able to deliver acceptable results. The drilling and routing coordinate extraction algorithm can extract in average 90% and 82% of the whole drills and routes available on the scanned PCB in a total processing time of less than 3 seconds. This is achievable through proper lighting condition, good PCB surface condition and good webcam quality. 

  14. Industrial Inspection with Open Eyes: Advance with Machine Vision Technology

    Energy Technology Data Exchange (ETDEWEB)

    Liu, Zheng; Ukida, H.; Niel, Kurt; Ramuhalli, Pradeep

    2015-10-01

    Machine vision systems have evolved significantly with the technology advances to tackle the challenges from modern manufacturing industry. A wide range of industrial inspection applications for quality control are benefiting from visual information captured by different types of cameras variously configured in a machine vision system. This chapter screens the state of the art in machine vision technologies in the light of hardware, software tools, and major algorithm advances for industrial inspection. The inspection beyond visual spectrum offers a significant complementary to the visual inspection. The combination with multiple technologies makes it possible for the inspection to achieve a better performance and efficiency in varied applications. The diversity of the applications demonstrates the great potential of machine vision systems for industry.

  15. Machine vision and the OMV

    Science.gov (United States)

    Mcanulty, M. A.

    1986-01-01

    The orbital Maneuvering Vehicle (OMV) is intended to close with orbiting targets for relocation or servicing. It will be controlled via video signals and thruster activation based upon Earth or space station directives. A human operator is squarely in the middle of the control loop for close work. Without directly addressing future, more autonomous versions of a remote servicer, several techniques that will doubtless be important in a future increase of autonomy also have some direct application to the current situation, particularly in the area of image enhancement and predictive analysis. Several techniques are presentet, and some few have been implemented, which support a machine vision capability proposed to be adequate for detection, recognition, and tracking. Once feasibly implemented, they must then be further modified to operate together in real time. This may be achieved by two courses, the use of an array processor and some initial steps toward data reduction. The methodology or adapting to a vector architecture is discussed in preliminary form, and a highly tentative rationale for data reduction at the front end is also discussed. As a by-product, a working implementation of the most advanced graphic display technique, ray-casting, is described.

  16. Research on color difference detection algorithm based on machine vision%基于机器视觉的色差检测算法

    Institute of Scientific and Technical Information of China (English)

    范鹏飞; 孙俊

    2016-01-01

    Algorithm for color difference detection based on machine vision is studied to solve the problem of color difference detection in metal printing. By printing color mark in metal blank area,capture color mark areas on metal printing products using industrial CCD cameras,digital image processing technique is applied to extract color. Use color difference detection algorithm based on HSV color space and color difference detection algorithm based on CIELAB color space,analyze color difference detection results in two kinds of color space,using the method combining the two kinds of color space detection algorithms,implement rapid and effective detection,and at the same time,it can ensure accuracy of detection results.%针对金属印刷质量中的色差检测问题,采用机器视觉的技术对色差检测算法进行了研究。通过在金属印刷品的留白区域印刷色标,使用工业CCD相机采集金属印刷产品上的色标区域,使用数字图像处理技术提取色标。使用了基于HSV颜色空间的色差检测算法和基于CIELAB颜色空间的色差检测算法,分析了两种颜色空间下色差检测的实验结果,采用两种颜色空间检测算法相结合的方法,实现对色差合理有效的快速检测,同时能保证检测结果的准确性。

  17. Insect vision as model for machine vision

    Science.gov (United States)

    Osorio, D.; Sobey, Peter J.

    1992-11-01

    The neural architecture, neurophysiology and behavioral abilities of insect vision are described, and compared with that of mammals. Insects have a hardwired neural architecture of highly differentiated neurons, quite different from the cerebral cortex, yet their behavioral abilities are in important respects similar to those of mammals. These observations challenge the view that the key to the power of biological neural computation is distributed processing by a plastic, highly interconnected, network of individually undifferentiated and unreliable neurons that has been a dominant picture of biological computation since Pitts and McCulloch's seminal work in the 1940's.

  18. Color in machine vision and its application

    Institute of Scientific and Technical Information of China (English)

    2001-01-01

    Color is the phenomenon of human visual perception and the module of machine vision. Color information is widely used in the areas of virtual reality and humancomputer interaction. Color is the product of a visual environment, illumination and the human brain. Research on color information representation and its processing is typically interdisciplinary. Based on our research work on human color perception and machine color vision and its application, we summarized the hotspots of color studies in recent developments and new approaches to color vision,including basic theories and the application of color information in virtual reality, content-based image retrieval, and face recognition.

  19. Research on Manufacturing Technology Based on Machine Vision

    Institute of Scientific and Technical Information of China (English)

    HU Zhanqi; ZHENG Kuijing

    2006-01-01

    The concept of machine vision based manufacturing technology is proposed first, and the key algorithms used in two-dimensional and three-dimensional machining are discussed in detail. Machining information can be derived from the binary images and gray picture after processing and transforming the picture. Contour and the parallel cutting method about two-dimensional machining are proposed. Polygon approximating algorithm is used to cutting the profile of the workpiece. Fill Scanning algorithm used to machining inner part of a pocket. The improved Shape From Shading method with adaptive pre-processing is adopted to reconstruct the three-dimensional model. Layer cutting method is adopted for three-dimensional machining. The tool path is then gotten from the model, and NC code is formed subsequently. The model can be machined conveniently by the lathe, milling machine or engraver. Some examples are given to demonstrate the results of ImageCAM system, which is developed by the author to implement the algorithms previously mentioned.

  20. Machine Vision Giving Eyes to Robots. Resources in Technology.

    Science.gov (United States)

    Technology Teacher, 1990

    1990-01-01

    This module introduces machine vision, which can be used for inspection, robot guidance and part sorting. The future for machine vision will include new technology and will bring vision systems closer to the ultimate vision processor, the human eye. Includes a student quiz, outcomes, and activities. (JOW)

  1. Machine Learning for Vision-Based Motion Analysis

    CERN Document Server

    Wang, Liang; Cheng, Li; Pietikainen, Matti

    2011-01-01

    Techniques of vision-based motion analysis aim to detect, track, identify, and generally understand the behavior of objects in image sequences. With the growth of video data in a wide range of applications from visual surveillance to human-machine interfaces, the ability to automatically analyze and understand object motions from video footage is of increasing importance. Among the latest developments in this field is the application of statistical machine learning algorithms for object tracking, activity modeling, and recognition. Developed from expert contributions to the first and second In

  2. Machine Learning Techniques in Clinical Vision Sciences.

    Science.gov (United States)

    Caixinha, Miguel; Nunes, Sandrina

    2017-01-01

    This review presents and discusses the contribution of machine learning techniques for diagnosis and disease monitoring in the context of clinical vision science. Many ocular diseases leading to blindness can be halted or delayed when detected and treated at its earliest stages. With the recent developments in diagnostic devices, imaging and genomics, new sources of data for early disease detection and patients' management are now available. Machine learning techniques emerged in the biomedical sciences as clinical decision-support techniques to improve sensitivity and specificity of disease detection and monitoring, increasing objectively the clinical decision-making process. This manuscript presents a review in multimodal ocular disease diagnosis and monitoring based on machine learning approaches. In the first section, the technical issues related to the different machine learning approaches will be present. Machine learning techniques are used to automatically recognize complex patterns in a given dataset. These techniques allows creating homogeneous groups (unsupervised learning), or creating a classifier predicting group membership of new cases (supervised learning), when a group label is available for each case. To ensure a good performance of the machine learning techniques in a given dataset, all possible sources of bias should be removed or minimized. For that, the representativeness of the input dataset for the true population should be confirmed, the noise should be removed, the missing data should be treated and the data dimensionally (i.e., the number of parameters/features and the number of cases in the dataset) should be adjusted. The application of machine learning techniques in ocular disease diagnosis and monitoring will be presented and discussed in the second section of this manuscript. To show the clinical benefits of machine learning in clinical vision sciences, several examples will be presented in glaucoma, age-related macular degeneration

  3. Machine vision and mechatronics in practice

    CERN Document Server

    Brett, Peter

    2015-01-01

    The contributions for this book have been gathered over several years from conferences held in the series of Mechatronics and Machine Vision in Practice, the latest of which was held in Ankara, Turkey. The essential aspect is that they concern practical applications rather than the derivation of mere theory, though simulations and visualization are important components. The topics range from mining, with its heavy engineering, to the delicate machining of holes in the human skull or robots for surgery on human flesh. Mobile robots continue to be a hot topic, both from the need for navigation and for the task of stabilization of unmanned aerial vehicles. The swinging of a spray rig is damped, while machine vision is used for the control of heating in an asphalt-laying machine.  Manipulators are featured, both for general tasks and in the form of grasping fingers. A robot arm is proposed for adding to the mobility scooter of the elderly. Can EEG signals be a means to control a robot? Can face recognition be ac...

  4. Conveyor Belt Surface Image Correction and Fault Detection Algorithm Research Based on Machine Vision%基于机器视觉的输送带图像校正和故障检测算法研究

    Institute of Scientific and Technical Information of China (English)

    2016-01-01

    为了消除基于机器视觉的输送带故障在线监测系统中采集图像的不均匀光照影响,提高图像质量,检测出图像中的故障区域,提出了一种基于机器视觉的输送带图像校正和故障检测算法.该算法首先采用Butterworth低通滤波器对图像滤波,结合Retinex理论计算估计真实图像的背景,对图像进行灰度校正,得到校正后的图像;然后将机器视觉与生物视觉相结合,利用PCNN算法,对采集的图像进行检测,检测出故障区域.实验结果表明,算法能有效校正输送带表面图像,清晰检测出故障区域,具有很高的应用价值.%For the purposes of eliminating the influence of the non-uniform illumination which was used in on-line fault moni-toring system of conveyor belt based on machine vision and improved the quality of detected image and detecting the fault area of the image, a new kind of detection algorithm based on machine vision was proposed which can be used to realize the image correc-tion and fault detection of conveyor belt. The proposed algorithm firstly implemented the low-pass filtering of the acquired images by using Butterworth low-pass filter, then established estimated background model of the non-uniform illumination based on Retinex theory. Gray scale of the image can be amended evenly. Lastly, by using the combination the machine vision with biological vision and PNCC theory, the defected area of collected surface image of the conveyor belt was detected. Experimental results show that the proposed algorithm can be effectively used to correct the uneven gray scale of the surface images and detect the defected area of the surface image. It proves that this proposed algorithm has very high application value in mine belt conveyor supervision system.

  5. Machine vision inspection of rice seed based on Hough transform

    Institute of Scientific and Technical Information of China (English)

    成芳; 应义斌

    2004-01-01

    A machine vision system was developed to inspect the quality of rice seeds. Five varieties of Jinyou402, Shanyou10, Zhongyou207, Jiayou and IIyou were evaluated. The images of both sides of rice seed with black background and white background were acquired with the image processing system for identifying external features of rice seeds. Five image sets consisting of 600 original images each were obtained. Then a digital image processing algorithm based on Hough transform was developed to inspect the rice seeds with incompletely closed glumes. The algorithm was implemented with all image sets using a Matlab 6.5 procedure. The results showed that the algorithm achieved an average accuracy of 96% for normal seeds, 92% for seeds with fine fissure and 87% for seeds with incompletely closed glumes. The algorithm was proved to be applicable to different seed varieties and insensitive to the color of the background.

  6. Machine vision inspection of rice seed based on Hough transform

    Institute of Scientific and Technical Information of China (English)

    成芳; 应义斌

    2004-01-01

    A machine vision system was developed to inspect the quality of rice seeds. Five varieties of Jinyou402,Shanyou 10, Zhongyou207, Jiayou and Ilyou were evaluated. The images of both sides of rice seed with black background and white background were acquired with the image processing system for identifying external features of rice seeds. Five image sets consisting of 600 original images each were obtained. Then a digital image processing algorithm based on Hough transform was developed to inspect the rice seeds with incompletely closed glumes. The algorithm was implemented with all image sets using a Matlab 6.5 procedure. The results showed that the algorithm achieved an average accuracy of 96% for normal seeds, 92% for seeds with fine fissure and 87% for seeds with incompletely closed glumes. The algorithm was proved to be applicable to different seed varieties and insensitive to the color of the background.

  7. Bioinspired minimal machine multiaperture apposition vision system.

    Science.gov (United States)

    Davis, John D; Barrett, Steven F; Wright, Cameron H G; Wilcox, Michael

    2008-01-01

    Traditional machine vision systems have an inherent data bottleneck that arises because data collected in parallel must be serialized for transfer from the sensor to the processor. Furthermore, much of this data is not useful for information extraction. This project takes inspiration from the visual system of the house fly, Musca domestica, to reduce this bottleneck by employing early (up front) analog preprocessing to limit the data transfer. This is a first step toward an all analog, parallel vision system. While the current implementation has serial stages, nothing would prevent it from being fully parallel. A one-dimensional photo sensor array with analog pre-processing is used as the sole sensory input to a mobile robot. The robot's task is to chase a target car while avoiding obstacles in a constrained environment. Key advantages of this approach include passivity and the potential for very high effective "frame rates."

  8. Fresh market carrot inspection by machine vision

    Science.gov (United States)

    Howarth, M. Scott; Searcy, Stephen W.

    1991-02-01

    A machine vision system was developed to inspect fresh market carrots. It was designed to grade carrots with an axial and transverse resolution of 0. 5mmper pixel. Hardware consisted of camera digital signal processing (DSP) imaging board host computer and illumination components. Feature extraction methods detect the major defects. A Bayes classification technique was used to construct the decision function which classify carrots as acceptable or cull. The system was able to image and classify in approximately 2. 5carrots/second. 1.

  9. Brake Pedal Displacement Measuring System based on Machine Vision

    Directory of Open Access Journals (Sweden)

    Chang Wang

    2013-10-01

    Full Text Available Displacement of brake pedal was an important characteristic of driving behavior. This paper proposed a displacement measure algorithm based on machine vision. Image of brake pedal was captured by camera from left side, and images were processed in industry computer. Firstly, average smooth algorithm and wavelet transform algorithm were used to smooth the original image consecutively. Then, edge extracting method which combined Roberts’s operator with wavelet analysis was used to identify the edge of brake pedal. At last, least square method was adopted to recognize the characteristic line of brake pedal’s displacement. The experimental results demonstrated that the proposed method takes the advantages of Roberts’s operator and wavelet transform, it can obtain better measurement result as well as linear displacement sensors

  10. A Machine Vision System for Ball Grid Array Package Inspection

    Institute of Scientific and Technical Information of China (English)

    XIA Nian-jiong; CAO Qi-xin; LEE Jey

    2005-01-01

    An optical inspection method of the Ball Grid Array package (BGA) was proposed by using a machine vision system. The developed machine vision system could get main critical factors for BGA quality evaluation, such as the height of solder ball, diameter, pitch and coplanarity. The experiment has proved that this system is available for BGA failure detection.

  11. A Machine Vision System for Automatically Grading Hardwood Lumber - (Proceedings)

    Science.gov (United States)

    Richard W. Conners; Tai-Hoon Cho; Chong T. Ng; Thomas H. Drayer; Joe G. Tront; Philip A. Araman; Robert L. Brisbon

    1990-01-01

    Any automatic system for grading hardwood lumber can conceptually be divided into two components. One of these is a machine vision system for locating and identifying grading defects. The other is an automatic grading program that accepts as input the output of the machine vision system and, based on these data, determines the grade of a board. The progress that has...

  12. Machine-vision based optofluidic cell sorting

    DEFF Research Database (Denmark)

    Glückstad, Jesper; Bañas, Andrew

    In contemporary life science there is an increasing emphasis on sorting rare disease-indicating cells within small dilute quantities such as in the confines of optofluidic lab-on-chip devices. Our approach to this is based on the use of optical forces to isolate red blood cells detected by advanc...... the available light and creating 2D or 3D beam distributions aimed at the positions of the detected cells. Furthermore, the beam shaping freedom provided by GPC can allow optimizations in the beam’s propagation and its interaction with the laser catapulted and sorted cells....... machine vision1. This approach is gentler, less invasive and more economical compared to conventional FACS-systems. As cells are less responsive to plastic or glass objects commonly used in the optical manipulation literature2, and since laser safety would be an issue in clinical use, we develop efficient...

  13. Sorting System Algorithms Based on Machine Vision for Delta Robot%基于机器视觉的Delta机器人分拣系统算法

    Institute of Scientific and Technical Information of China (English)

    倪鹤鹏; 刘亚男; 张承瑞; 王云飞; 夏飞虎; 邱正师

    2016-01-01

    To overcome the repeat shooting to workpieces by vision system in sorting process, an image deduplication algorithm based on time and workpieces’ positions is proposed. The running time of the real-time sorting system is used as basis of each sorting module, and the predicted time that workpieces arrive at a fixed reference position is combined with its current location into a set of coordinates to uniquely identify a part. So the duplicate image information can be found and removed by comparing those coordinates periodically. At the same time, in order to improve sorting efficiency, a dynamic picking algorithm based on Newton-Raphson method is proposed. The non-linear mathematical model is established for workpiece tracking, which is solved by Newton-Raphson iteration. Finally, the proposed dynamic picking algorithm is verified by MATLAB. In prototype test the maximum sorting speed can reach 110 times per minute, mistaken-grab rate is lower than 2‰, missing-grab rate is 0, which proves that the algorithms can meet the real-time, the accuracy and the stability requirements.%针对分拣过程中视觉系统对工件的重复拍摄问题,提出一种基于时间与工件位置的图像去重复算法,以实时分拣系统的系统运行时刻作为各单元的时间基准,将预测的工件到达某一固定参考位置的时刻与工件当前位置组合成一组能唯一识别工件的坐标,经周期性比较,判断并去掉重复图像信息。同时为提高分拣效率,提出一种基于牛顿-拉夫森迭代的动态抓取算法,建立了机器人跟踪工件的数学模型,并通过牛顿-拉夫森方法求解该非线性数学模型。最后用MATLAB对动态抓取算法进行了验证。样机实验中最快分拣速度达110次/min,误抓率小于2‰,漏抓率为0,证明了算法能够满足实时性要求,同时具有较高的准确性和稳定性。

  14. Vision-Based People Detection System for Heavy Machine Applications

    Directory of Open Access Journals (Sweden)

    Vincent Fremont

    2016-01-01

    Full Text Available This paper presents a vision-based people detection system for improving safety in heavy machines. We propose a perception system composed of a monocular fisheye camera and a LiDAR. Fisheye cameras have the advantage of a wide field-of-view, but the strong distortions that they create must be handled at the detection stage. Since people detection in fisheye images has not been well studied, we focus on investigating and quantifying the impact that strong radial distortions have on the appearance of people, and we propose approaches for handling this specificity, adapted from state-of-the-art people detection approaches. These adaptive approaches nevertheless have the drawback of high computational cost and complexity. Consequently, we also present a framework for harnessing the LiDAR modality in order to enhance the detection algorithm for different camera positions. A sequential LiDAR-based fusion architecture is used, which addresses directly the problem of reducing false detections and computational cost in an exclusively vision-based system. A heavy machine dataset was built, and different experiments were carried out to evaluate the performance of the system. The results are promising, in terms of both processing speed and performance.

  15. A New Incremental Support Vector Machine Algorithm

    Directory of Open Access Journals (Sweden)

    Wenjuan Zhao

    2012-10-01

    Full Text Available Support vector machine is a popular method in machine learning. Incremental support vector machine algorithm is ideal selection in the face of large learning data set. In this paper a new incremental support vector machine learning algorithm is proposed to improve efficiency of large scale data processing. The model of this incremental learning algorithm is similar to the standard support vector machine. The goal concept is updated by incremental learning. Each training procedure only includes new training data. The time complexity is independent of whole training set. Compared with the other incremental version, the training speed of this approach is improved and the change of hyperplane is reduced.

  16. Machine vision for airport runway identification

    Science.gov (United States)

    Schubert, Matthew; Moore, Andrew J.; Dolph, Chester; Woodell, Glenn

    2015-03-01

    For rigid objects and fixed scenes, current machine vision technology is capable of identifying imagery rapidly and with specificity over a modest range of camera viewpoints and scene illumination. We applied that capability to the problem of runway identification using video of sixteen runway approaches at nine locations, subject to two simplifying assumptions. First, by using approach video from just one of the several possible seasonal variations (no snow cover and full foliage), we artificially removed one source of scene variation in this study. Secondly, by not using approach video at dawn and dusk, we limited the study to two illumination variants (day and night). We did allow scene variation due to atmospheric turbidity by using approach video from rainy and foggy days in some daytime approaches. With suitable ensemble statistics to account for temporal continuity in video, we observed high location specificity (<90% Bayesian posterior probability). We also tested repeatability, i.e., identification of a given runway across multiple videos, and observed robust repeatability only if illumination (day vs. night) was the same and approach visibility was good. Both specificity and repeatability degraded in poor weather conditions. The results of this simplified study show that geolocation via real-time comparison of cockpit image sensor video to a database of runway approach imagery is feasible, as long as the database contains imagery from about the same time of day (complete daylight and nighttime, excluding dawn and dusk) and the weather is clear at the time of the flight.

  17. Target-tracking algorithm for omnidirectional vision

    Science.gov (United States)

    Cai, Chengtao; Weng, Xiangyu; Fan, Bing; Zhu, Qidan

    2017-05-01

    Omnidirectional vision with the advantage of a large field-of-view overcomes the problem that a target is easily lost due to the narrow sight of perspective vision. We improve a target-tracking algorithm based on discriminative tracking features in several aspects and propose a target-tracking algorithm for an omnidirectional vision system. (1) An elliptical target window expression model is presented to represent the target's outline, which can adapt to the deformation of an object and reduce background interference. (2) The background-weighted linear RGB histogram target feature is introduced, which decreases the weight of the background feature. (3) The Bhattacharyya coefficients-based feature identification method is employed, which reduces the computation time of the tracking algorithm. (4) An adaptive target scale and orientation measurement method is applied to adapt to severe deformations of the target's outline. (5) A model update strategy is put forward, which is based on similarity measurements to achieve an effective and accurate model update. The experimental results show the proposed algorithm can achieve better performance than the state-of-the-art algorithms when using omnidirectional vision to perform long-term target-tracking tasks.

  18. 基于机器视觉的水稻秧苗图像分割%Machine vision based segmentation algorithm for rice seedling

    Institute of Scientific and Technical Information of China (English)

    袁加红; 朱德泉; 孙丙宇; 孙磊; 武立权; 宋宇; 蒋锐

    2016-01-01

    水稻秧苗的识别是水稻插秧机自主导航系统的关键内容之一.针对插秧机机器视觉导航中稻田图像秧苗与背景分割问题,建立了基于RGB(红绿蓝)颜色空间的秧苗表面颜色模型.通过颜色特征对秧苗图像进行处理,使用Photoshop软件获取秧苗部分和背景R,G,B分量值;通过对G-R值与G-B值的分析统计,发现两者之间存在分界关系:各自的权重与各分量的乘积之和为某个定值;为方便分析,选取权值a,b为0.5,即ExG因子,采用Otsu法获取定值最佳值,最大程度分割出目标和背景.与适合于大多数绿色作物的传统RGB法进行比较,并采用分割质量因子和算法运算时间作为评判标准,分析各算法的综合性能.试验发现,ExG因子结合Otsu分割法分割效果相对理想、稳定性更高,而且耗时更短.%The recognition of rice seedling is one of the significant parts of autonomous guidance for rice transplan-ting. Considering the segmentation of seedlings and remainder based on machine vision system, a simple dichromatic reflection model was established in RGB color space, which represented that the seedling could be recognized by u-sing its color feature. The values of R, G, B components of seedlings and remainder were obtained in Photoshop soft-ware respectively and analyzed statistically in order to get the relation between them. In order to simplify the compu-ting process, the weight values of a and b were set as 0. 5, ExG index and Otsu method (ExG+Otsu method) which could obtain the optimal threshold were combined to distinguish the seedlings and remainder well. The RGB method and previous ExG+Otsu method were carried out to compare their performance intuitively. Their comprehensive per-formance was evaluated with segmentation quality factor and time consuming. The results have proved that the latter for segmenting was more efficient, highly stable and timesaving.

  19. Application of Machine Vision Technique in Weed Identification

    Institute of Scientific and Technical Information of China (English)

    LIU Zhen-heng; ZHANG Chang-li; FANG Jun-long

    2004-01-01

    This paper mainly introduces some foreign research methods and fruits about weed identification by applying machine vision. This facet researches is lack in our country, this paper could be reference for domestic studies about weed identification.

  20. Theories and Algorithms of Computational Vision

    Institute of Scientific and Technical Information of China (English)

    Ma Songde; Tan Tieniu; Hu Zhanyi; Jiang Tianzi; Lu Hanqing

    2005-01-01

    @@ Inspired by the recent progresses in the related fields such as cognitive psychology, neural physiology and neural anatomy, the project aims to put forward new computational theories and algorithms which could overcome the main shortcomings in the Marr's computational theory, a dominant paradigm for the last 20 years in computer vision field.

  1. Machine Vision Systems for Processing Hardwood Lumber and Logs

    Science.gov (United States)

    Philip A. Araman; Daniel L. Schmoldt; Tai-Hoon Cho; Dongping Zhu; Richard W. Conners; D. Earl Kline

    1992-01-01

    Machine vision and automated processing systems are under development at Virginia Tech University with support and cooperation from the USDA Forest Service. Our goals are to help U.S. hardwood producers automate, reduce costs, increase product volume and value recovery, and market higher value, more accurately graded and described products. Any vision system is...

  2. Spectrum Assignment Algorithm for Cognitive Machine-to-Machine Networks

    Directory of Open Access Journals (Sweden)

    Soheil Rostami

    2016-01-01

    Full Text Available A novel aggregation-based spectrum assignment algorithm for Cognitive Machine-To-Machine (CM2M networks is proposed. The introduced algorithm takes practical constraints including interference to the Licensed Users (LUs, co-channel interference (CCI among CM2M devices, and Maximum Aggregation Span (MAS into consideration. Simulation results show clearly that the proposed algorithm outperforms State-Of-The-Art (SOTA algorithms in terms of spectrum utilisation and network capacity. Furthermore, the convergence analysis of the proposed algorithm verifies its high convergence rate.

  3. Advanced Machine learning Algorithm Application for Rotating Machine Health Monitoring

    Energy Technology Data Exchange (ETDEWEB)

    Kanemoto, Shigeru; Watanabe, Masaya [The University of Aizu, Aizuwakamatsu (Japan); Yusa, Noritaka [Tohoku University, Sendai (Japan)

    2014-08-15

    The present paper tries to evaluate the applicability of conventional sound analysis techniques and modern machine learning algorithms to rotating machine health monitoring. These techniques include support vector machine, deep leaning neural network, etc. The inner ring defect and misalignment anomaly sound data measured by a rotating machine mockup test facility are used to verify the above various kinds of algorithms. Although we cannot find remarkable difference of anomaly discrimination performance, some methods give us the very interesting eigen patterns corresponding to normal and abnormal states. These results will be useful for future more sensitive and robust anomaly monitoring technology.

  4. Learning surface molecular structures via machine vision

    Science.gov (United States)

    Ziatdinov, Maxim; Maksov, Artem; Kalinin, Sergei V.

    2017-08-01

    Recent advances in high resolution scanning transmission electron and scanning probe microscopies have allowed researchers to perform measurements of materials structural parameters and functional properties in real space with a picometre precision. In many technologically relevant atomic and/or molecular systems, however, the information of interest is distributed spatially in a non-uniform manner and may have a complex multi-dimensional nature. One of the critical issues, therefore, lies in being able to accurately identify (`read out') all the individual building blocks in different atomic/molecular architectures, as well as more complex patterns that these blocks may form, on a scale of hundreds and thousands of individual atomic/molecular units. Here we employ machine vision to read and recognize complex molecular assemblies on surfaces. Specifically, we combine Markov random field model and convolutional neural networks to classify structural and rotational states of all individual building blocks in molecular assembly on the metallic surface visualized in high-resolution scanning tunneling microscopy measurements. We show how the obtained full decoding of the system allows us to directly construct a pair density function—a centerpiece in analysis of disorder-property relationship paradigm—as well as to analyze spatial correlations between multiple order parameters at the nanoscale, and elucidate reaction pathway involving molecular conformation changes. The method represents a significant shift in our way of analyzing atomic and/or molecular resolved microscopic images and can be applied to variety of other microscopic measurements of structural, electronic, and magnetic orders in different condensed matter systems.

  5. Applications of AI, machine vision and robotics

    CERN Document Server

    Boyer, Kim; Bunke, H

    1995-01-01

    This text features a broad array of research efforts in computer vision including low level processing, perceptual organization, object recognition and active vision. The volume's nine papers specifically report on topics such as sensor confidence, low level feature extraction schemes, non-parametric multi-scale curve smoothing, integration of geometric and non-geometric attributes for object recognition, design criteria for a four degree-of-freedom robot head, a real-time vision system based on control of visual attention and a behavior-based active eye vision system. The scope of the book pr

  6. Standard machine vision systems used in different industrial applications

    Science.gov (United States)

    Bruehl, Wolfgang

    1993-12-01

    Fully standardized machine vision systems won't require task specific hard- or software development. This allows short project realization times at minimized cost. This paper describes two very different applications which were realized only by menu-guided configuration of the QueCheck standard machine vision system. The first is an in-line survey of oilpump castings necessary to protect the following working machine from being damaged by castings not according to the specified geometrical measures. The second application shows the replacement of time consuming manual particle size analysis of fertilizer pellets, by a continuous analysis with a vision system. At the same time the data of the vision system can be used to optimize particle size during production.

  7. 3D vision assisted flexible robotic assembly of machine components

    Science.gov (United States)

    Ogun, Philips S.; Usman, Zahid; Dharmaraj, Karthick; Jackson, Michael R.

    2015-12-01

    Robotic assembly systems either make use of expensive fixtures to hold components in predefined locations, or the poses of the components are determined using various machine vision techniques. Vision-guided assembly robots can handle subtle variations in geometries and poses of parts. Therefore, they provide greater flexibility than the use of fixtures. However, the currently established vision-guided assembly systems use 2D vision, which is limited to three degrees of freedom. The work reported in this paper is focused on flexible automated assembly of clearance fit machine components using 3D vision. The recognition and the estimation of the poses of the components are achieved by matching their CAD models with the acquired point cloud data of the scene. Experimental results obtained from a robot demonstrating the assembly of a set of rings on a shaft show that the developed system is not only reliable and accurate, but also fast enough for industrial deployment.

  8. Algorithms for image processing and computer vision

    CERN Document Server

    Parker, J R

    2010-01-01

    A cookbook of algorithms for common image processing applications Thanks to advances in computer hardware and software, algorithms have been developed that support sophisticated image processing without requiring an extensive background in mathematics. This bestselling book has been fully updated with the newest of these, including 2D vision methods in content-based searches and the use of graphics cards as image processing computational aids. It's an ideal reference for software engineers and developers, advanced programmers, graphics programmers, scientists, and other specialists wh

  9. Lane Detection Based on Machine Learning Algorithm

    National Research Council Canada - National Science Library

    Chao Fan; Jingbo Xu; Shuai Di

    2013-01-01

    In order to improve accuracy and robustness of the lane detection in complex conditions, such as the shadows and illumination changing, a novel detection algorithm was proposed based on machine learning...

  10. Trends and developments in industrial machine vision: 2013

    Science.gov (United States)

    Niel, Kurt; Heinzl, Christoph

    2014-03-01

    When following current advancements and implementations in the field of machine vision there seems to be no borders for future developments: Calculating power constantly increases, and new ideas are spreading and previously challenging approaches are introduced in to mass market. Within the past decades these advances have had dramatic impacts on our lives. Consumer electronics, e.g. computers or telephones, which once occupied large volumes, now fit in the palm of a hand. To note just a few examples e.g. face recognition was adopted by the consumer market, 3D capturing became cheap, due to the huge community SW-coding got easier using sophisticated development platforms. However, still there is a remaining gap between consumer and industrial applications. While the first ones have to be entertaining, the second have to be reliable. Recent studies (e.g. VDMA [1], Germany) show a moderately increasing market for machine vision in industry. Asking industry regarding their needs the main challenges for industrial machine vision are simple usage and reliability for the process, quick support, full automation, self/easy adjustment at changing process parameters, "forget it in the line". Furthermore a big challenge is to support quality control: Nowadays the operator has to accurately define the tested features for checking the probes. There is an upcoming development also to let automated machine vision applications find out essential parameters in a more abstract level (top down). In this work we focus on three current and future topics for industrial machine vision: Metrology supporting automation, quality control (inline/atline/offline) as well as visualization and analysis of datasets with steadily growing sizes. Finally the general trend of the pixel orientated towards object orientated evaluation is addressed. We do not directly address the field of robotics taking advances from machine vision. This is actually a fast changing area which is worth an own

  11. Direction Identification System of Garlic Clove Based on Machine Vision

    Directory of Open Access Journals (Sweden)

    Gao Chi

    2013-05-01

    Full Text Available In order to fulfill the requirements of seeding direction of garlic cloves, the paper proposed a research method of garlic clove direction identification based on machine vision, it expounded the theory of garlic clove direction identification, stated the arithmetic of it, designed the direction identification device of it, then developed the control system of garlic clove direction identification based on machine vision, at last tested the garlic clove direction identification, and the result of the experiment certificated that the rate of garlic clove direction identification could reach to more than 97%, and it demonstrated that the research is of high feasibility and technological values.

  12. Machine Vision For Industrial Control:The Unsung Opportunity

    Science.gov (United States)

    Falkman, Gerald A.; Murray, Lawrence A.; Cooper, James E.

    1984-05-01

    Vision modules have primarily been developed to relieve those pressures newly brought into existence by Inspection (QUALITY) and Robotic (PRODUCTIVITY) mandates. Industrial Control pressure stems on the other hand from the older first industrial revolution mandate of throughput. Satisfying such pressure calls for speed in both imaging and decision making. Vision companies have, however, put speed on a backburner or ignore it entirely because most modules are computer/software based which limits their speed potential. Increasingly, the keynote being struck at machine vision seminars is that "Visual and Computational Speed Must Be Increased and Dramatically!" There are modular hardwired-logic systems that are fast but, all too often, they are not very bright. Such units: Measure the fill factor of bottles as they spin by, Read labels on cans, Count stacked plastic cups or Monitor the width of parts streaming past the camera. Many are only a bit more complex than a photodetector. Once in place, most of these units are incapable of simple upgrading to a new task and are Vision's analog to the robot industry's pick and place (RIA TYPE E) robot. Vision thus finds itself amidst the same quandries that once beset the Robot Industry of America when it tried to define a robot, excluded dumb ones, and was left with only slow machines whose unit volume potential is shatteringly low. This paper develops an approach to meeting the need of a vision system that cuts a swath into the terra incognita of intelligent, high-speed vision processing. Main attention is directed to vision for industrial control. Some presently untapped vision application areas that will be serviced include: Electronics, Food, Sports, Pharmaceuticals, Machine Tools and Arc Welding.

  13. Building Artificial Vision Systems with Machine Learning

    Energy Technology Data Exchange (ETDEWEB)

    LeCun, Yann [New York University

    2011-02-23

    Three questions pose the next challenge for Artificial Intelligence (AI), robotics, and neuroscience. How do we learn perception (e.g. vision)? How do we learn representations of the perceptual world? How do we learn visual categories from just a few examples?

  14. Handbook of 3D machine vision optical metrology and imaging

    CERN Document Server

    Zhang, Song

    2013-01-01

    With the ongoing release of 3D movies and the emergence of 3D TVs, 3D imaging technologies have penetrated our daily lives. Yet choosing from the numerous 3D vision methods available can be frustrating for scientists and engineers, especially without a comprehensive resource to consult. Filling this gap, Handbook of 3D Machine Vision: Optical Metrology and Imaging gives an extensive, in-depth look at the most popular 3D imaging techniques. It focuses on noninvasive, noncontact optical methods (optical metrology and imaging). The handbook begins with the well-studied method of stereo vision and

  15. Linear Time Algorithms for Parallel Machine Scheduling

    Institute of Scientific and Technical Information of China (English)

    Zhi Yi TAN; Yong HE

    2007-01-01

    This paper addresses linear time algorithms for parallel machine scheduling problems. We introduce a kind of threshold algorithms and discuss their main features. Three linear time threshold algorithm classes DT, PT and DTm are studied thoroughly. For all classes, we study their best possible algorithms among each class. We also present their application to several scheduling problems.The new algorithms are better than classical algorithms in time complexity and/or worst-case ratio.Computer-aided proof technique is used in the proof of main results, which greatly simplifies the proof and decreases case by case analysis.

  16. A NEW HYPERSPHERE SUPPORT VECTOR MACHINE ALGORITHM

    Institute of Scientific and Technical Information of China (English)

    Zhang Xinfeng; Shen Lansun

    2006-01-01

    The hypersphere support vector machine is a new algorithm in pattern recognition. By studying three kinds ofhypersphere support vector machines, it is found that their solutions are identical and the margin between two classes of samples is zero or is not unique. In this letter, a new kind ofhypersphere support vector machine is proposed. By introducing a parameter n(n>l), a unique solution of the margin can be obtained.Theoretical analysis and experimental results show that the proposed algorithm can achieve better generalization performance.

  17. Close range photogrammetry and machine vision

    CERN Document Server

    Atkinson, KB

    1996-01-01

    This book presents the methodology, algorithms, techniques and equipment necessary to achieve real time digital photogrammetric solutions, together with contemporary examples of close range photogrammetry.

  18. Research and application of visual location technology for solder paste printing based on machine vision

    Institute of Scientific and Technical Information of China (English)

    Luosi WEI; Zongxia JIAO

    2009-01-01

    A location system is very important for solder paste printing in the process of surface mount technology (SMT). Using machine vision technology to complete the location mission is new and very efficient. This paper presents an integrated visual location system for solder paste printing based on machine vision. The working principle of solder paste printing is introduced and then the design and implementation of the visual location system are described. In the system, two key techniques are completed by secondary development based on VisionPro.One is accurate image location solved by the pattern-based location algorithms of PatMax. The other one is camera calibration that is achieved by image warping technology through the checkerboard plate. Moreover, the system can provide good performances such as high image locating accuracy with 1/40 sub-pixels, high anti-jamming, and high-speed location of objects whose appearance is rotated, scaled, and/or stretched.

  19. Parallelization of TMVA Machine Learning Algorithms

    CERN Document Server

    Hajili, Mammad

    2017-01-01

    This report reflects my work on Parallelization of TMVA Machine Learning Algorithms integrated to ROOT Data Analysis Framework during summer internship at CERN. The report consists of 4 impor- tant part - data set used in training and validation, algorithms that multiprocessing applied on them, parallelization techniques and re- sults of execution time changes due to number of workers.

  20. Machine vision for a selective broccoli harvesting robot

    NARCIS (Netherlands)

    Blok, Pieter M.; Barth, Ruud; Berg, Van Den Wim

    2016-01-01

    The selective hand-harvest of fresh market broccoli is labor-intensive and comprises about 35% of the total production costs. This research was conducted to determine whether machine vision can be used to detect broccoli heads, as a first step in the development of a fully autonomous selective harve

  1. Design and construction of automatic sorting station with machine vision

    Directory of Open Access Journals (Sweden)

    Oscar D. Velasco-Delgado

    2014-01-01

    Full Text Available This article presents the design, construction and testing of an automatic product sorting system in belt conveyor with machine vision that integrates Free and Open Source Software technology and Allen Bradley commercial equipment. Requirements are defined to determine features such as: mechanics of manufacturing station, an app of product sorting with machine vision and for automation system. For the app of machine vision a library is used for optical digital image processing Open CV, for the mechanical design of the manufacturing station is used the CAD tool Solid Edge and for the design and implementation of automation ISA standards are used along with an automation engineering project methodology integrating a PLC, an inverter, a Panel View and a DeviceNet Network. Performance tests are shown by classifying bottles and PVC pieces in four established types, the behavior of the integrated system is checked so as the efficiency of the same. The processing time on machine vision is 0.290 s on average for a piece of PVC, a capacity of 206 accessories per minute, for bottles was obtained a processing time of 0.267 s, a capacity of 224 bottles per minute. A maximum mechanical performance is obtained with 32 products per minute (1920 products/hour with the conveyor to 22 cm/s and 40 cm of distance between products obtaining an average error of 0.8%.

  2. Machine-Vision Systems Selection for Agricultural Vehicles: A Guide

    Directory of Open Access Journals (Sweden)

    Gonzalo Pajares

    2016-11-01

    Full Text Available Machine vision systems are becoming increasingly common onboard agricultural vehicles (autonomous and non-autonomous for different tasks. This paper provides guidelines for selecting machine-vision systems for optimum performance, considering the adverse conditions on these outdoor environments with high variability on the illumination, irregular terrain conditions or different plant growth states, among others. In this regard, three main topics have been conveniently addressed for the best selection: (a spectral bands (visible and infrared; (b imaging sensors and optical systems (including intrinsic parameters and (c geometric visual system arrangement (considering extrinsic parameters and stereovision systems. A general overview, with detailed description and technical support, is provided for each topic with illustrative examples focused on specific applications in agriculture, although they could be applied in different contexts other than agricultural. A case study is provided as a result of research in the RHEA (Robot Fleets for Highly Effective Agriculture and Forestry Management project for effective weed control in maize fields (wide-rows crops, funded by the European Union, where the machine vision system onboard the autonomous vehicles was the most important part of the full perception system, where machine vision was the most relevant. Details and results about crop row detection, weed patches identification, autonomous vehicle guidance and obstacle detection are provided together with a review of methods and approaches on these topics.

  3. Measurement of seedling growth rate by machine vision

    Science.gov (United States)

    Howarth, M. Scott; Stanwood, Phillip C.

    1993-05-01

    Seed vigor and germination tests have traditionally been used to determine deterioration of seed samples. Vigor tests describe the seed potential to emerge and produce a mature crop under certain field conditions and one measure is seedling growth rate. A machine vision system was developed to measure root growth rate over the entire germination period. The machine vision measurement technique was compared to the manual growth rate technique. The vision system provided similar growth rate measurements as compared to the manual growth rate technique. The average error between the system and a manual measurement was -0.13 for the lettuce test and -0.07 for the sorghum test. This technique also provided an accurate representation of the growth rate as well as percent germination.

  4. Enhanced Machine Vision System for Ripe Fruit Detection Based on Robotic Harvesting

    Directory of Open Access Journals (Sweden)

    R. Thendral

    2015-04-01

    Full Text Available The proposed work intends to provide an efficient algorithm for the instruction of an automatic robot arm to choose the ripe fruits on the tree. Steps involved in this study are recognizing and locating the ripe fruits from the leaf and branch portions by using an efficient machine vision algorithm. Initially, discrete wavelet transform is used for better preserving of edges and fine details in the given input image. Then RGB, HSV, L*a*b* and YIQ color spaces were studied to segment the ripe fruits from the surrounding objects. Finally, the results showed that ‘I’ component of the YIQ color space has the best criterion for recognizing the fruit from the foliage. The fruit segmentation based on machine vision has an occlusion problem. In this proposed method these problems are also examined.

  5. Using an FPGA-Based Processing Platform in an Industrial Machine Vision System

    OpenAIRE

    King, William E

    1998-01-01

    This thesis describes the development of a commercial machine vision system as a case study for utilizing the Modular Reprogrammable Real-time Processing Hardware (MORRPH) board. The commercial system described in this thesis is based on a prototype system that was developed as a test-bed for developing the necessary concepts and algorithms. The prototype system utilized color linescan cameras, custom framegrabbers, and standard PCs to color-sort red oak parts (staves). When a furniture ma...

  6. Stereoscopic Machine-Vision System Using Projected Circles

    Science.gov (United States)

    Mackey, Jeffrey R.

    2010-01-01

    A machine-vision system capable of detecting obstacles large enough to damage or trap a robotic vehicle is undergoing development. The system includes (1) a pattern generator that projects concentric circles of laser light forward onto the terrain, (2) a stereoscopic pair of cameras that are aimed forward to acquire images of the circles, (3) a frame grabber and digitizer for acquiring image data from the cameras, and (4) a single-board computer that processes the data. The system is being developed as a prototype of machine- vision systems to enable robotic vehicles ( rovers ) on remote planets to avoid craters, large rocks, and other terrain features that could capture or damage the vehicles. Potential terrestrial applications of systems like this one could include terrain mapping, collision avoidance, navigation of robotic vehicles, mining, and robotic rescue. This system is based partly on the same principles as those of a prior stereoscopic machine-vision system in which the cameras acquire images of a single stripe of laser light that is swept forward across the terrain. However, this system is designed to afford improvements over some of the undesirable features of the prior system, including the need for a pan-and-tilt mechanism to aim the laser to generate the swept stripe, ambiguities in interpretation of the single-stripe image, the time needed to sweep the stripe across the terrain and process the data from many images acquired during that time, and difficulty of calibration because of the narrowness of the stripe. In this system, the pattern generator does not contain any moving parts and need not be mounted on a pan-and-tilt mechanism: the pattern of concentric circles is projected steadily in the forward direction. The system calibrates itself by use of data acquired during projection of the concentric-circle pattern onto a known target representing flat ground. The calibration- target image data are stored in the computer memory for use as a

  7. SAD-based stereo vision machine on a System-on-Programmable-Chip (SoPC).

    Science.gov (United States)

    Zhang, Xiang; Chen, Zhangwei

    2013-03-04

    This paper, proposes a novel solution for a stereo vision machine based on the System-on-Programmable-Chip (SoPC) architecture. The SOPC technology provides great convenience for accessing many hardware devices such as DDRII, SSRAM, Flash, etc., by IP reuse. The system hardware is implemented in a single FPGA chip involving a 32-bit Nios II microprocessor, which is a configurable soft IP core in charge of managing the image buffer and users' configuration data. The Sum of Absolute Differences (SAD) algorithm is used for dense disparity map computation. The circuits of the algorithmic module are modeled by the Matlab-based DSP Builder. With a set of configuration interfaces, the machine can process many different sizes of stereo pair images. The maximum image size is up to 512 K pixels. This machine is designed to focus on real time stereo vision applications. The stereo vision machine offers good performance and high efficiency in real time. Considering a hardware FPGA clock of 90 MHz, 23 frames of 640 × 480 disparity maps can be obtained in one second with 5 × 5 matching window and maximum 64 disparity pixels.

  8. SAD-Based Stereo Vision Machine on a System-on-Programmable-Chip (SoPC

    Directory of Open Access Journals (Sweden)

    Zhangwei Chen

    2013-03-01

    Full Text Available This paper, proposes a novel solution for a stereo vision machine based on the System-on-Programmable-Chip (SoPC architecture. The SOPC technology provides great convenience for accessing many hardware devices such as DDRII, SSRAM, Flash, etc., by IP reuse. The system hardware is implemented in a single FPGA chip involving a 32-bit Nios II microprocessor, which is a configurable soft IP core in charge of managing the image buffer and users’ configuration data. The Sum of Absolute Differences (SAD algorithm is used for dense disparity map computation. The circuits of the algorithmic module are modeled by the Matlab-based DSP Builder. With a set of configuration interfaces, the machine can process many different sizes of stereo pair images. The maximum image size is up to 512 K pixels. This machine is designed to focus on real time stereo vision applications. The stereo vision machine offers good performance and high efficiency in real time. Considering a hardware FPGA clock of 90 MHz, 23 frames of 640 × 480 disparity maps can be obtained in one second with 5 × 5 matching window and maximum 64 disparity pixels.

  9. Paradigms for Realizing Machine Learning Algorithms.

    Science.gov (United States)

    Agneeswaran, Vijay Srinivas; Tonpay, Pranay; Tiwary, Jayati

    2013-12-01

    The article explains the three generations of machine learning algorithms-with all three trying to operate on big data. The first generation tools are SAS, SPSS, etc., while second generation realizations include Mahout and RapidMiner (that work over Hadoop), and the third generation paradigms include Spark and GraphLab, among others. The essence of the article is that for a number of machine learning algorithms, it is important to look beyond the Hadoop's Map-Reduce paradigm in order to make them work on big data. A number of promising contenders have emerged in the third generation that can be exploited to realize deep analytics on big data.

  10. Principles of image processing in machine vision systems for the color analysis of minerals

    Science.gov (United States)

    Petukhova, Daria B.; Gorbunova, Elena V.; Chertov, Aleksandr N.; Korotaev, Valery V.

    2014-09-01

    At the moment color sorting method is one of promising methods of mineral raw materials enrichment. This method is based on registration of color differences between images of analyzed objects. As is generally known the problem with delimitation of close color tints when sorting low-contrast minerals is one of the main disadvantages of color sorting method. It is can be related with wrong choice of a color model and incomplete image processing in machine vision system for realizing color sorting algorithm. Another problem is a necessity of image processing features reconfiguration when changing the type of analyzed minerals. This is due to the fact that optical properties of mineral samples vary from one mineral deposit to another. Therefore searching for values of image processing features is non-trivial task. And this task doesn't always have an acceptable solution. In addition there are no uniform guidelines for determining criteria of mineral samples separation. It is assumed that the process of image processing features reconfiguration had to be made by machine learning. But in practice it's carried out by adjusting the operating parameters which are satisfactory for one specific enrichment task. This approach usually leads to the fact that machine vision system unable to estimate rapidly the concentration rate of analyzed mineral ore by using color sorting method. This paper presents the results of research aimed at addressing mentioned shortcomings in image processing organization for machine vision systems which are used to color sorting of mineral samples. The principles of color analysis for low-contrast minerals by using machine vision systems are also studied. In addition, a special processing algorithm for color images of mineral samples is developed. Mentioned algorithm allows you to determine automatically the criteria of mineral samples separation based on an analysis of representative mineral samples. Experimental studies of the proposed algorithm

  11. Two dimensional convolute integers for machine vision and image recognition

    Science.gov (United States)

    Edwards, Thomas R.

    1988-01-01

    Machine vision and image recognition require sophisticated image processing prior to the application of Artificial Intelligence. Two Dimensional Convolute Integer Technology is an innovative mathematical approach for addressing machine vision and image recognition. This new technology generates a family of digital operators for addressing optical images and related two dimensional data sets. The operators are regression generated, integer valued, zero phase shifting, convoluting, frequency sensitive, two dimensional low pass, high pass and band pass filters that are mathematically equivalent to surface fitted partial derivatives. These operators are applied non-recursively either as classical convolutions (replacement point values), interstitial point generators (bandwidth broadening or resolution enhancement), or as missing value calculators (compensation for dead array element values). These operators show frequency sensitive feature selection scale invariant properties. Such tasks as boundary/edge enhancement and noise or small size pixel disturbance removal can readily be accomplished. For feature selection tight band pass operators are essential. Results from test cases are given.

  12. Collection of Group Characteristics of Pleurotus Eryngii Using Machine Vision

    Science.gov (United States)

    Wang, Yunsheng; Wan, Changzhao; Yang, Juan; Chen, Jianlin; Yuan, Tao; Zhao, Jingyin

    An information collection system which was used to group characteristics of pleurotus eryngii was introduced. The group characteristics of pleurotus eryngii were quantified using machine vision in order to inspect and control the pleurotus eryngii house environment by an automated system. Its main contents include the following: collection of pleurotus eryngii image; image processing and pattern recognition. Finally, by analysing pleurotus eryngii image, the systems for group characteristics of pleurotus eryngii are proved to be greatly effective.

  13. Machine vision automated visual inspection theory, practice and applications

    CERN Document Server

    Beyerer, Jürgen; Frese, Christian

    2016-01-01

    The book offers a thorough introduction to machine vision. It is organized in two parts. The first part covers the image acquisition, which is the crucial component of most automated visual inspection systems. All important methods are described in great detail and are presented with a reasoned structure. The second part deals with the modeling and processing of image signals and pays particular regard to methods, which are relevant for automated visual inspection.

  14. Potato Size and Shape Detection Using Machine Vision

    Directory of Open Access Journals (Sweden)

    Liao Guiping

    2015-01-01

    Full Text Available To reduce the error and faster classification by mechanizing in classifying the potato shape and size through machine vision using the extraction of characters procedure to identify the size, and using the shape detection procedure to identify the shape. Test results in potato size detection revealed 40/191 = 0.210mm/pixel as length scale or calibration factor (40/M where 40 is the table tennis ball size (40mm and 191 as image pixels table tennis (M; measurement results revealed that between the algorithm results and the manual measurements, the absolute error was <3mm, while the relative error rate was <4%; and the measurement results based on the ellipse axis length can accurately calculate the actual long axis and short axis of potato. Potato shape detection revealed the analysis of 228 images composed of 114 positive and 114 negatives side, only 2 have been incorrectly classified, mainly because the Extracted ratio (R of the potato image of those two positive and negative images are near 0.67, respectively 0.671887, 0.661063, 0.667604, and 0.67193. The comparison to establish a calibration system method using both basic rectangle and ellipse R ratio methods to detect the potato size and shape, revealed that the basic rectangle method has better effect in the case of fixed place. Moreover, the ellipse axis method was observed to be more stable with an error rate of 7%. Therefore it is recommended that the ellipse axis method should be used to detect the shape of potato for differentiation into round, long cylindrical, and oval shapes, with the accuracy level of 98.8%.

  15. Software architecture for time-constrained machine vision applications

    Science.gov (United States)

    Usamentiaga, Rubén; Molleda, Julio; García, Daniel F.; Bulnes, Francisco G.

    2013-01-01

    Real-time image and video processing applications require skilled architects, and recent trends in the hardware platform make the design and implementation of these applications increasingly complex. Many frameworks and libraries have been proposed or commercialized to simplify the design and tuning of real-time image processing applications. However, they tend to lack flexibility, because they are normally oriented toward particular types of applications, or they impose specific data processing models such as the pipeline. Other issues include large memory footprints, difficulty for reuse, and inefficient execution on multicore processors. We present a novel software architecture for time-constrained machine vision applications that addresses these issues. The architecture is divided into three layers. The platform abstraction layer provides a high-level application programming interface for the rest of the architecture. The messaging layer provides a message-passing interface based on a dynamic publish/subscribe pattern. A topic-based filtering in which messages are published to topics is used to route the messages from the publishers to the subscribers interested in a particular type of message. The application layer provides a repository for reusable application modules designed for machine vision applications. These modules, which include acquisition, visualization, communication, user interface, and data processing, take advantage of the power of well-known libraries such as OpenCV, Intel IPP, or CUDA. Finally, the proposed architecture is applied to a real machine vision application: a jam detector for steel pickling lines.

  16. Machine Vision-Based Measurement Systems for Fruit and Vegetable Quality Control in Postharvest.

    Science.gov (United States)

    Blasco, José; Munera, Sandra; Aleixos, Nuria; Cubero, Sergio; Molto, Enrique

    2017-03-14

    Individual items of any agricultural commodity are different from each other in terms of colour, shape or size. Furthermore, as they are living thing, they change their quality attributes over time, thereby making the development of accurate automatic inspection machines a challenging task. Machine vision-based systems and new optical technologies make it feasible to create non-destructive control and monitoring tools for quality assessment to ensure adequate accomplishment of food standards. Such systems are much faster than any manual non-destructive examination of fruit and vegetable quality, thus allowing the whole production to be inspected with objective and repeatable criteria. Moreover, current technology makes it possible to inspect the fruit in spectral ranges beyond the sensibility of the human eye, for instance in the ultraviolet and near-infrared regions. Machine vision-based applications require the use of multiple technologies and knowledge, ranging from those related to image acquisition (illumination, cameras, etc.) to the development of algorithms for spectral image analysis. Machine vision-based systems for inspecting fruit and vegetables are targeted towards different purposes, from in-line sorting into commercial categories to the detection of contaminants or the distribution of specific chemical compounds on the product's surface. This chapter summarises the current state of the art in these techniques, starting with systems based on colour images for the inspection of conventional colour, shape or external defects and then goes on to consider recent developments in spectral image analysis for internal quality assessment or contaminant detection.

  17. Practical guide to machine vision software an introduction with LabVIEW

    CERN Document Server

    Kwon, Kye-Si

    2014-01-01

    For both students and engineers in R&D, this book explains machine vision in a concise, hands-on way, using the Vision Development Module of the LabView software by National Instruments. Following a short introduction to the basics of machine vision and the technical procedures of image acquisition, the book goes on to guide readers in the use of the various software functions of LabView's machine vision module. It covers typical machine vision tasks, including particle analysis, edge detection, pattern and shape matching, dimension measurements as well as optical character recognition, enabli

  18. Machine Vision Applied to Navigation of Confined Spaces

    Science.gov (United States)

    Briscoe, Jeri M.; Broderick, David J.; Howard, Ricky; Corder, Eric L.

    2004-01-01

    The reliability of space related assets has been emphasized after the second loss of a Space Shuttle. The intricate nature of the hardware being inspected often requires a complete disassembly to perform a thorough inspection which can be difficult as well as costly. Furthermore, it is imperative that the hardware under inspection not be altered in any other manner than that which is intended. In these cases the use of machine vision can allow for inspection with greater frequency using less intrusive methods. Such systems can provide feedback to guide, not only manually controlled instrumentation, but autonomous robotic platforms as well. This paper serves to detail a method using machine vision to provide such sensing capabilities in a compact package. A single camera is used in conjunction with a projected reference grid to ascertain precise distance measurements. The design of the sensor focuses on the use of conventional components in an unconventional manner with the goal of providing a solution for systems that do not require or cannot accommodate more complex vision systems.

  19. Machine learning, computer vision, and probabilistic models in jet physics

    CERN Document Server

    CERN. Geneva; NACHMAN, Ben

    2015-01-01

    In this talk we present recent developments in the application of machine learning, computer vision, and probabilistic models to the analysis and interpretation of LHC events. First, we will introduce the concept of jet-images and computer vision techniques for jet tagging. Jet images enabled the connection between jet substructure and tagging with the fields of computer vision and image processing for the first time, improving the performance to identify highly boosted W bosons with respect to state-of-the-art methods, and providing a new way to visualize the discriminant features of different classes of jets, adding a new capability to understand the physics within jets and to design more powerful jet tagging methods. Second, we will present Fuzzy jets: a new paradigm for jet clustering using machine learning methods. Fuzzy jets view jet clustering as an unsupervised learning task and incorporate a probabilistic assignment of particles to jets to learn new features of the jet structure. In particular, we wi...

  20. Ozone ensemble forecast with machine learning algorithms

    OpenAIRE

    Mallet, Vivien; Stoltz, Gilles; Mauricette, Boris

    2009-01-01

    International audience; We apply machine learning algorithms to perform sequential aggregation of ozone forecasts. The latter rely on a multimodel ensemble built for ozone forecasting with the modeling system Polyphemus. The ensemble simulations are obtained by changes in the physical parameterizations, the numerical schemes, and the input data to the models. The simulations are carried out for summer 2001 over western Europe in order to forecast ozone daily peaks and ozone hourly concentrati...

  1. Machine Learning and Computer Vision System for Phenotype Data Acquisition and Analysis in Plants

    Directory of Open Access Journals (Sweden)

    Pedro J. Navarro

    2016-05-01

    Full Text Available Phenomics is a technology-driven approach with promising future to obtain unbiased data of biological systems. Image acquisition is relatively simple. However data handling and analysis are not as developed compared to the sampling capacities. We present a system based on machine learning (ML algorithms and computer vision intended to solve the automatic phenotype data analysis in plant material. We developed a growth-chamber able to accommodate species of various sizes. Night image acquisition requires near infrared lightning. For the ML process, we tested three different algorithms: k-nearest neighbour (kNN, Naive Bayes Classifier (NBC, and Support Vector Machine. Each ML algorithm was executed with different kernel functions and they were trained with raw data and two types of data normalisation. Different metrics were computed to determine the optimal configuration of the machine learning algorithms. We obtained a performance of 99.31% in kNN for RGB images and a 99.34% in SVM for NIR. Our results show that ML techniques can speed up phenomic data analysis. Furthermore, both RGB and NIR images can be segmented successfully but may require different ML algorithms for segmentation.

  2. Machine Learning and Computer Vision System for Phenotype Data Acquisition and Analysis in Plants.

    Science.gov (United States)

    Navarro, Pedro J; Pérez, Fernando; Weiss, Julia; Egea-Cortines, Marcos

    2016-05-05

    Phenomics is a technology-driven approach with promising future to obtain unbiased data of biological systems. Image acquisition is relatively simple. However data handling and analysis are not as developed compared to the sampling capacities. We present a system based on machine learning (ML) algorithms and computer vision intended to solve the automatic phenotype data analysis in plant material. We developed a growth-chamber able to accommodate species of various sizes. Night image acquisition requires near infrared lightning. For the ML process, we tested three different algorithms: k-nearest neighbour (kNN), Naive Bayes Classifier (NBC), and Support Vector Machine. Each ML algorithm was executed with different kernel functions and they were trained with raw data and two types of data normalisation. Different metrics were computed to determine the optimal configuration of the machine learning algorithms. We obtained a performance of 99.31% in kNN for RGB images and a 99.34% in SVM for NIR. Our results show that ML techniques can speed up phenomic data analysis. Furthermore, both RGB and NIR images can be segmented successfully but may require different ML algorithms for segmentation.

  3. On the performances of computer vision algorithms on mobile platforms

    Science.gov (United States)

    Battiato, S.; Farinella, G. M.; Messina, E.; Puglisi, G.; Ravì, D.; Capra, A.; Tomaselli, V.

    2012-01-01

    Computer Vision enables mobile devices to extract the meaning of the observed scene from the information acquired with the onboard sensor cameras. Nowadays, there is a growing interest in Computer Vision algorithms able to work on mobile platform (e.g., phone camera, point-and-shot-camera, etc.). Indeed, bringing Computer Vision capabilities on mobile devices open new opportunities in different application contexts. The implementation of vision algorithms on mobile devices is still a challenging task since these devices have poor image sensors and optics as well as limited processing power. In this paper we have considered different algorithms covering classic Computer Vision tasks: keypoint extraction, face detection, image segmentation. Several tests have been done to compare the performances of the involved mobile platforms: Nokia N900, LG Optimus One, Samsung Galaxy SII.

  4. Intelligent Machine Vision Based Modeling and Positioning System in Sand Casting Process

    Directory of Open Access Journals (Sweden)

    Shahid Ikramullah Butt

    2017-01-01

    Full Text Available Advanced vision solutions enable manufacturers in the technology sector to reconcile both competitive and regulatory concerns and address the need for immaculate fault detection and quality assurance. The modern manufacturing has completely shifted from the manual inspections to the machine assisted vision inspection methodology. Furthermore, the research outcomes in industrial automation have revolutionized the whole product development strategy. The purpose of this research paper is to introduce a new scheme of automation in the sand casting process by means of machine vision based technology for mold positioning. Automation has been achieved by developing a novel system in which casting molds of different sizes, having different pouring cup location and radius, position themselves in front of the induction furnace such that the center of pouring cup comes directly beneath the pouring point of furnace. The coordinates of the center of pouring cup are found by using computer vision algorithms. The output is then transferred to a microcontroller which controls the alignment mechanism on which the mold is placed at the optimum location.

  5. Multimedia extensions to prototyping software for machine vision

    Science.gov (United States)

    Batchelor, Bruce G.; Griffiths, Eric C.; Hack, Ralf; Jones, Andrew C.

    1996-10-01

    PIP (prolog image processing) is a prototyping tool, intended to assists designers of intelligent industrial machine vision systems. This article concentrates on the multi-media extensions to PIP, including: 1) on-line HELP, which allows the user to satisfy PIP goals from within the HELP facility, 2) lighting advisor, which gives advice to a vision engineer about which lighting/viewing arrangement is appropriate to use in a given situation, 3) device control, for operating a robot work cell, 4) speech input and (simple) natural language understanding, 5) speech synthesis, 6) remote operation of PIP via a local area network, and 7) remote operation of PIP via a local area network. At the time of writing, on-line access to PIP, via the Internet, is being developed.

  6. 基于流形学习算法的马铃薯机械损伤机器视觉检测方法%Machine vision detecting potato mechanical damage based on manifold learning algorithm

    Institute of Scientific and Technical Information of China (English)

    汪成龙; 李小昱; 武振中; 周竹; 冯耀泽

    2014-01-01

    Buds and uneven surface of potatoes have caused problems to detect the mechanical damage based on machine vision. The lighting conditions and gray value changes of defect region have great impacts on the pixel level feature extraction. While manifold learning methods have been extensively studied in the face recognition, they have not been used for the external quality inspection of agricultural products. The manifold learning method is mainly divided into linear and nonlinear manifold learning algorithms. The nonlinear manifold learning algorithm includes isometric mapping (Isomap), locally linear embedding (LLE), laplacian eigenmaping (LE). The linear algorithm is extension of the nonlinear methods such as principal component analysis (PCA) and multidimensional scaling (MDS). In order to weaken the influence of the buds and uneven surface on potatoes mechanical damage detection, the image was characterized by using low dimensional manifolds. A mechanical damage detection method for potatoes was provided based on manifold learning. In this study, the Saliency and H images were firstly segmented on the potato regional image. The segmentation accuracies of both images are 100%. However, Saliency-H method can the potato’s location information of the image by unsupervised pattern was automatically obtained. In addition, Saliency-H method was faster (average elapsed time is 477.7ms) than H method with a high data compression rate. After the potato region images were resampled from 1024×768 to 64×64, the features of potato images were extracted from the resample images by using the three manifold learning methods: principal component analysis (PCA), isometric mapping (Isomap) and locally linear embedding (LLE). Thirdly, the three corresponding SVM classification models were developed based on their features. Finally the parameters of the models were optimized to develop corresponding optimal classification models by using the grid search method (grid search

  7. A machine vision approach to seam tracking in real-time in PAW of large-diameter stainless steel tube

    Institute of Scientific and Technical Information of China (English)

    葛景国; 朱政强; 何德孚; 陈立功

    2004-01-01

    Manual monitoring and seam tracking through watching weld pool images in real-time, by naked eyes or by industrial TV, are experience-depended, subjective, labor intensive, and sometimes biased. So it is necessary to realize the automation of computer-aided seam tracking. A PAW (plasma arc welding) seam tracking system was developed, which senses the molten pool and the seam in one frame by a vision sensor, and then detects the seam deviation to adjust the work piece motion adaptively to the seam position sensed by vision sensor. A novel molten pool area image-processing algorithm based on machine vision was proposed. The algorithm processes each image at the speed of 20 frames/second in real-time to extract three feature variables to get the seam deviation. It is proved experimentally that the algorithm is very fast and effective. Issues related to the algorithm are also discussed.

  8. Machine vision system for automated detection of stained pistachio nuts

    Science.gov (United States)

    Pearson, Tom C.

    1995-01-01

    A machine vision system was developed to separate stained pistachio nuts, which comprise of about 5% of the California crop, from unstained nuts. The system may be used to reduce labor involved with manual grading or to remove aflatoxin contaminated product from low grade process streams. The system was tested on two different pistachio process streams: the bi- chromatic color sorter reject stream and the small nut shelling stock stream. The system had a minimum overall error rate of 14% for the bi-chromatic sorter reject stream and 15% for the small shelling stock stream.

  9. Development of machine vision system for PHWR fuel pellet inspection

    Energy Technology Data Exchange (ETDEWEB)

    Kamalesh Kumar, B.; Reddy, K.S.; Lakshminarayana, A.; Sastry, V.S.; Ramana Rao, A.V. [Nuclear Fuel Complex, Hyderabad, Andhra Pradesh (India); Joshi, M.; Deshpande, P.; Navathe, C.P.; Jayaraj, R.N. [Raja Ramanna Centre for Advanced Technology, Indore, Madhya Pradesh (India)

    2008-07-01

    Nuclear Fuel Complex, a constituent of Department of Atomic Energy; India is responsible for manufacturing nuclear fuel in India . Over a million Uranium-di-oxide pellets fabricated per annum need visual inspection . In order to overcome the limitations of human based visual inspection, NFC has undertaken the development of machine vision system. The development involved designing various subsystems viz. mechanical and control subsystem for handling and rotation of fuel pellets, lighting subsystem for illumination, image acquisition system, and image processing system and integration. This paper brings out details of various subsystems and results obtained from the trials conducted. (author)

  10. Lane Detection Based on Machine Learning Algorithm

    Directory of Open Access Journals (Sweden)

    Chao Fan

    2013-09-01

    Full Text Available In order to improve accuracy and robustness of the lane detection in complex conditions, such as the shadows and illumination changing, a novel detection algorithm was proposed based on machine learning. After pretreatment, a set of haar-like filters were used to calculate the eigenvalue in the gray image f(x,y and edge e(x,y. Then these features were trained by using improved boosting algorithm and the final class function g(x was obtained, which was used to judge whether the point x belonging to the lane or not. To avoid the over fitting in traditional boosting, Fisher discriminant analysis was used to initialize the weights of samples. After testing by many road in all conditions, it showed that this algorithm had good robustness and real-time to recognize the lane in all challenging conditions.

  11. Potential application of machine vision technology to saffron (Crocus sativus L.) quality characterization.

    Science.gov (United States)

    Kiani, Sajad; Minaei, Saeid

    2016-12-01

    Saffron quality characterization is an important issue in the food industry and of interest to the consumers. This paper proposes an expert system based on the application of machine vision technology for characterization of saffron and shows how it can be employed in practical usage. There is a correlation between saffron color and its geographic location of production and some chemical attributes which could be properly used for characterization of saffron quality and freshness. This may be accomplished by employing image processing techniques coupled with multivariate data analysis for quantification of saffron properties. Expert algorithms can be made available for prediction of saffron characteristics such as color as well as for product classification.

  12. Technique for Calibration of Chassis components based on encoding marks and machine Vision metrology

    Institute of Scientific and Technical Information of China (English)

    SONG Li-mei; ZHANG Chun-bo; WEI Yi-ying; CHEN Hua-wei

    2011-01-01

    @@ A novel technique for calibrating crucial parameters of chassis components is proposed, which utilizes the machine vision metrology to measure 3D coordinates of the center of a component's hole for assembling in the 3D world coordinate system.In the measurement, encoding marks with special patterns will be assembled on the chassis component associated with cross drone and staff gauge located near the chassis.The geometry and coordinates of the cross drone consist of two planes orthogonal to each other and the staff gauge is in 3D space with high precision.A few images are taken by a highresolution camera in different orientations and perspectives.The 3D coordinates of 5 key points on the encoding marks will be calculated by the machine vision technique and those of the center of the holes to be calibrated will be calculated by the deduced algorithm in this paper.Experimental results show that the algorithm and the technique can satisfy the precision requirement when the components are assembled, and the average measurement precision provided by the algorithm is 0.0174 mm.

  13. Accurate measurement method for tube's endpoints based on machine vision

    Science.gov (United States)

    Liu, Shaoli; Jin, Peng; Liu, Jianhua; Wang, Xiao; Sun, Peng

    2017-01-01

    Tubes are used widely in aerospace vehicles, and their accurate assembly can directly affect the assembling reliability and the quality of products. It is important to measure the processed tube's endpoints and then fix any geometric errors correspondingly. However, the traditional tube inspection method is time-consuming and complex operations. Therefore, a new measurement method for a tube's endpoints based on machine vision is proposed. First, reflected light on tube's surface can be removed by using photometric linearization. Then, based on the optimization model for the tube's endpoint measurements and the principle of stereo matching, the global coordinates and the relative distance of the tube's endpoint are obtained. To confirm the feasibility, 11 tubes are processed to remove the reflected light and then the endpoint's positions of tubes are measured. The experiment results show that the measurement repeatability accuracy is 0.167 mm, and the absolute accuracy is 0.328 mm. The measurement takes less than 1 min. The proposed method based on machine vision can measure the tube's endpoints without any surface treatment or any tools and can realize on line measurement.

  14. Accurate Measurement Method for Tube's Endpoints Based on Machine Vision

    Institute of Scientific and Technical Information of China (English)

    LIU Shaoli; JIN Peng; LIU Jianhua; WANG Xiao; SUN Peng

    2017-01-01

    Tubes are used widely in aerospace vehicles,and their accurate assembly can directly affect the assembling reliability and the quality of products.It is important to measure the processed tube's endpoints and then fix any geometric errors correspondingly.However,the traditional tube inspection method is time-consuming and complex operations.Therefore,a new measurement method for a tube's endpoints based on machine vision is proposed.First,reflected light on tube's surface can be removed by using photometric linearization.Then,based on the optimization model for the tube's endpoint measurements and the principle of stereo matching,the global coordinates and the relative distance of the tube's endpoint are obtained.To confirm the feasibility,11 tubes are processed to remove the reflected light and then the endpoint's positions of tubes are measured.The experiment results show that the measurement repeatability accuracy is 0.167 mm,and the absolute accuracy is 0.328 mm.The measurement takes less than 1 min.The proposed method based on machine vision can measure the tube's endpoints without any surface treatment or any tools and can realize on line measurement.

  15. Accurate measurement method for tube's endpoints based on machine vision

    Science.gov (United States)

    Liu, Shaoli; Jin, Peng; Liu, Jianhua; Wang, Xiao; Sun, Peng

    2016-08-01

    Tubes are used widely in aerospace vehicles, and their accurate assembly can directly affect the assembling reliability and the quality of products. It is important to measure the processed tube's endpoints and then fix any geometric errors correspondingly. However, the traditional tube inspection method is time-consuming and complex operations. Therefore, a new measurement method for a tube's endpoints based on machine vision is proposed. First, reflected light on tube's surface can be removed by using photometric linearization. Then, based on the optimization model for the tube's endpoint measurements and the principle of stereo matching, the global coordinates and the relative distance of the tube's endpoint are obtained. To confirm the feasibility, 11 tubes are processed to remove the reflected light and then the endpoint's positions of tubes are measured. The experiment results show that the measurement repeatability accuracy is 0.167 mm, and the absolute accuracy is 0.328 mm. The measurement takes less than 1 min. The proposed method based on machine vision can measure the tube's endpoints without any surface treatment or any tools and can realize on line measurement.

  16. Development of an automatic weld surface appearance inspection system using machine vision

    Institute of Scientific and Technical Information of China (English)

    Lin Sanbao; Fu Xibin; Fan Chenglei; Yang Chunli; Luo Lu

    2009-01-01

    In this paper, an automatic inspection system for weld surface appearance using machine vision has been developed to recognize weld surface defects such as porosities, cracks, etc. It can replace conventional manual visual inspection method, which is tedious, time-consuming, subjective, experience-depended, and sometimes biased. The system consists of a CCD camera, a self-designed annular light source, a sensor controller, a frame grabbing card, a computer and so on. After acquiring weld surface appearance images using CCD, the images are preprocessed using median filtering and a series of image enhancement algorithms. Then a dynamic threshold and morphology algorithms are applied to segment defect object. Finally, defect features information is obtained by eight neighborhoods boundary chain code algorithm. Experimental results show that the developed system is capable of inspecting most surface defects such as porosities, cracks with high reliability and accuracy.

  17. Online algorithms for scheduling with machine activation cost on two uniform machines

    Institute of Scientific and Technical Information of China (English)

    HAN Shu-guang; JIANG Yi-wei; HU Jue-liang

    2007-01-01

    In this paper we investigate a variant of the scheduling problem on two uniform machines with speeds 1 and s. For this problem, we are given two potential uniform machines to process a sequence of independent jobs. Machines need to be activated before starting to process, and each machine activated incurs a fixed machine activation cost. No machines are initially activated,and when a job is revealed, the algorithm has the option to activate new machines. The objective is to minimize the sum of the makespan and the machine activation cost. We design optimal online algorithms with competitive ratio of (2s+1)/(s+1) for every s≥1.

  18. Optimization of machining processes using pattern search algorithm

    Directory of Open Access Journals (Sweden)

    Miloš Madić

    2014-04-01

    Full Text Available Optimization of machining processes not only increases machining efficiency and economics, but also the end product quality. In recent years, among the traditional optimization methods, stochastic direct search optimization methods such as meta-heuristic algorithms are being increasingly applied for solving machining optimization problems. Their ability to deal with complex, multi-dimensional and ill-behaved optimization problems made them the preferred optimization tool by most researchers and practitioners. This paper introduces the use of pattern search (PS algorithm, as a deterministic direct search optimization method, for solving machining optimization problems. To analyze the applicability and performance of the PS algorithm, six case studies of machining optimization problems, both single and multi-objective, were considered. The PS algorithm was employed to determine optimal combinations of machining parameters for different machining processes such as abrasive waterjet machining, turning, turn-milling, drilling, electrical discharge machining and wire electrical discharge machining. In each case study the optimization solutions obtained by the PS algorithm were compared with the optimization solutions that had been determined by past researchers using meta-heuristic algorithms. Analysis of obtained optimization results indicates that the PS algorithm is very applicable for solving machining optimization problems showing good competitive potential against stochastic direct search methods such as meta-heuristic algorithms. Specific features and merits of the PS algorithm were also discussed.

  19. Machine Learning Algorithms in Web Page Classification

    Directory of Open Access Journals (Sweden)

    W.A.AWAD

    2012-11-01

    Full Text Available In this paper we use machine learning algorithms like SVM, KNN and GIS to perform a behaviorcomparison on the web pages classifications problem, from the experiment we see in the SVM with smallnumber of negative documents to build the centroids has the smallest storage requirement and the least online test computation cost. But almost all GIS with different number of nearest neighbors have an evenhigher storage requirement and on line test computation cost than KNN. This suggests that some futurework should be done to try to reduce the storage requirement and on list test cost of GIS.

  20. Computer Vision and Machine Learning for Autonomous Characterization of AM Powder Feedstocks

    Science.gov (United States)

    DeCost, Brian L.; Jain, Harshvardhan; Rollett, Anthony D.; Holm, Elizabeth A.

    2017-03-01

    By applying computer vision and machine learning methods, we develop a system to characterize powder feedstock materials for metal additive manufacturing (AM). Feature detection and description algorithms are applied to create a microstructural scale image representation that can be used to cluster, compare, and analyze powder micrographs. When applied to eight commercial feedstock powders, the system classifies powder images into the correct material systems with greater than 95% accuracy. The system also identifies both representative and atypical powder images. These results suggest the possibility of measuring variations in powders as a function of processing history, relating microstructural features of powders to properties relevant to their performance in AM processes, and defining objective material standards based on visual images. A significant advantage of the computer vision approach is that it is autonomous, objective, and repeatable.

  1. Computer Vision and Machine Learning for Autonomous Characterization of AM Powder Feedstocks

    Science.gov (United States)

    DeCost, Brian L.; Jain, Harshvardhan; Rollett, Anthony D.; Holm, Elizabeth A.

    2016-12-01

    By applying computer vision and machine learning methods, we develop a system to characterize powder feedstock materials for metal additive manufacturing (AM). Feature detection and description algorithms are applied to create a microstructural scale image representation that can be used to cluster, compare, and analyze powder micrographs. When applied to eight commercial feedstock powders, the system classifies powder images into the correct material systems with greater than 95% accuracy. The system also identifies both representative and atypical powder images. These results suggest the possibility of measuring variations in powders as a function of processing history, relating microstructural features of powders to properties relevant to their performance in AM processes, and defining objective material standards based on visual images. A significant advantage of the computer vision approach is that it is autonomous, objective, and repeatable.

  2. Using Advanced Computer Vision Algorithms on Small Mobile Robots

    Science.gov (United States)

    2006-04-20

    this approach is the implementation of advanced computer vision algorithms on small mobile robots . We demonstrate the implementation and testing of the...following two algorithms useful on mobile robots : (1) object classification using a boosted Cascade of classifiers trained with the Adaboost training

  3. Extreme Learning Machine and Moving Least Square Regression Based Solar Panel Vision Inspection

    Directory of Open Access Journals (Sweden)

    Heng Liu

    2017-01-01

    Full Text Available In recent years, learning based machine intelligence has aroused a lot of attention across science and engineering. Particularly in the field of automatic industry inspection, the machine learning based vision inspection plays a more and more important role in defect identification and feature extraction. Through learning from image samples, many features of industry objects, such as shapes, positions, and orientations angles, can be obtained and then can be well utilized to determine whether there is defect or not. However, the robustness and the quickness are not easily achieved in such inspection way. In this work, for solar panel vision inspection, we present an extreme learning machine (ELM and moving least square regression based approach to identify solder joint defect and detect the panel position. Firstly, histogram peaks distribution (HPD and fractional calculus are applied for image preprocessing. Then an ELM-based defective solder joints identification is discussed in detail. Finally, moving least square regression (MLSR algorithm is introduced for solar panel position determination. Experimental results and comparisons show that the proposed ELM and MLSR based inspection method is efficient not only in detection accuracy but also in processing speed.

  4. A Machine Vision System for Automatically Grading Hardwood Lumber - (Industrial Metrology)

    Science.gov (United States)

    Richard W. Conners; Tai-Hoon Cho; Chong T. Ng; Thomas T. Drayer; Philip A. Araman; Robert L. Brisbon

    1992-01-01

    Any automatic system for grading hardwood lumber can conceptually be divided into two components. One of these is a machine vision system for locating and identifying grading defects. The other is an automatic grading program that accepts as input the output of the machine vision system and, based on these data, determines the grade of a board. The progress that has...

  5. Using Multiple FPGA Architectures for Real-time Processing of Low-level Machine Vision Functions

    Science.gov (United States)

    Thomas H. Drayer; William E. King; Philip A. Araman; Joseph G. Tront; Richard W. Conners

    1995-01-01

    In this paper, we investigate the use of multiple Field Programmable Gate Array (FPGA) architectures for real-time machine vision processing. The use of FPGAs for low-level processing represents an excellent tradeoff between software and special purpose hardware implementations. A library of modules that implement common low-level machine vision operations is presented...

  6. Robotics, vision and control fundamental algorithms in Matlab

    CERN Document Server

    Corke, Peter

    2017-01-01

    Robotic vision, the combination of robotics and computer vision, involves the application of computer algorithms to data acquired from sensors. The research community has developed a large body of such algorithms but for a newcomer to the field this can be quite daunting. For over 20 years the author has maintained two open-source MATLAB® Toolboxes, one for robotics and one for vision. They provide implementations of many important algorithms and allow users to work with real problems, not just trivial examples. This book makes the fundamental algorithms of robotics, vision and control accessible to all. It weaves together theory, algorithms and examples in a narrative that covers robotics and computer vision separately and together. Using the latest versions of the Toolboxes the author shows how complex problems can be decomposed and solved using just a few simple lines of code. The topics covered are guided by real problems observed by the author over many years as a practitioner of both robotics and compu...

  7. A real time vehicles detection algorithm for vision based sensors

    CERN Document Server

    Płaczek, Bartłomiej

    2011-01-01

    A vehicle detection plays an important role in the traffic control at signalised intersections. This paper introduces a vision-based algorithm for vehicles presence recognition in detection zones. The algorithm uses linguistic variables to evaluate local attributes of an input image. The image attributes are categorised as vehicle, background or unknown features. Experimental results on complex traffic scenes show that the proposed algorithm is effective for a real-time vehicles detection.

  8. Characterization of oats (Avena sativa L.) cultivars using machine vision.

    Science.gov (United States)

    Sumathi, S; Balamurugan, P

    2013-10-15

    Machine vision or image analysis is an important tool in the study of morphology of any materials. This technique has been used successfully to differentiate the eleven oats cultivars based on morphological characters. The geometry of seeds was measured through image analyzer and the variation was observed and recorded. From the recorded data, the cluster analysis was carried out and it revealed that the cultivars could be grouped into two main clusters based on similarity in the measured parameters. Cultivar Sabzar, UPO 212, OL 9 and OL 88 formed one main cluster. The another main cluster includes cv. Kent, OS 6, UPO 94, HFO 114, OS 7, HJ 8 and JHO 822 with many sub clusters. Among the cultivars HJ 8 and JHO 822 has more similarity in all measured parameters than other cultivars. Thus morphological characterization through seed image analysis was found useful to discriminate the cultivars.

  9. FUSION OF MULTI FOCUSED IMAGES USING HDWT FOR MACHINE VISION

    Directory of Open Access Journals (Sweden)

    S. Arumuga Perumal

    2011-10-01

    Full Text Available During image acquisition in machine vision, due to limited depth of field of lens, it is possible to take clear image of the objects in the scene which are in focus only. The remaining objects in the scene will be out of focus. A possible solution to bring clear images of all objects in the scene is image fusion. Image fusion is a process of combining multiple images to form the composite image with extended information content. This paper uses three band expansive higher density discrete wavelet transform to fuse two numbers of images focusing different objects in the same scene and also proposes three methods for image fusion. Experimental results on multi focused image fusion are presented in terms of root mean square, peak signal to noise ratio and quality index to illustrate the proposed fusion methods.

  10. Vision Based Autonomous Robot Navigation Algorithms and Implementations

    CERN Document Server

    Chatterjee, Amitava; Nirmal Singh, N

    2013-01-01

    This book is devoted to the theory and development of autonomous navigation of mobile robots using computer vision based sensing mechanism. The conventional robot navigation systems, utilizing traditional sensors like ultrasonic, IR, GPS, laser sensors etc., suffer several drawbacks related to either the physical limitations of the sensor or incur high cost. Vision sensing has emerged as a popular alternative where cameras can be used to reduce the overall cost, maintaining high degree of intelligence, flexibility and robustness. This book includes a detailed description of several new approaches for real life vision based autonomous navigation algorithms and SLAM. It presents the concept of how subgoal based goal-driven navigation can be carried out using vision sensing. The development concept of vision based robots for path/line tracking using fuzzy logic is presented, as well as how a low-cost robot can be indigenously developed in the laboratory with microcontroller based sensor systems. The book descri...

  11. Parallel Architectures and Parallel Algorithms for Integrated Vision Systems. Ph.D. Thesis

    Science.gov (United States)

    Choudhary, Alok Nidhi

    1989-01-01

    Computer vision is regarded as one of the most complex and computationally intensive problems. An integrated vision system (IVS) is a system that uses vision algorithms from all levels of processing to perform for a high level application (e.g., object recognition). An IVS normally involves algorithms from low level, intermediate level, and high level vision. Designing parallel architectures for vision systems is of tremendous interest to researchers. Several issues are addressed in parallel architectures and parallel algorithms for integrated vision systems.

  12. Dynamic programming and graph algorithms in computer vision.

    Science.gov (United States)

    Felzenszwalb, Pedro F; Zabih, Ramin

    2011-04-01

    Optimization is a powerful paradigm for expressing and solving problems in a wide range of areas, and has been successfully applied to many vision problems. Discrete optimization techniques are especially interesting since, by carefully exploiting problem structure, they often provide nontrivial guarantees concerning solution quality. In this paper, we review dynamic programming and graph algorithms, and discuss representative examples of how these discrete optimization techniques have been applied to some classical vision problems. We focus on the low-level vision problem of stereo, the mid-level problem of interactive object segmentation, and the high-level problem of model-based recognition.

  13. Dynamic Programming and Graph Algorithms in Computer Vision*

    Science.gov (United States)

    Felzenszwalb, Pedro F.; Zabih, Ramin

    2013-01-01

    Optimization is a powerful paradigm for expressing and solving problems in a wide range of areas, and has been successfully applied to many vision problems. Discrete optimization techniques are especially interesting, since by carefully exploiting problem structure they often provide non-trivial guarantees concerning solution quality. In this paper we briefly review dynamic programming and graph algorithms, and discuss representative examples of how these discrete optimization techniques have been applied to some classical vision problems. We focus on the low-level vision problem of stereo; the mid-level problem of interactive object segmentation; and the high-level problem of model-based recognition. PMID:20660950

  14. Vision Algorithms Catch Defects in Screen Displays

    Science.gov (United States)

    2014-01-01

    Andrew Watson, a senior scientist at Ames Research Center, developed a tool called the Spatial Standard Observer (SSO), which models human vision for use in robotic applications. Redmond, Washington-based Radiant Zemax LLC licensed the technology from NASA and combined it with its imaging colorimeter system, creating a powerful tool that high-volume manufacturers of flat-panel displays use to catch defects in screens.

  15. Research on PCB Micro-drill Detection by Machine Vision Based on Genetic Algorithm%基于遗传算法的PCB微钻视觉检测方法研究

    Institute of Scientific and Technical Information of China (English)

    丁度坤

    2013-01-01

    The vision detection method was researched,which was applied to PCB micro-drill under 0.1 mm.The detection platform was setup,and the micro-drill image could be obtained real time.On this basis,the genetic algorithm was deeply studied,the threshold segmentation method based on GA was designed,and it was applied to PCB micro-drill detection.The experimental results show that compared with the traditional Ostu method,using the GA segmentation method,more information is obtained,which is helpful to PCB micro-drill detection later on.%对直径在0.1mm以下的PCB微钻的视觉检测方法进行研究.搭建了PCB微钻视觉检测平台,可实时采集PCB微钻的图像信息;在此基础上,对遗传算法进行深入研究,设计了基于遗传算法的图像阈值分割算法,并将其应用于PCB微钻检测.实验结果表明:与传统的Otsu分割法相比,遗传算法获取的信息量更大,为后续微钻相关参数的检测奠定基础.

  16. Machine Vision Perception of the Human Body 3-d Behavior Recognition Algorithms%机器视觉感知三维图像中的人体行为识别算法

    Institute of Scientific and Technical Information of China (English)

    韩雪; 齐园

    2013-01-01

    研究机器视觉感知中的人体行为准确识别问题.机器视觉感知中,采集的信息多为二维平面信息,在合成三维图像感知信息过程中,传统的因式分解合成法运用形状基数量固定,很难表达复杂行为特征,造成行为特征会出现一定的偏差,人体行为识别准确性不高.为了避免上述缺陷,提出了一种新的机器视觉感知中的人体三维行为识别算法.采集人体行为图像,并检测图像的轮廓区域,对检测区间间隙初始划分,通过把三维不定特征在三维空间进行空间映射,完成模糊性的消除,为人体三维行为识别提供数据基础.根据提取的消除模糊性后的人体三维行作为特征分量,对人体三维行为进行识别.实验结果表明,利用这种算法进行人体三维行为识别,能够准确的识别人体的行为,极大地提高了人体行为识别的准确率.%Study machine visual perception of human behavior accurate identification method.Machine visual perception,the information collected more for two dimensional plane information,resulted in three dimensional perception information loss,the human body movement characteristic point shape base number fixed,it is difficult to express complex behavior characteristics,cause the traditional based on two-dimensional shape base algorithm of human behavior recognition accuracy is not high.In order to avoid the above defects,this paper puts forward a machine visual perception of the human body 3 d behavior recognition algorithm.Acquisition human behavior image,and testing image contour area,for the human body 3 d behavior identity provide data base.Extraction human three-dimensional behavior characteristics component,the human body 3 d behavior for identification.The experimental resuits show that using this algorithm human three-dimensional behavior identity,can accurate identification of human behavior,greatly enhancing the human behavior recognition accuracy.

  17. The Employment Effects of High-Technology: A Case Study of Machine Vision. Research Report No. 86-19.

    Science.gov (United States)

    Chen, Kan; Stafford, Frank P.

    A case study of machine vision was conducted to identify and analyze the employment effects of high technology in general. (Machine vision is the automatic acquisition and analysis of an image to obtain desired information for use in controlling an industrial activity, such as the visual sensor system that gives eyes to a robot.) Machine vision as…

  18. Development a Vision Based Seam Tracking System for None Destructive Testing Machines

    Directory of Open Access Journals (Sweden)

    Nasser moradi

    2013-04-01

    Full Text Available The automatic weld seam tracking is an important challenge in None Destructive Testing (NDT systems for welded pipe inspection. In this Study, a machine vision based seam tracker, is developed and implemented, instead of old electro-mechanical system. A novel algorithm based on the weld image centered is presented, to reduce Environment conditions and improve the seam tracking accuracy. The weld seam images are taken by a camera arranged ahead of the machine and the centered is extracted as a parameter to detect the weld position, and offset between this point and central axis is computed and used as control parameter of servomotors. Adaptive multi step segmentation t technique is employed to increase the probable of real edge of the welds and improve the line fitting accuracy. This new approach offers some important technical advantages over the existing solutions to weld seam detection: Its based on natural light and does not need any auxiliary light. The adaptive threshold segmentation technique applied, decrease Environmental lighting condition. Its accurate and stable in real time NDT testing machines. After a series of experiments in real industrial environment, it is demonstrated that accuracy of this method can improve the quality of NDT machines. The average tracking error is 1.5 pixels approximately 0.25mm..

  19. Nondestructive and rapid detection of potato black heart based on machine vision technology

    Science.gov (United States)

    Tian, Fang; Peng, Yankun; Wei, Wensong

    2016-05-01

    Potatoes are one of the major food crops in the world. Potato black heart is a kind of defect that the surface is intact while the tissues in skin become black. This kind of potato has lost the edibleness, but it's difficult to be detected with conventional methods. A nondestructive detection system based on the machine vision technology was proposed in this study to distinguish the normal and black heart of potatoes according to the different transmittance of them. The detection system was equipped with a monochrome CCD camera, LED light sources for transmitted illumination and a computer. Firstly, the transmission images of normal and black heart potatoes were taken by the detection system. Then the images were processed by algorithm written with VC++. As the transmitted light intensity was influenced by the radial dimension of the potato samples, the relationship between the grayscale value and the potato radial dimension was acquired by analyzing the grayscale value changing rule of the transmission image. Then proper judging condition was confirmed to distinguish the normal and black heart of potatoes after image preprocessing. The results showed that the nondestructive system built coupled with the processing methods was accessible for the detection of potato black heart at a considerable accuracy rate. The transmission detection technique based on machine vision is nondestructive and feasible to realize the detection of potato black heart.

  20. Broiler weight estimation based on machine vision and artificial neural network.

    Science.gov (United States)

    Amraei, S; Abdanan Mehdizadeh, S; Salari, S

    2017-04-01

    1. Machine vision and artificial neural network (ANN) procedures were used to estimate live body weight of broiler chickens in 30 1-d-old broiler chickens reared for 42 d. 2. Imaging was performed two times daily. To localise chickens within the pen, an ellipse fitting algorithm was used and the chickens' head and tail removed using the Chan-Vese method. 3. The correlations between the body weight and 6 physical extracted features indicated that there were strong correlations between body weight and the 5 features including area, perimeter, convex area, major and minor axis length. 5. According to statistical analysis there was no significant difference between morning and afternoon data over 42 d. 6. In an attempt to improve the accuracy of live weight approximation different ANN techniques, including Bayesian regulation, Levenberg-Marquardt, Scaled conjugate gradient and gradient descent were used. Bayesian regulation with R(2) value of 0.98 was the best network for prediction of broiler weight. 7. The accuracy of the machine vision technique was examined and most errors were less than 50 g.

  1. GEOLOGICAL MAPPING USING MACHINE LEARNING ALGORITHMS

    Directory of Open Access Journals (Sweden)

    A. S. Harvey

    2016-06-01

    Full Text Available Remotely sensed spectral imagery, geophysical (magnetic and gravity, and geodetic (elevation data are useful in a variety of Earth science applications such as environmental monitoring and mineral exploration. Using these data with Machine Learning Algorithms (MLA, which are widely used in image analysis and statistical pattern recognition applications, may enhance preliminary geological mapping and interpretation. This approach contributes towards a rapid and objective means of geological mapping in contrast to conventional field expedition techniques. In this study, four supervised MLAs (naïve Bayes, k-nearest neighbour, random forest, and support vector machines are compared in order to assess their performance for correctly identifying geological rocktypes in an area with complete ground validation information. Geological maps of the Sudbury region are used for calibration and validation. Percent of correct classifications was used as indicators of performance. Results show that random forest is the best approach. As expected, MLA performance improves with more calibration clusters, i.e. a more uniform distribution of calibration data over the study region. Performance is generally low, though geological trends that correspond to a ground validation map are visualized. Low performance may be the result of poor spectral images of bare rock which can be covered by vegetation or water. The distribution of calibration clusters and MLA input parameters affect the performance of the MLAs. Generally, performance improves with more uniform sampling, though this increases required computational effort and time. With the achievable performance levels in this study, the technique is useful in identifying regions of interest and identifying general rocktype trends. In particular, phase I geological site investigations will benefit from this approach and lead to the selection of sites for advanced surveys.

  2. Geological Mapping Using Machine Learning Algorithms

    Science.gov (United States)

    Harvey, A. S.; Fotopoulos, G.

    2016-06-01

    Remotely sensed spectral imagery, geophysical (magnetic and gravity), and geodetic (elevation) data are useful in a variety of Earth science applications such as environmental monitoring and mineral exploration. Using these data with Machine Learning Algorithms (MLA), which are widely used in image analysis and statistical pattern recognition applications, may enhance preliminary geological mapping and interpretation. This approach contributes towards a rapid and objective means of geological mapping in contrast to conventional field expedition techniques. In this study, four supervised MLAs (naïve Bayes, k-nearest neighbour, random forest, and support vector machines) are compared in order to assess their performance for correctly identifying geological rocktypes in an area with complete ground validation information. Geological maps of the Sudbury region are used for calibration and validation. Percent of correct classifications was used as indicators of performance. Results show that random forest is the best approach. As expected, MLA performance improves with more calibration clusters, i.e. a more uniform distribution of calibration data over the study region. Performance is generally low, though geological trends that correspond to a ground validation map are visualized. Low performance may be the result of poor spectral images of bare rock which can be covered by vegetation or water. The distribution of calibration clusters and MLA input parameters affect the performance of the MLAs. Generally, performance improves with more uniform sampling, though this increases required computational effort and time. With the achievable performance levels in this study, the technique is useful in identifying regions of interest and identifying general rocktype trends. In particular, phase I geological site investigations will benefit from this approach and lead to the selection of sites for advanced surveys.

  3. 3-D measuring of engine camshaft based on machine vision

    Science.gov (United States)

    Qiu, Jianxin; Tan, Liang; Xu, Xiaodong

    2008-12-01

    The non-touch 3D measuring based on machine vision is introduced into camshaft precise measuring. Currently, because CCD 3-dimensional measuring can't meet requirements for camshaft's measuring precision, it's necessary to improve its measuring precision. In this paper, we put forward a method to improve the measuring method. A Multi-Character Match method based on the Polygonal Non-regular model is advanced with the theory of Corner Extraction and Corner Matching .This method has solved the problem of the matching difficulty and a low precision. In the measuring process, the use of the Coded marked Point method and Self-Character Match method can bring on this problem. The 3D measuring experiment on camshaft, which based on the Multi-Character Match method of the Polygonal Non-regular model, proves that the normal average measuring precision is increased to a new level less than 0.04mm in the point-clouds photo merge. This measuring method can effectively increase the 3D measuring precision of the binocular CCD.

  4. Prolog-based prototyping software for machine vision

    Science.gov (United States)

    Batchelor, Bruce G.; Hack, Ralf; Jones, Andrew C.

    1996-10-01

    Prolog image processing (PIP) is a multi-media prototyping tool, intended to assist designers of intelligent industrial machine vision systems. This is the latest in a series of prolog-based systems that have been implemented at Cardiff, specifically for this purpose. The software package provides fully integrated facilities for both interactive and programmed image processing, 'smart' documentation, guidance about which lighting/viewing set-up to use, speech/natural language input and speech output. It can also be used to control a range of electro-mechanical devices, such as lamps, cameras, lenses, pneumatic positioning mechanisms, robots, etc., via a low-cost hardware interfacing module. The software runs on a standard computer, with no predecessors in that the image processing is carried out entirely in software. This article concentrates on the design and implementation of the PIP system, and presents programs for two demonstration applications: (a) recognizing a non-picture playing card; (b) recognizing a well laid table place setting.

  5. Robotic Arm Control Algorithm Based on Stereo Vision Using RoboRealm Vision

    Directory of Open Access Journals (Sweden)

    SZABO, R.

    2015-05-01

    Full Text Available The goal of this paper is to present a stereo computer vision algorithm intended to control a robotic arm. Specific points on the robot joints are marked and recognized in the software. Using a dedicated set of mathematic equations, the movement of the robot is continuously computed and monitored with webcams. Positioning error is finally analyzed.

  6. Fast and Robust Stereo Vision Algorithm for Obstacle Detection

    Institute of Scientific and Technical Information of China (English)

    Yi-peng Zhou

    2008-01-01

    Binocular computer vision is based on bionics, after the calibration through the camera head by double-exposure image synchronization, access to the calculation of two-dimensional image pixels of the three-dimensional depth information. In this paper, a fast and robust stereo vision algorithm is described to perform in-vehicle obstacles detection and characterization. The stereo algorithm which provides a suitable representation of the geometric content of the road scene is described, and an in-vehicle embedded system is presented. We present the way in which the algorithm is used, and then report experiments on real situations which show that our solution is accurate, reliable and efficient. In particular, both processes are fast, generic,robust to noise and bad conditions, and work even with partial occlusion.

  7. A real-time surface inspection system for precision steel balls based on machine vision

    Science.gov (United States)

    Chen, Yi-Ji; Tsai, Jhy-Cherng; Hsu, Ya-Chen

    2016-07-01

    Precision steel balls are one of the most fundament components for motion and power transmission parts and they are widely used in industrial machinery and the automotive industry. As precision balls are crucial for the quality of these products, there is an urgent need to develop a fast and robust system for inspecting defects of precision steel balls. In this paper, a real-time system for inspecting surface defects of precision steel balls is developed based on machine vision. The developed system integrates a dual-lighting system, an unfolding mechanism and inspection algorithms for real-time signal processing and defect detection. The developed system is tested under feeding speeds of 4 pcs s-1 with a detection rate of 99.94% and an error rate of 0.10%. The minimum detectable surface flaw area is 0.01 mm2, which meets the requirement for inspecting ISO grade 100 precision steel balls.

  8. A New High-Speed Foreign Fiber Detection System with Machine Vision

    Directory of Open Access Journals (Sweden)

    Zhiguo Chen

    2010-01-01

    Full Text Available A new high-speed foreign fiber detection system with machine vision is proposed for removing foreign fibers from raw cotton using optimal hardware components and appropriate algorithms designing. Starting from a specialized lens of 3-charged couple device (CCD camera, the system applied digital signal processor (DSP and field-programmable gate array (FPGA on image acquisition and processing illuminated by ultraviolet light, so as to identify transparent objects such as polyethylene and polypropylene fabric from cotton tuft flow by virtue of the fluorescent effect, until all foreign fibers that have been blown away safely by compressed air quality can be achieved. An image segmentation algorithm based on fast wavelet transform is proposed to identify block-like foreign fibers, and an improved canny detector is also developed to segment wire-like foreign fibers from raw cotton. The procedure naturally provides color image segmentation method with region growing algorithm for better adaptability. Experiments on a variety of images show that the proposed algorithms can effectively segment foreign fibers from test images under various circumstances.

  9. Support Vector Machine Optimized by Improved Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    Xiang Chang Sheng

    2013-07-01

    Full Text Available Parameters of support vector machines (SVM which is optimized by standard genetic algorithm is easy to trap into the local minimum, in order to get the optimal parameters of support vector machine, this paper proposed a parameters optimization method for support vector machines based on improved genetic algorithm, the simulation experiment is carried out on 5 benchmark datasets. The simulation show that the proposed method not only can assure the classification precision, but also can reduce training time markedly compared with standard genetic algorithm.

  10. Application of edge detection algorithm for vision guided robotics assembly system

    Science.gov (United States)

    Balabantaray, Bunil Kumar; Jha, Panchanand; Biswal, Bibhuti Bhusan

    2013-12-01

    Machine vision system has a major role in making robotic assembly system autonomous. Part detection and identification of the correct part are important tasks which need to be carefully done by a vision system to initiate the process. This process consists of many sub-processes wherein, the image capturing, digitizing and enhancing, etc. do account for reconstructive the part for subsequent operations. Edge detection of the grabbed image, therefore, plays an important role in the entire image processing activity. Thus one needs to choose the correct tool for the process with respect to the given environment. In this paper the comparative study of edge detection algorithm with grasping the object in robot assembly system is presented. The proposed work is performed on the Matlab R2010a Simulink. This paper proposes four algorithms i.e. Canny's, Robert, Prewitt and Sobel edge detection algorithm. An attempt has been made to find the best algorithm for the problem. It is found that Canny's edge detection algorithm gives better result and minimum error for the intended task.

  11. Low-Level Vision Algorithms for Localization, Classification, and Tracking

    OpenAIRE

    Kevin N. Gabayan

    2003-01-01

    Camera networks can provide images of detected objects that vary in perspective and level of obstruction. To improve the understanding of visual events, vision algorithms are implemented in a wireless sensor network. Methods were developed to fuse data from multiple cameras to improve object identification and location in the presence of obstructions. Training sets of images allow classification of objects into familiar categories. Feature-based object correspondence is used to track multiple...

  12. Constraint Drive Generation of Vision Algorithms on an Elastic Infrastructure

    Science.gov (United States)

    2014-10-01

    classifiers, image search indexes, human annotators, and heterogeneous computer vision algorithms. Processing is performed using the Apache Hadoop cluster...workers). Picarus is a web-service that executes large-scale visual analysis jobs using Hadoop with data stored on 10 Approved for Public Release...Installed Picarus (which requires Hadoop , HBase, and Redis) on two govcloud servers. Wrote up documentation for picarus adminis- tration http://goo.gl

  13. Machines are benchmarked by code, not algorithms

    NARCIS (Netherlands)

    Poss, R.

    2013-01-01

    This article highlights how small modifications to either the source code of a benchmark program or the compilation options may impact its behavior on a specific machine. It argues that for evaluating machines, benchmark providers and users be careful to ensure reproducibility of results based on th

  14. Optimization of machining processes using pattern search algorithm

    OpenAIRE

    Miloš Madić; Miroslav Radovanović

    2014-01-01

    Optimization of machining processes not only increases machining efficiency and economics, but also the end product quality. In recent years, among the traditional optimization methods, stochastic direct search optimization methods such as meta-heuristic algorithms are being increasingly applied for solving machining optimization problems. Their ability to deal with complex, multi-dimensional and ill-behaved optimization problems made them the preferred optimization tool by most researchers a...

  15. Optimal Placement Algorithms for Virtual Machines

    CERN Document Server

    Bellur, Umesh; SD, Madhu Kumar

    2010-01-01

    Cloud computing provides a computing platform for the users to meet their demands in an efficient, cost-effective way. Virtualization technologies are used in the clouds to aid the efficient usage of hardware. Virtual machines (VMs) are utilized to satisfy the user needs and are placed on physical machines (PMs) of the cloud for effective usage of hardware resources and electricity in the cloud. Optimizing the number of PMs used helps in cutting down the power consumption by a substantial amount. In this paper, we present an optimal technique to map virtual machines to physical machines (nodes) such that the number of required nodes is minimized. We provide two approaches based on linear programming and quadratic programming techniques that significantly improve over the existing theoretical bounds and efficiently solve the problem of virtual machine (VM) placement in data centers.

  16. Saudi License Plate Recognition Algorithm Based on Support Vector Machine

    Institute of Scientific and Technical Information of China (English)

    Khaled Suwais; Rana Al-Otaibi; Ali Alshahrani

    2013-01-01

    License plate recognition (LPR) is an image processing technology that is used to identify vehicles by their license plates. This paper presents a license plate recognition algorithm for Saudi car plates based on the support vector machine (SVM) algorithm. The new algorithm is efficient in recognizing the vehicles from the Arabic part of the plate. The performance of the system has been investigated and analyzed. The recognition accuracy of the algorithm is about 93.3%.

  17. Integrating Symbolic and Statistical Methods for Testing Intelligent Systems Applications to Machine Learning and Computer Vision

    Energy Technology Data Exchange (ETDEWEB)

    Jha, Sumit Kumar [University of Central Florida, Orlando; Pullum, Laura L [ORNL; Ramanathan, Arvind [ORNL

    2016-01-01

    Embedded intelligent systems ranging from tiny im- plantable biomedical devices to large swarms of autonomous un- manned aerial systems are becoming pervasive in our daily lives. While we depend on the flawless functioning of such intelligent systems, and often take their behavioral correctness and safety for granted, it is notoriously difficult to generate test cases that expose subtle errors in the implementations of machine learning algorithms. Hence, the validation of intelligent systems is usually achieved by studying their behavior on representative data sets, using methods such as cross-validation and bootstrapping.In this paper, we present a new testing methodology for studying the correctness of intelligent systems. Our approach uses symbolic decision procedures coupled with statistical hypothesis testing to. We also use our algorithm to analyze the robustness of a human detection algorithm built using the OpenCV open-source computer vision library. We show that the human detection implementation can fail to detect humans in perturbed video frames even when the perturbations are so small that the corresponding frames look identical to the naked eye.

  18. Machine Vision for Relative Spacecraft Navigation During Approach to Docking

    Science.gov (United States)

    Chien, Chiun-Hong; Baker, Kenneth

    2011-01-01

    This paper describes a machine vision system for relative spacecraft navigation during the terminal phase of approach to docking that: 1) matches high contrast image features of the target vehicle, as seen by a camera that is bore-sighted to the docking adapter on the chase vehicle, to the corresponding features in a 3d model of the docking adapter on the target vehicle and 2) is robust to on-orbit lighting. An implementation is provided for the case of the Space Shuttle Orbiter docking to the International Space Station (ISS) with quantitative test results using a full scale, medium fidelity mock-up of the ISS docking adapter mounted on a 6-DOF motion platform at the NASA Marshall Spaceflight Center Flight Robotics Laboratory and qualitative test results using recorded video from the Orbiter Docking System Camera (ODSC) during multiple orbiter to ISS docking missions. The Natural Feature Image Registration (NFIR) system consists of two modules: 1) Tracking which tracks the target object from image to image and estimates the position and orientation (pose) of the docking camera relative to the target object and 2) Acquisition which recognizes the target object if it is in the docking camera Field-of-View and provides an approximate pose that is used to initialize tracking. Detected image edges are matched to the 3d model edges whose predicted location, based on the pose estimate and its first time derivative from the previous frame, is closest to the detected edge1 . Mismatches are eliminated using a rigid motion constraint. The remaining 2d image to 3d model matches are used to make a least squares estimate of the change in relative pose from the previous image to the current image. The changes in position and in attitude are used as data for two Kalman filters whose outputs are smoothed estimate of position and velocity plus attitude and attitude rate that are then used to predict the location of the 3d model features in the next image.

  19. Extreme learning machines 2013 algorithms and applications

    CERN Document Server

    Toh, Kar-Ann; Romay, Manuel; Mao, Kezhi

    2014-01-01

    In recent years, ELM has emerged as a revolutionary technique of computational intelligence, and has attracted considerable attentions. An extreme learning machine (ELM) is a single layer feed-forward neural network alike learning system, whose connections from the input layer to the hidden layer are randomly generated, while the connections from the hidden layer to the output layer are learned through linear learning methods. The outstanding merits of extreme learning machine (ELM) are its fast learning speed, trivial human intervene and high scalability.   This book contains some selected papers from the International Conference on Extreme Learning Machine 2013, which was held in Beijing China, October 15-17, 2013. This conference aims to bring together the researchers and practitioners of extreme learning machine from a variety of fields including artificial intelligence, biomedical engineering and bioinformatics, system modelling and control, and signal and image processing, to promote research and discu...

  20. Investigation into the use of smartphone as a machine vision device for engineering metrology and flaw detection, with focus on drilling

    Science.gov (United States)

    Razdan, Vikram; Bateman, Richard

    2015-05-01

    This study investigates the use of a Smartphone and its camera vision capabilities in Engineering metrology and flaw detection, with a view to develop a low cost alternative to Machine vision systems which are out of range for small scale manufacturers. A Smartphone has to provide a similar level of accuracy as Machine Vision devices like Smart cameras. The objective set out was to develop an App on an Android Smartphone, incorporating advanced Computer vision algorithms written in java code. The App could then be used for recording measurements of Twist Drill bits and hole geometry, and analysing the results for accuracy. A detailed literature review was carried out for in-depth study of Machine vision systems and their capabilities, including a comparison between the HTC One X Android Smartphone and the Teledyne Dalsa BOA Smart camera. A review of the existing metrology Apps in the market was also undertaken. In addition, the drilling operation was evaluated to establish key measurement parameters of a twist Drill bit, especially flank wear and diameter. The methodology covers software development of the Android App, including the use of image processing algorithms like Gaussian Blur, Sobel and Canny available from OpenCV software library, as well as designing and developing the experimental set-up for carrying out the measurements. The results obtained from the experimental set-up were analysed for geometry of Twist Drill bits and holes, including diametrical measurements and flaw detection. The results show that Smartphones like the HTC One X have the processing power and the camera capability to carry out metrological tasks, although dimensional accuracy achievable from the Smartphone App is below the level provided by Machine vision devices like Smart cameras. A Smartphone with mechanical attachments, capable of image processing and having a reasonable level of accuracy in dimensional measurement, has the potential to become a handy low-cost Machine vision

  1. Research on automatic inspection system for defects on precise optical surface based on machine vision

    Institute of Scientific and Technical Information of China (English)

    WANG Xue; XIE Zhi-jiang

    2006-01-01

    In manufacture of precise optical products, it is important to inspect and classify the potential defects existing on the products' surfaces after precise machining in order to obtain high quality in both functionality and aesthetics. The existing methods for detecting and classifying defects all are low accuracy or efficiency or high cost in inspection process. In this paper, a new inspection system based on machine vision has been introduced, which uses automatic focusing and image mosaic technologies to rapidly acquire distinct surface image, and employs Case-Based Reasoning(CBR)method in defects classification. A modificatory fuzzy similarity algorithm in CBR has been adopted for more quick and robust need of pattern recognition in practice inspection. Experiments show that the system can inspect surface diameter of 500mm in half an hour with resolving power of 0.8μm diameter according to digs or 0.5μm transverse width according to scratches. The proposed inspection principles and methods not only have meet manufacturing requirements of precise optical products, but also have great potential applications in other fields of precise surface inspection.

  2. Interaction of algorithm and implementation for analog VLSI stereo vision

    Science.gov (United States)

    Hakkarainen, J. M.; Little, James J.; Lee, Hae-Seung; Wyatt, John L., Jr.

    1991-07-01

    Design of a high-speed stereo vision system in analog VLSI technology is reported. The goal is to determine how the advantages of analog VLSI--small area, high speed, and low power-- can be exploited, and how the effects of its principal disadvantages--limited accuracy, inflexibility, and lack of storage capacity--can be minimized. Three stereo algorithms are considered, and a simulation study is presented to examine details of the interaction between algorithm and analog VLSI implementation. The Marr-Poggio-Drumheller algorithm is shown to be best suited for analog VLSI implementation. A CCD/CMOS stereo system implementation is proposed, capable of operation at 6000 image frame pairs per second for 48 X 48 images, and faster than frame rate operation on 256 X 256 binocular image pairs.

  3. Geometry constraints and matching algorithm for lunar rover stereo vision

    Institute of Scientific and Technical Information of China (English)

    HOU Jian; QI Nai-ming

    2005-01-01

    A feature-constrained stereo matching algorithm for lunar rover navigation is presented based on the analysis of the stereo vision system and working environments of lunar rover. In feature-matching phase, edge points are extracted with wavelet transform and are used as the primitives for matching. Then three criterions are utilized in turn to select the correct matching points with the pyramidal searching strategy. As a result,the algorithm finds corresponding points successfully for large numbers of edge points. Area-matching is accomplished under the constraint of edge-matching results,and the correlation is selected as the criterion.Experimental results with real images of natural terrain indicate that the algorithm provides dense disparity maps with fairly high accuracy.

  4. State-Estimation Algorithm Based on Computer Vision

    Science.gov (United States)

    Bayard, David; Brugarolas, Paul

    2007-01-01

    An algorithm and software to implement the algorithm are being developed as means to estimate the state (that is, the position and velocity) of an autonomous vehicle, relative to a visible nearby target object, to provide guidance for maneuvering the vehicle. In the original intended application, the autonomous vehicle would be a spacecraft and the nearby object would be a small astronomical body (typically, a comet or asteroid) to be explored by the spacecraft. The algorithm could also be used on Earth in analogous applications -- for example, for guiding underwater robots near such objects of interest as sunken ships, mineral deposits, or submerged mines. It is assumed that the robot would be equipped with a vision system that would include one or more electronic cameras, image-digitizing circuitry, and an imagedata- processing computer that would generate feature-recognition data products.

  5. Ethical, environmental and social issues for machine vision in manufacturing industry

    Science.gov (United States)

    Batchelor, Bruce G.; Whelan, Paul F.

    1995-10-01

    Some of the ethical, environmental and social issues relating to the design and use of machine vision systems in manufacturing industry are highlighted. The authors' aim is to emphasize some of the more important issues, and raise general awareness of the need to consider the potential advantages and hazards of machine vision technology. However, in a short article like this, it is impossible to cover the subject comprehensively. This paper should therefore be seen as a discussion document, which it is hoped will provoke more detailed consideration of these very important issues. It follows from an article presented at last year's workshop. Five major topics are discussed: (1) The impact of machine vision systems on the environment; (2) The implications of machine vision for product and factory safety, the health and well-being of employees; (3) The importance of intellectual integrity in a field requiring a careful balance of advanced ideas and technologies; (4) Commercial and managerial integrity; and (5) The impact of machine visions technology on employment prospects, particularly for people with low skill levels.

  6. Vision-based vehicle detection and tracking algorithm design

    Science.gov (United States)

    Hwang, Junyeon; Huh, Kunsoo; Lee, Donghwi

    2009-12-01

    The vision-based vehicle detection in front of an ego-vehicle is regarded as promising for driver assistance as well as for autonomous vehicle guidance. The feasibility of vehicle detection in a passenger car requires accurate and robust sensing performance. A multivehicle detection system based on stereo vision has been developed for better accuracy and robustness. This system utilizes morphological filter, feature detector, template matching, and epipolar constraint techniques in order to detect the corresponding pairs of vehicles. After the initial detection, the system executes the tracking algorithm for the vehicles. The proposed system can detect front vehicles such as the leading vehicle and side-lane vehicles. The position parameters of the vehicles located in front are obtained based on the detection information. The proposed vehicle detection system is implemented on a passenger car, and its performance is verified experimentally.

  7. Vision Algorithm for the Solar Aspect System of the HEROES Mission

    Science.gov (United States)

    Cramer, Alexander; Christe, Steven; Shih, Albert

    2014-01-01

    This work covers the design and test of a machine vision algorithm for generating high-accuracy pitch and yaw pointing solutions relative to the sun for the High Energy Replicated Optics to Explore the Sun (HEROES) mission. It describes how images were constructed by focusing an image of the sun onto a plate printed with a pattern of small fiducial markers. Images of this plate were processed in real time to determine relative position of the balloon payload to the sun. The algorithm is broken into four problems: circle detection, fiducial detection, fiducial identification, and image registration. Circle detection is handled by an Average Intersection method, fiducial detection by a matched filter approach, identification with an ad-hoc method based on the spacing between fiducials, and image registration with a simple least squares fit. Performance is verified on a combination of artificially generated images, test data recorded on the ground, and images from the 2013 flight.

  8. Picture Collage with Genetic Algorithm and Stereo vision

    Directory of Open Access Journals (Sweden)

    Hesam Ekhtiyar

    2011-09-01

    Full Text Available In this paper, a salient region extraction method for creating picture collage based on stereo vision is proposed. Picture collage is a kind of visual image summary to arrange all input images on a given canvas, allowing overlay, to maximize visible visual information. The salient regions of each image are firstly extracted and represented as a depth map. The output picture collage shows as many visible salient regions (without being overlaid by others from all images as possible. A very efficient Genetic algorithm is used here for the optimization. The experimental results showed the superior performance of the proposed method.

  9. Some multigrid algorithms for SIMD machines

    Energy Technology Data Exchange (ETDEWEB)

    Dendy, J.E. Jr. [Los Alamos National Lab., NM (United States)

    1996-12-31

    Previously a semicoarsening multigrid algorithm suitable for use on SIMD architectures was investigated. Through the use of new software tools, the performance of this algorithm has been considerably improved. The method has also been extended to three space dimensions. The method performs well for strongly anisotropic problems and for problems with coefficients jumping by orders of magnitude across internal interfaces. The parallel efficiency of this method is analyzed, and its actual performance on the CM-5 is compared with its performance on the CRAY-YMP. A standard coarsening multigrid algorithm is also considered, and we compare its performance on these two platforms as well.

  10. Musca domestica inspired machine vision system with hyperacuity

    Science.gov (United States)

    Riley, Dylan T.; Harman, William M.; Tomberlin, Eric; Barrett, Steven F.; Wilcox, Michael; Wright, Cameron H. G.

    2005-05-01

    Musca domestica, the common house fly, has a simple yet powerful and accessible vision system. Cajal indicated in 1885 the fly's vision system is the same as in the human retina. The house fly has some intriguing vision system features such as fast, analog, parallel operation. Furthermore, it has the ability to detect movement and objects at far better resolution than predicted by photoreceptor spacing, termed hyperacuity. We are investigating the mechanisms behind these features and incorporating them into next generation vision systems. We have developed a prototype sensor that employs a fly inspired arrangement of photodetectors sharing a common lens. The Gaussian shaped acceptance profile of each sensor coupled with overlapped sensor field of views provide the necessary configuration for obtaining hyperacuity data. The sensor is able to detect object movement with far greater resolution than that predicted by photoreceptor spacing. We have exhaustively tested and characterized the sensor to determine its practical resolution limit. Our tests coupled with theory from Bucklew and Saleh (1985) indicate that the limit to the hyperacuity response may only be related to target contrast. We have also implemented an array of these prototype sensors which will allow for two - dimensional position location. These high resolution, low contrast capable sensors are being developed for use as a vision system for an autonomous robot and the next generation of smart wheel chairs. However, they are easily adapted for biological endoscopy, downhole monitoring in oil wells, and other applications.

  11. Machine-Learning Algorithms to Code Public Health Spending Accounts.

    Science.gov (United States)

    Brady, Eoghan S; Leider, Jonathon P; Resnick, Beth A; Alfonso, Y Natalia; Bishai, David

    Government public health expenditure data sets require time- and labor-intensive manipulation to summarize results that public health policy makers can use. Our objective was to compare the performances of machine-learning algorithms with manual classification of public health expenditures to determine if machines could provide a faster, cheaper alternative to manual classification. We used machine-learning algorithms to replicate the process of manually classifying state public health expenditures, using the standardized public health spending categories from the Foundational Public Health Services model and a large data set from the US Census Bureau. We obtained a data set of 1.9 million individual expenditure items from 2000 to 2013. We collapsed these data into 147 280 summary expenditure records, and we followed a standardized method of manually classifying each expenditure record as public health, maybe public health, or not public health. We then trained 9 machine-learning algorithms to replicate the manual process. We calculated recall, precision, and coverage rates to measure the performance of individual and ensembled algorithms. Compared with manual classification, the machine-learning random forests algorithm produced 84% recall and 91% precision. With algorithm ensembling, we achieved our target criterion of 90% recall by using a consensus ensemble of ≥6 algorithms while still retaining 93% coverage, leaving only 7% of the summary expenditure records unclassified. Machine learning can be a time- and cost-saving tool for estimating public health spending in the United States. It can be used with standardized public health spending categories based on the Foundational Public Health Services model to help parse public health expenditure information from other types of health-related spending, provide data that are more comparable across public health organizations, and evaluate the impact of evidence-based public health resource allocation.

  12. Machine learning algorithms for datasets popularity prediction

    CERN Document Server

    Kancys, Kipras

    2016-01-01

    This report represents continued study where ML algorithms were used to predict databases popularity. Three topics were covered. First of all, there was a discrepancy between old and new meta-data collection procedures, so a reason for that had to be found. Secondly, different parameters were analysed and dropped to make algorithms perform better. And third, it was decided to move modelling part on Spark.

  13. Hand gesture recognition system based in computer vision and machine learning

    OpenAIRE

    Trigueiros, Paulo; Ribeiro, António Fernando; Reis, L.P.

    2015-01-01

    "Lecture notes in computational vision and biomechanics series, ISSN 2212-9391, vol. 19" Hand gesture recognition is a natural way of human computer interaction and an area of very active research in computer vision and machine learning. This is an area with many different possible applications, giving users a simpler and more natural way to communicate with robots/systems interfaces, without the need for extra devices. So, the primary goal of gesture recognition research applied to Hum...

  14. A hybrid algorithm for unrelated parallel machines scheduling

    Directory of Open Access Journals (Sweden)

    Mohsen Shafiei Nikabadi

    2016-09-01

    Full Text Available In this paper, a new hybrid algorithm based on multi-objective genetic algorithm (MOGA using simulated annealing (SA is proposed for scheduling unrelated parallel machines with sequence-dependent setup times, varying due dates, ready times and precedence relations among jobs. Our objective is to minimize makespan (Maximum completion time of all machines, number of tardy jobs, total tardiness and total earliness at the same time which can be more advantageous in real environment than considering each of objectives separately. For obtaining an optimal solution, hybrid algorithm based on MOGA and SA has been proposed in order to gain both good global and local search abilities. Simulation results and four well-known multi-objective performance metrics, indicate that the proposed hybrid algorithm outperforms the genetic algorithm (GA and SA in terms of each objective and significantly in minimizing the total cost of the weighted function.

  15. Computer vision and machine learning with RGB-D sensors

    CERN Document Server

    Shao, Ling; Kohli, Pushmeet

    2014-01-01

    This book presents an interdisciplinary selection of cutting-edge research on RGB-D based computer vision. Features: discusses the calibration of color and depth cameras, the reduction of noise on depth maps and methods for capturing human performance in 3D; reviews a selection of applications which use RGB-D information to reconstruct human figures, evaluate energy consumption and obtain accurate action classification; presents an approach for 3D object retrieval and for the reconstruction of gas flow from multiple Kinect cameras; describes an RGB-D computer vision system designed to assist t

  16. [Algorithms, machine intelligence, big data : general considerations].

    Science.gov (United States)

    Radermacher, F J

    2015-08-01

    We are experiencing astonishing developments in the areas of big data and artificial intelligence. They follow a pattern that we have now been observing for decades: according to Moore's Law,the performance and efficiency in the area of elementary arithmetic operations increases a thousand-fold every 20 years. Although we have not achieved the status where in the singular sense machines have become as "intelligent" as people, machines are becoming increasingly better. The Internet of Things has again helped to massively increase the efficiency of machines. Big data and suitable analytics do the same. If we let these processes simply continue, our civilization may be endangerd in many instances. If the "containment" of these processes succeeds in the context of a reasonable political global governance, a worldwide eco-social market economy, andan economy of green and inclusive markets, many desirable developments that are advantageous for our future may result. Then, at some point in time, the constant need for more and faster innovation may even stop. However, this is anything but certain. We are facing huge challenges.

  17. The systematic development of a machine vision based milking robot.

    NARCIS (Netherlands)

    Gouws, J.

    1993-01-01

    Agriculture involves unique interactions between man, machines, and various elements from nature. Therefore the implementation of advanced technology in agriculture holds different challenges than in other sectors of the economy. This dissertation stems from research into the application of advanced

  18. Machine Vision Automation for Ground Control Tele-Robotics Project

    Data.gov (United States)

    National Aeronautics and Space Administration — This project seeks to advance ground based tele-robotic capabilities with the development of natural feature target tracking technology with the use of machine...

  19. Support vector machines optimization based theory, algorithms, and extensions

    CERN Document Server

    Deng, Naiyang; Zhang, Chunhua

    2013-01-01

    Support Vector Machines: Optimization Based Theory, Algorithms, and Extensions presents an accessible treatment of the two main components of support vector machines (SVMs)-classification problems and regression problems. The book emphasizes the close connection between optimization theory and SVMs since optimization is one of the pillars on which SVMs are built.The authors share insight on many of their research achievements. They give a precise interpretation of statistical leaning theory for C-support vector classification. They also discuss regularized twi

  20. Dermoscopic Image Segmentation using Machine Learning Algorithm

    Directory of Open Access Journals (Sweden)

    L. P. Suresh

    2011-01-01

    Full Text Available Problem statement: Malignant melanoma is the most frequent type of skin cancer. Its incidence has been rapidly increasing over the last few decades. Medical image segmentation is the most essential and crucial process in order to facilitate the characterization and visualization of the structure of interest in medical images. Approach: This study explains the task of segmenting skin lesions in Dermoscopy images based on intelligent systems such as Fuzzy and Neural Networks clustering techniques for the early diagnosis of Malignant Melanoma. The various intelligent system based clustering techniques used are Fuzzy C Means Algorithm (FCM, Possibilistic C Means Algorithm (PCM, Hierarchical C Means Algorithm (HCM; C-mean based Fuzzy Hopfield Neural Network, Adaline Neural Network and Regression Neural Network. Results: The segmented images are compared with the ground truth image using various parameters such as False Positive Error (FPE, False Negative Error (FNE Coefficient of similarity, spatial overlap and their performance is evaluated. Conclusion: The experimental results show that the Hierarchical C Means algorithm( Fuzzy provides better segmentation than other (Fuzzy C Means, Possibilistic C Means, Adaline Neural Network, FHNN and GRNN clustering algorithms. Thus Hierarchical C Means approach can handle uncertainties that exist in the data efficiently and useful for the lesion segmentation in a computer aided diagnosis system to assist the clinical diagnosis of dermatologists.

  1. Semi-Online Algorithms for Scheduling with Machine Cost

    Institute of Scientific and Technical Information of China (English)

    Yi-Wei Jiang; Yong He

    2006-01-01

    In this paper, we consider the following semi-online List Model problem with known total size. We are given a sequence of independent jobs with positive sizes, which must be assigned to be processed on machines. No machines are initially provided, and when a job is revealed the algorithm has the option to purchase new machines. By normalizing all job sizes and machine cost, we assume that the cost of purchasing one machine is 1. We further know the total size of all jobs in advance. The objective is to minimize the sum of the makespan and the number of machines to be purchased. Both non-preemptive and preemptive versions are considered. For the non-preemptive version, we present a new lower bound 6/5 which improves the known lower bound 1.161. For the preemptive version, we present an optimal semi-online algorithm with a competitive ratio of 1 in the case that the total size is not greater than 4, and an algorithm with a competitive ratio of 5/4 otherwise, while a lower bound 1.0957 is also presented for general case.

  2. Measuring Leaf Motion of Tomato by Machine Vision

    NARCIS (Netherlands)

    Henten, van E.J.; Marx, G.E.H.; Hofstee, J.W.; Hemming, J.; Sarlikioti, V.

    2012-01-01

    For a better understanding of growth and development of tomato plants in three dimensional space, tomato plants were monitored using a computer vision system. It is commonly known that leaves of tomato plants do not have a fixed position and orientation during the day; they move in response to chang

  3. On-line estimation of laser-drilled hole depth using a machine vision method.

    Science.gov (United States)

    Ho, Chao-Ching; He, Jun-Jia; Liao, Te-Ying

    2012-01-01

    The paper presents a novel method for monitoring and estimating the depth of a laser-drilled hole using machine vision. Through on-line image acquisition and analysis in laser machining processes, we could simultaneously obtain correlations between the machining processes and analyzed images. Based on the machine vision method, the depths of laser-machined holes could be estimated in real time. Therefore, a low cost on-line inspection system is developed to increase productivity. All of the processing work was performed in air under standard atmospheric conditions and gas assist was used. A correlation between the cumulative size of the laser-induced plasma region and the depth of the hole is presented. The result indicates that the estimated depths of the laser-drilled holes were a linear function of the cumulative plasma size, with a high degree of confidence. This research provides a novel machine vision-based method for estimating the depths of laser-drilled holes in real time.

  4. On-Line Estimation of Laser-Drilled Hole Depth Using a Machine Vision Method

    Directory of Open Access Journals (Sweden)

    Te-Ying Liao

    2012-07-01

    Full Text Available The paper presents a novel method for monitoring and estimating the depth of a laser-drilled hole using machine vision. Through on-line image acquisition and analysis in laser machining processes, we could simultaneously obtain correlations between the machining processes and analyzed images. Based on the machine vision method, the depths of laser-machined holes could be estimated in real time. Therefore, a low cost on-line inspection system is developed to increase productivity. All of the processing work was performed in air under standard atmospheric conditions and gas assist was used. A correlation between the cumulative size of the laser-induced plasma region and the depth of the hole is presented. The result indicates that the estimated depths of the laser-drilled holes were a linear function of the cumulative plasma size, with a high degree of confidence. This research provides a novel machine vision-based method for estimating the depths of laser-drilled holes in real time.

  5. 75 FR 71146 - In the Matter of Certain Machine Vision Software, Machine Vision Systems, and Products Containing...

    Science.gov (United States)

    2010-11-22

    ..., California; Techno Soft Systemnics, Inc. (``Techno Soft'') of Japan; Fuji Machine Manufacturing Co., Ltd. of... the investigation as to Amistar based on a consent order and settlement agreement, and as to...

  6. Machine vision-based high-resolution weed mapping and patch-sprayer performance simulation

    NARCIS (Netherlands)

    Tang, L.; Tian, L.F.; Steward, B.L.

    1999-01-01

    An experimental machine vision-based patch-sprayer was developed. This sprayer was primarily designed to do real-time weed density estimation and variable herbicide application rate control. However, the sprayer also had the capability to do high-resolution weed mapping if proper mapping techniques

  7. A Multiple Sensor Machine Vision System for Automatic Hardwood Feature Detection

    Science.gov (United States)

    D. Earl Kline; Richard W. Conners; Daniel L. Schmoldt; Philip A. Araman; Robert L. Brisbin

    1993-01-01

    A multiple sensor machine vision prototype is being developed to scan full size hardwood lumber at industrial speeds for automatically detecting features such as knots holes, wane, stain, splits, checks, and color. The prototype integrates a multiple sensor imaging system, a materials handling system, a computer system, and application software. The prototype provides...

  8. Gall mite inspection on dormant black currant buds using machine vision

    DEFF Research Database (Denmark)

    Nielsen, M. R.; Stigaard Laursen, Morten; Jonassen, M. S.

    2013-01-01

    This paper presents a novel machine vision-based approach detecting and mapping gall mite infection in dormant buds on black currant bushes. A vehicle was fitted with four cameras and RTK-GPS. Results compared automatic detection to human decisions based on the images, and by mapping the results ...

  9. Machine vision analysis for industrial beet color change kinetics and total soluble solid content

    Science.gov (United States)

    A machine vision system (MVS) for the measurement of color change kinetics in crushed industrial beet to evaluate the total soluble solid content (°Brix) was developed in this study. It is expected that higher the °Brix faster the color change and modeling this color change kinetics helps in assessi...

  10. Automatic detection and counting of cattle in UAV imagery based on machine vision technology (Conference Presentation)

    Science.gov (United States)

    Rahnemoonfar, Maryam; Foster, Jamie; Starek, Michael J.

    2017-05-01

    Beef production is the main agricultural industry in Texas, and livestock are managed in pasture and rangeland which are usually huge in size, and are not easily accessible by vehicles. The current research method for livestock location identification and counting is visual observation which is very time consuming and costly. For animals on large tracts of land, manned aircraft may be necessary to count animals which is noisy and disturbs the animals, and may introduce a source of error in counts. Such manual approaches are expensive, slow and labor intensive. In this paper we study the combination of small unmanned aerial vehicle (sUAV) and machine vision technology as a valuable solution to manual animal surveying. A fixed-wing UAV fitted with GPS and digital RGB camera for photogrammetry was flown at the Welder Wildlife Foundation in Sinton, TX. Over 600 acres were flown with four UAS flights and individual photographs used to develop orthomosaic imagery. To detect animals in UAV imagery, a fully automatic technique was developed based on spatial and spectral characteristics of objects. This automatic technique can even detect small animals that are partially occluded by bushes. Experimental results in comparison to ground-truth show the effectiveness of our algorithm.

  11. Histogram of Intensity Feature Extraction for Automatic Plastic Bottle Recycling System Using Machine Vision

    Directory of Open Access Journals (Sweden)

    Suzaimah Ramli

    2008-01-01

    Full Text Available Currently, many recycling activities adopt manual sorting for plastic recycling that relies on plant personnel who visually identify and pick plastic bottles as they travel along the conveyor belt. These bottles are then sorted into the respective containers. Manual sorting may not be a suitable option for recycling facilities of high throughput. It has also been noted that the high turnover among sorting line workers had caused difficulties in achieving consistency in the plastic separation process. As a result, an intelligent system for automated sorting is greatly needed to replace manual sorting system. The core components of machine vision for this intelligent sorting system is the image recognition and classification. In this research, the overall plastic bottle sorting system is described. Additionally, the feature extraction algorithm used is discussed in detail since it is the core component of the overall system that determines the success rate. The performance of the proposed feature extractions were evaluated in terms of classification accuracy and result obtained showed an accuracy of more than 80%.

  12. Machine Vision Based Measurement of Dynamic Contact Angles in Microchannel Flows

    Institute of Scientific and Technical Information of China (English)

    Valtteri Heiskanen; Kalle Marjanen; Pasi Kallio

    2008-01-01

    When characterizing flows in miniaturized channels, the determination of the dynamic contact angle is important. By measuring the dynamic contact angle, the flow properties of the flowing liquid and the effect of material properties on the flow can be characterized. A machine vision based system to measure the contact angle of front or rear menisci of a moving liquid plug is described in this article. In this research, transparent flow channels fabricated on thermoplastic polymer and scaled with an adhesive tape are used. The transparency of the channels enables image based monitoring and measurement of flow variables, including the dynamic contact angle. It is shown that the dynamic angle can be measured from a liquid flow in a channel using the image based measurement system. An image processing algorithm has been developed in a MATLAB environment. Im-ages are taken using a CCD camera and the channels are illuminated using a custom made ring light. Two fitting methods, a circle and two parabolas, are experimented and the results are compared in the measurement of the dynamic contact angles.

  13. 3D Machine Vision and Additive Manufacturing: Concurrent Product and Process Development

    Science.gov (United States)

    Ilyas, Ismet P.

    2013-06-01

    The manufacturing environment rapidly changes in turbulence fashion. Digital manufacturing (DM) plays a significant role and one of the key strategies in setting up vision and strategic planning toward the knowledge based manufacturing. An approach of combining 3D machine vision (3D-MV) and an Additive Manufacturing (AM) may finally be finding its niche in manufacturing. This paper briefly overviews the integration of the 3D machine vision and AM in concurrent product and process development, the challenges and opportunities, the implementation of the 3D-MV and AM at POLMAN Bandung in accelerating product design and process development, and discusses a direct deployment of this approach on a real case from our industrial partners that have placed this as one of the very important and strategic approach in research as well as product/prototype development. The strategic aspects and needs of this combination approach in research, design and development are main concerns of the presentation.

  14. A Stereo-Vision Based Hazard-Detection Algorithm for Future Planetary Landers

    Science.gov (United States)

    Woicke, S.; Mooij, E.

    2014-06-01

    A hazard detection algorithm based on the stereo-vision principle is presented. A sensitivity analysis concerning the minimum baseline and the maximum altitude is discussed, based on which the limitations of this algorithm are investigated.

  15. Dataflow-Based Mapping of Computer Vision Algorithms onto FPGAs

    Directory of Open Access Journals (Sweden)

    Schlessman Jason

    2007-01-01

    Full Text Available We develop a design methodology for mapping computer vision algorithms onto an FPGA through the use of coarse-grain reconfigurable dataflow graphs as a representation to guide the designer. We first describe a new dataflow modeling technique called homogeneous parameterized dataflow (HPDF, which effectively captures the structure of an important class of computer vision applications. This form of dynamic dataflow takes advantage of the property that in a large number of image processing applications, data production and consumption rates can vary, but are equal across dataflow graph edges for any particular application iteration. After motivating and defining the HPDF model of computation, we develop an HPDF-based design methodology that offers useful properties in terms of verifying correctness and exposing performance-enhancing transformations; we discuss and address various challenges in efficiently mapping an HPDF-based application representation into target-specific HDL code; and we present experimental results pertaining to the mapping of a gesture recognition application onto the Xilinx Virtex II FPGA.

  16. Robot Guidance Using Machine Vision Techniques in Industrial Environments: A Comparative Review

    Directory of Open Access Journals (Sweden)

    Luis Pérez

    2016-03-01

    Full Text Available In the factory of the future, most of the operations will be done by autonomous robots that need visual feedback to move around the working space avoiding obstacles, to work collaboratively with humans, to identify and locate the working parts, to complete the information provided by other sensors to improve their positioning accuracy, etc. Different vision techniques, such as photogrammetry, stereo vision, structured light, time of flight and laser triangulation, among others, are widely used for inspection and quality control processes in the industry and now for robot guidance. Choosing which type of vision system to use is highly dependent on the parts that need to be located or measured. Thus, in this paper a comparative review of different machine vision techniques for robot guidance is presented. This work analyzes accuracy, range and weight of the sensors, safety, processing time and environmental influences. Researchers and developers can take it as a background information for their future works.

  17. Robot Guidance Using Machine Vision Techniques in Industrial Environments: A Comparative Review

    Science.gov (United States)

    Pérez, Luis; Rodríguez, Íñigo; Rodríguez, Nuria; Usamentiaga, Rubén; García, Daniel F.

    2016-01-01

    In the factory of the future, most of the operations will be done by autonomous robots that need visual feedback to move around the working space avoiding obstacles, to work collaboratively with humans, to identify and locate the working parts, to complete the information provided by other sensors to improve their positioning accuracy, etc. Different vision techniques, such as photogrammetry, stereo vision, structured light, time of flight and laser triangulation, among others, are widely used for inspection and quality control processes in the industry and now for robot guidance. Choosing which type of vision system to use is highly dependent on the parts that need to be located or measured. Thus, in this paper a comparative review of different machine vision techniques for robot guidance is presented. This work analyzes accuracy, range and weight of the sensors, safety, processing time and environmental influences. Researchers and developers can take it as a background information for their future works. PMID:26959030

  18. Real Time Intelligent Target Detection and Analysis with Machine Vision

    Science.gov (United States)

    Howard, Ayanna; Padgett, Curtis; Brown, Kenneth

    2000-01-01

    We present an algorithm for detecting a specified set of targets for an Automatic Target Recognition (ATR) application. ATR involves processing images for detecting, classifying, and tracking targets embedded in a background scene. We address the problem of discriminating between targets and nontarget objects in a scene by evaluating 40x40 image blocks belonging to an image. Each image block is first projected onto a set of templates specifically designed to separate images of targets embedded in a typical background scene from those background images without targets. These filters are found using directed principal component analysis which maximally separates the two groups. The projected images are then clustered into one of n classes based on a minimum distance to a set of n cluster prototypes. These cluster prototypes have previously been identified using a modified clustering algorithm based on prior sensed data. Each projected image pattern is then fed into the associated cluster's trained neural network for classification. A detailed description of our algorithm will be given in this paper. We outline our methodology for designing the templates, describe our modified clustering algorithm, and provide details on the neural network classifiers. Evaluation of the overall algorithm demonstrates that our detection rates approach 96% with a false positive rate of less than 0.03%.

  19. A New Color Constancy Model for Machine Vision

    Institute of Scientific and Technical Information of China (English)

    TAO Linmi; XU Guangyou

    2001-01-01

    Both physiological and psychological evidences suggest that the human visual system analyze images in neural subsystems tuned to different attributes of the stimulus. Color module and lightness module are such subsystems. Under this general result, a new physical model of trichromatic system has been developed to deal with the color constancy of computer vision. A normal color image is split into two images: the gray scale image and the equal lightness color image for the two modules. Relatively, a two-dimensional descriptor is applied to describe the property of surface reflectance in the equal lightness color image. This description of surface spectral reflectance has the property of color constancy. Image segmentation experiments based on color property of object show that the presented model is effective.

  20. Approximation algorithms for scheduling unrelated parallel machines with release dates

    Science.gov (United States)

    Avdeenko, T. V.; Mesentsev, Y. A.; Estraykh, I. V.

    2017-01-01

    In this paper we propose approaches to optimal scheduling of unrelated parallel machines with release dates. One approach is based on the scheme of dynamic programming modified with adaptive narrowing of search domain ensuring its computational effectiveness. We discussed complexity of the exact schedules synthesis and compared it with approximate, close to optimal, solutions. Also we explain how the algorithm works for the example of two unrelated parallel machines and five jobs with release dates. Performance results that show the efficiency of the proposed approach have been given.

  1. Linguistically motivated statistical machine translation models and algorithms

    CERN Document Server

    Xiong, Deyi

    2015-01-01

    This book provides a wide variety of algorithms and models to integrate linguistic knowledge into Statistical Machine Translation (SMT). It helps advance conventional SMT to linguistically motivated SMT by enhancing the following three essential components: translation, reordering and bracketing models. It also serves the purpose of promoting the in-depth study of the impacts of linguistic knowledge on machine translation. Finally it provides a systematic introduction of Bracketing Transduction Grammar (BTG) based SMT, one of the state-of-the-art SMT formalisms, as well as a case study of linguistically motivated SMT on a BTG-based platform.

  2. Development of a Machine Vision Fire Detection System

    Science.gov (United States)

    1994-03-01

    Incandescent Lamps 1. Quartz Tungsten Halogen 2. Sealed Beam - Automotive : 3. Headlamp a. Spotlamp b. Signal c. Light Bar d. Rotating Lights 4. Flashlight a...and pyrotechnic materials fires/explosions. B. BACKGROUND A continuing goal of the Air Force is to advance the technology of fire protection by...as shape, spectral reflectance, and material . The use of physically motivated models for algorithm development provide several important advantages

  3. Hardware Approach for Real Time Machine Stereo Vision

    Directory of Open Access Journals (Sweden)

    Michael Tornow

    2006-02-01

    Full Text Available Image processing is an effective tool for the analysis of optical sensor information for driver assistance systems and controlling of autonomous robots. Algorithms for image processing are often very complex and costly in terms of computation. In robotics and driver assistance systems, real-time processing is necessary. Signal processing algorithms must often be drastically modified so they can be implemented in the hardware. This task is especially difficult for continuous real-time processing at high speeds. This article describes a hardware-software co-design for a multi-object position sensor based on a stereophotogrammetric measuring method. In order to cover a large measuring area, an optimized algorithm based on an image pyramid is implemented in an FPGA as a parallel hardware solution for depth map calculation. Object recognition and tracking are then executed in real-time in a processor with help of software. For this task a statistical cluster method is used. Stabilization of the tracking is realized through use of a Kalman filter. Keywords: stereophotogrammetry, hardware-software co-design, FPGA, 3-d image analysis, real-time, clustering and tracking.

  4. Machine vision algorithms applied to dynamic traffic light control

    Directory of Open Access Journals (Sweden)

    Fabio Andrés Espinosa Valcárcel

    2013-01-01

    número de autos presentes en imágenes capturadas por un conjunto de cámaras estratégicamente ubicadas en cada intersección. Usando esta información, el sistema selecciona la secuencia de acciones que optimicen el flujo vehicular dentro de la zona de control, en un escenario simulado. Los resultados obtenidos muestran que el sistema disminuye en un 20% los tiempos de retraso para cada vehículo y que además es capaz de adaptarse rápida y eficientemente a los cambios de flujo.

  5. Machine vision based on the concept of contrast sensitivity of the human eye

    Science.gov (United States)

    Bezzubik, Vitali; Belashenkov, Nikolai; Vdovin, Gleb

    2014-09-01

    The model of contrast sensitivity function (CSF) of machine vision system, based on the CSF of the human visual system is proposed. By analogy with the human eye, we employ the concept of ganglion cell receptive field to the artificial light-sensitive elements. By further following this concept, we introduced quantative metrics of local and global contrast of digital image. We suggested that the contrast sensitivity threshold forms an iso-line in the parameter space contrast - spatial frequency. The model, implemented in a computer vision system, has been compared to the results of contrast sensitivity research, conducted directly with the human visual system, and demonstrated a good match.

  6. A review of RGB-LED based mixed-color illumination system for machine vision and microscopy

    Science.gov (United States)

    Hou, Lexin; Wang, Hexin; Xu, Min

    2016-09-01

    The theory and application of RGB-LED based mixed-color illumination system for use in machine vision and optical microscopy systems are presented. For machine vision system, relationship of various color sources and output image sharpness is discussed. From the viewpoint of gray scale images, evaluation and optimization methods of optimal illumination for machine vision are concluded. The image quality under monochromatic and mixed color illumination is compared. For optical microscopy system, demand of light source is introduced and design thoughts of RGB-LED based mixed-color illumination system are concluded. The problems need to be solved in this field are pointed out.

  7. Fall detection using supervised machine learning algorithms: A comparative study

    KAUST Repository

    Zerrouki, Nabil

    2017-01-05

    Fall incidents are considered as the leading cause of disability and even mortality among older adults. To address this problem, fall detection and prevention fields receive a lot of intention over the past years and attracted many researcher efforts. We present in the current study an overall performance comparison between fall detection systems using the most popular machine learning approaches which are: Naïve Bayes, K nearest neighbor, neural network, and support vector machine. The analysis of the classification power associated to these most widely utilized algorithms is conducted on two fall detection databases namely FDD and URFD. Since the performance of the classification algorithm is inherently dependent on the features, we extracted and used the same features for all classifiers. The classification evaluation is conducted using different state of the art statistical measures such as the overall accuracy, the F-measure coefficient, and the area under ROC curve (AUC) value.

  8. Novel algorithm for constructing support vector machine regression ensemble

    Institute of Scientific and Technical Information of China (English)

    Li Bo; Li Xinjun; Zhao Zhiyan

    2006-01-01

    A novel algorithm for constructing support vector machine regression ensemble is proposed. As to regression prediction, support vector machine regression(SVMR) ensemble is proposed by resampling from given training data sets repeatedly and aggregating several independent SVMRs, each of which is trained to use a replicated training set. After training, several independently trained SVMRs need to be aggregated in an appropriate combination manner. Generally, the linear weighting is usually used like expert weighting score in Boosting Regression and it is without optimization capacity. Three combination techniques are proposed, including simple arithmetic mean,linear least square error weighting and nonlinear hierarchical combining that uses another upper-layer SVMR to combine several lower-layer SVMRs. Finally, simulation experiments demonstrate the accuracy and validity of the presented algorithm.

  9. Comparison of machine learning algorithms for detecting coral reef

    Directory of Open Access Journals (Sweden)

    Eduardo Tusa

    2014-09-01

    Full Text Available (Received: 2014/07/31 - Accepted: 2014/09/23This work focuses on developing a fast coral reef detector, which is used for an autonomous underwater vehicle, AUV. A fast detection secures the AUV stabilization respect to an area of reef as fast as possible, and prevents devastating collisions. We use the algorithm of Purser et al. (2009 because of its precision. This detector has two parts: feature extraction that uses Gabor Wavelet filters, and feature classification that uses machine learning based on Neural Networks. Due to the extensive time of the Neural Networks, we exchange for a classification algorithm based on Decision Trees. We use a database of 621 images of coral reef in Belize (110 images for training and 511 images for testing. We implement the bank of Gabor Wavelets filters using C++ and the OpenCV library. We compare the accuracy and running time of 9 machine learning algorithms, whose result was the selection of the Decision Trees algorithm. Our coral detector performs 70ms of running time in comparison to 22s executed by the algorithm of Purser et al. (2009.

  10. Digital VLSI algorithms and architectures for support vector machines.

    Science.gov (United States)

    Anguita, D; Boni, A; Ridella, S

    2000-06-01

    In this paper, we propose some very simple algorithms and architectures for a digital VLSI implementation of Support Vector Machines. We discuss the main aspects concerning the realization of the learning phase of SVMs, with special attention on the effects of fixed-point math for computing and storing the parameters of the network. Some experiments on two classification problems are described that show the efficiency of the proposed methods in reaching optimal solutions with reasonable hardware requirements.

  11. Towards the compression of parton densities through machine learning algorithms

    CERN Document Server

    Carrazza, Stefano

    2016-01-01

    One of the most fascinating challenges in the context of parton density function (PDF) is the determination of the best combined PDF uncertainty from individual PDF sets. Since 2014 multiple methodologies have been developed to achieve this goal. In this proceedings we first summarize the strategy adopted by the PDF4LHC15 recommendation and then, we discuss about a new approach to Monte Carlo PDF compression based on clustering through machine learning algorithms.

  12. Behavioral Profiling of Scada Network Traffic Using Machine Learning Algorithms

    Science.gov (United States)

    2014-03-27

    encryption [37]. As an alternative to traditional classification approaches, machine learning (ML) algorithms (e.g., Naı̈ve Bayes) have successfully used...systems, and conducting physical security surveys of remote sites. Eliminating possible backdoor entry into a SCADA network can be a daunting task...notify the master of an issue. Furthermore, SCADA protocols generally lack authentication and encryption due to operating requirements and use of

  13. A two-level real-time vision machine combining coarse and fine grained parallelism

    DEFF Research Database (Denmark)

    Jensen, Lars Baunegaard With; Kjær-Nielsen, Anders; Pauwels, Karl;

    2010-01-01

    In this paper, we describe a real-time vision machine having a stereo camera as input generating visual information on two different levels of abstraction. The system provides visual low-level and mid-level information in terms of dense stereo and optical flow, egomotion, indicating areas...... a factor 90 and a reduction of latency of a factor 26 compared to processing on a single CPU--core. Since the vision machine provides generic visual information it can be used in many contexts. Currently it is used in a driver assistance context as well as in two robotic applications....... with independently moving objects as well as a condensed geometric description of the scene. The system operates at more than 20 Hz using a hybrid architecture consisting of one dual--GPU card and one quad-core CPU. The different processing stages of visual information have rather different characteristics...

  14. Quantifying nonhomogeneous colors in agricultural materials. Part II: comparison of machine vision and sensory panel evaluations.

    Science.gov (United States)

    Balaban, M O; Aparicio, J; Zotarelli, M; Sims, C

    2008-11-01

    The average colors of mangos and apples were measured using machine vision. A method to quantify the perception of nonhomogeneous colors by sensory panelists was developed. Three colors out of several reference colors and their perceived percentage of the total sample area were selected by untrained panelists. Differences between the average colors perceived by panelists and those from the machine vision were reported as DeltaE values (color difference error). Effects of nonhomogeneity of color, and using real samples or their images in the sensory panels on DeltaE were evaluated. In general, samples with more nonuniform colors had higher DeltaE values, suggesting that panelists had more difficulty in evaluating more nonhomogeneous colors. There was no significant difference in DeltaE values between the real fruits and their screen image, therefore images can be used to evaluate color instead of the real samples.

  15. Optimization on robot arm machining by using genetic algorithms

    Science.gov (United States)

    Liu, Tung-Kuan; Chen, Chiu-Hung; Tsai, Shang-En

    2007-12-01

    In this study, an optimization problem on the robot arm machining is formulated and solved by using genetic algorithms (GAs). The proposed approach adopts direct kinematics model and utilizes GA's global search ability to find the optimum solution. The direct kinematics equations of the robot arm are formulated and can be used to compute the end-effector coordinates. Based on these, the objective of optimum machining along a set of points can be evolutionarily evaluated with the distance between machining points and end-effector positions. Besides, a 3D CAD application, CATIA, is used to build up the 3D models of the robot arm, work-pieces and their components. A simulated experiment in CATIA is used to verify the computation results first and a practical control on the robot arm through the RS232 port is also performed. From the results, this approach is proved to be robust and can be suitable for most machining needs when robot arms are adopted as the machining tools.

  16. A Machine Vision System for Quality Inspection of Pine Nuts

    Directory of Open Access Journals (Sweden)

    Ikramullah Khosa

    2016-09-01

    Full Text Available Computers and artificial intelligence have penetrated in the food industry since last decade, for intellectual automatic processing and packaging in general, and in assisting for quality inspection of the food itself in particular. The food quality assessment task becomes more challenging when it is about harmless internal examination of the ingredient, and even more when its size is also minute. In this article, a method for automatic detection, extraction and classification of raw food item is presented using x-ray image data of pine nuts. Image processing techniques are employed in developing an efficient method for automatic detection and then extraction of individual ingredient, from the source x-ray image which comprises bunch of nuts in a single frame. For data representation, statistical texture analysis is carried out and attributes are calculated from each of the sample image on the global level as features. In addition co-occurrence matrices are computed from images with four different offsets, and hence more features are extracted by using them. To find fewer meaningful characteristics, all the calculated features are organized in several combinations and then tested. Seventy percent of image data is used for training and 15% each for cross-validation and test purposes. Binary classification is performed using two state-of-the-art non-linear classifiers: Artificial Neural Network (ANN and Support Vector Machines (SVM. Performance is evaluated in terms of classification accuracy, specificity and sensitivity. ANN classifier showed 87.6% accuracy with correct recognition rate of healthy nuts and unhealthy nuts as 94% and 62% respectively. SVM classifier produced the similar accuracy achieving 86.3% specificity and 89.2% sensitivity rate. The results obtained are unique itself in terms of ingredient and promising relatively. It is also found that feature set size can be reduced up to 57% by compromising 3.5% accuracy, in combination with

  17. Online tomato sorting based on shape, maturity, size, and surface defects using machine vision

    OpenAIRE

    ARJENAKI, Omid Omidi; MOGHADDAM, Parviz Ahmadi; MOTLAGH, Asad Moddares

    2013-01-01

    Online sorting of tomatoes according to their features is an important postharvest procedure. The purpose of this research was to develop an efficient machine vision-based experimental sorting system for tomatoes. Relevant sorting parameters included shape (oblong and circular), size (small and large), maturity (color), and defects. The variables defining shape, maturity, and size of the tomatoes were eccentricity, average of color components, and 2-D pixel area, respectively. Tomato defects ...

  18. Real-time machine vision FPGA implementation for microfluidic monitoring on Lab-on-Chips.

    Science.gov (United States)

    Sotiropoulou, Calliope-Louisa; Voudouris, Liberis; Gentsos, Christos; Demiris, Athanasios M; Vassiliadis, Nikolaos; Nikolaidis, Spyridon

    2014-04-01

    A machine vision implementation on a field-programmable gate array (FPGA) device for real-time microfluidic monitoring on Lab-On-Chips is presented in this paper. The machine vision system is designed to follow continuous or plug flows, for which the menisci of the fluids are always visible. The system discriminates between the front or "head" of the flow and the back or "tail" and is able to follow flows with a maximum speed of 20 mm/sec in circular channels of a diameter of 200 μm (corresponding to approx. 60 μl/sec ). It is designed to be part of a complete Point-of-Care system, which will be portable and operate in non-ideal laboratory conditions. Thus, it is able to cope with noise due to lighting conditions and small LoC displacements during the experiment execution. The machine vision system can be used for a variety of LoC devices, without the need for fiducial markers (such as redundancy patterns) for its operation. The underlying application requirements called for a complete hardware implementation. The architecture uses a variety of techniques to improve performance and minimize memory access requirements. The system input is 8 bit grayscale uncompressed video of up to 1 Mpixel resolution. The system uses an operating frequency of 170 Mhz and achieves a computational time of 13.97 ms (worst case), which leads to a throughput of 71.6 fps for 1 Mpixel video resolution.

  19. Prediction of shelled shrimp weight by machine vision.

    Science.gov (United States)

    Pan, Peng-min; Li, Jian-ping; Lv, Gu-lai; Yang, Hui; Zhu, Song-ming; Lou, Jian-zhong

    2009-08-01

    The weight of shelled shrimp is an important parameter for grading process. The weight prediction of shelled shrimp by contour area is not accurate enough because of the ignorance of the shrimp thickness. In this paper, a multivariate prediction model containing area, perimeter, length, and width was established. A new calibration algorithm for extracting length of shelled shrimp was proposed, which contains binary image thinning, branch recognition and elimination, and length reconstruction, while its width was calculated during the process of length extracting. The model was further validated with another set of images from 30 shelled shrimps. For a comparison purpose, artificial neural network (ANN) was used for the shrimp weight predication. The ANN model resulted in a better prediction accuracy (with the average relative error at 2.67%), but took a tenfold increase in calculation time compared with the weight-area-perimeter (WAP) model (with the average relative error at 3.02%). We thus conclude that the WAP model is a better method for the prediction of the weight of shelled red shrimp.

  20. Prediction of shelled shrimp weight by machine vision

    Institute of Scientific and Technical Information of China (English)

    Peng-min PAN; Jian-ping LI; Gu-lai LV; Hui YANG; Song-ming ZHU; Jian-zhong LOU

    2009-01-01

    The weight of shelled shrimp is an important parameter for grading process. The weight prediction of shelled shrimp by contour area is not accurate enough because of the ignorance of the shrimp thickness. In this paper, a multivariate prediction model containing area, perimeter, length, and width was established. A new calibration algorithm for extracting length of shelled shrimp was proposed, which contains binary image thinning, branch recognition and elimination, and length reconstruction, while its width was calculated during the process of length extracting. The model was further validated with another set of images from 30 shelled shrimps. For a comparison purpose, artificial neural network (ANN) was used for the shrimp weight predication. The ANN model resulted in a better prediction accuracy (with the average relative error at 2.67%), but took a tenfold increase in calculation time compared with the weight-area-perimeter (WAP) model (with the average relative error at 3.02%). We thus conclude that the WAP model is a better method for the prediction of the weight of shelled red shrimp.

  1. The Obstacle Detection and Measurement Based on Machine Vision

    Directory of Open Access Journals (Sweden)

    Xitao Zheng

    2010-12-01

    Full Text Available To develop a quick obstacle detection and measurement algorithm for the image-based autonomous vehicle (AV or computer assisted driving system, this paper utilize the previous work of object detection to get the position of an obstacle and refocus windows on the selected target. Further calculation based on single camera will give the detailed measurement of the object, like the height, the distance to the vehicle, and possibly the width. It adopts a two camera system with different pitch angles, which can perform real-time monitoring for the front area of the vehicle with different coverage. This paper assumes that the vehicle will move at an even speed on a flat road, cameras will sample images at a given rate and the images will be analyzed simultaneously. Focus will be on the virtual window area of the image which is proved to be related to the distance to the object and speed of the vehicle. Counting of the blackened virtual sub-area can quickly find the existence of an obstacle and the obstacle area will be cut to get the interested parameter measurements for the object evaluation.

  2. Machine Vision System Design Method%机器视觉系统的设计方法

    Institute of Scientific and Technical Information of China (English)

    王运哲; 白雁兵; 张博

    2011-01-01

    文章主要介绍了机器视觉系统的概念和发展历程,介绍了机器视觉的组成和基本原理,从工业摄像机、镜头、光源、图像采集卡几方面详细阐述了机器视觉系统的设计要点、分类、选型。%The article introduces the conception and the developmental process of machine vision system, the component and fundamental theory of machine vision, expatiates on main points of designing machine vision system, classifying, choosing type, enumerates the most of manufacturers in the field of machine vision system in china.

  3. Algorithms for single machine scheduling with availability constraints

    Institute of Scientific and Technical Information of China (English)

    LI Bo; SHI Bing-xin; SHEN Bin; LIU Ji-cheng

    2005-01-01

    It is a NP-hard problem to schedule a list of nonresumable jobs to the available intervals of an availability-constrained single machine to minimize the scheduling length. This paper transformed this scheduling problem into a variant of the variable-sized bin packing problem, put forward eight bin packing algorithms adapted from the classic one-dimensional bin packing problem and investigated their performances from both of the worst-case and the average-case scenarios. Analytical results show that the worst-case performance ratios of the algorithms are not less than 2. Experimental results for average cases show that the Best Fit and the Best Fit Decreasing algorithm outperform any others for independent and precedence-constrained jobs respectively.

  4. Alignment of Custom Standards by Machine Learning Algorithms

    Directory of Open Access Journals (Sweden)

    Adela Sirbu

    2010-09-01

    Full Text Available Building an efficient model for automatic alignment of terminologies would bring a significant improvement to the information retrieval process. We have developed and compared two machine learning based algorithms whose aim is to align 2 custom standards built on a 3 level taxonomy, using kNN and SVM classifiers that work on a vector representation consisting of several similarity measures. The weights utilized by the kNN were optimized with an evolutionary algorithm, while the SVM classifier's hyper-parameters were optimized with a grid search algorithm. The database used for train was semi automatically obtained by using the Coma++ tool. The performance of our aligners is shown by the results obtained on the test set.

  5. Selection of parameters for advanced machining processes using firefly algorithm

    Directory of Open Access Journals (Sweden)

    Rajkamal Shukla

    2017-02-01

    Full Text Available Advanced machining processes (AMPs are widely utilized in industries for machining complex geometries and intricate profiles. In this paper, two significant processes such as electric discharge machining (EDM and abrasive water jet machining (AWJM are considered to get the optimum values of responses for the given range of process parameters. The firefly algorithm (FA is attempted to the considered processes to obtain optimized parameters and the results obtained are compared with the results given by previous researchers. The variation of process parameters with respect to the responses are plotted to confirm the optimum results obtained using FA. In EDM process, the performance parameter “MRR” is increased from 159.70 gm/min to 181.6723 gm/min, while “Ra” and “REWR” are decreased from 6.21 μm to 3.6767 μm and 6.21% to 6.324 × 10−5% respectively. In AWJM process, the value of the “kerf” and “Ra” are decreased from 0.858 mm to 0.3704 mm and 5.41 mm to 4.443 mm respectively. In both the processes, the obtained results show a significant improvement in the responses.

  6. METHODS OF ASSESSING THE DEGREE OF DESTRUCTION OF RUBBER PRODUCTS USING COMPUTER VISION ALGORITHMS

    Directory of Open Access Journals (Sweden)

    A. A. Khvostov

    2015-01-01

    Full Text Available For technical inspection of rubber products are essential methods of improving video scopes analyzing the degree of destruction and aging of rubber in an aggressive environment. The main factor determining the degree of destruction of the rubber product, the degree of coverage is cracked, which can be described as the amount of the total area, perimeter cracks, geometric shapes and other parameters. In the process of creating a methodology for assessing the degree of destruction of rubber products arises the problem of the development of machine vision algorithm for estimating the degree of coverage of the sample fractures and fracture characterization. For the development of image processing algorithm performed experimental studies on the artificial aging of several samples of products that are made from different rubbers. In the course of the experiments it was obtained several samples of shots vulcanizates in real time. To achieve the goals initially made light stabilization of array images using Gaussian filter. Thereafter, for each image binarization operation is applied. To highlight the contours of the surface damage of the sample is used Canny algorithm. The detected contours are converted into an array of pixels. However, a crack may be allocated to several contours. Therefore, an algorithm was developed by combining contours criterion of minimum distance between them. At the end of the calculation is made of the morphological features of each contour (area, perimeter, length, width, angle of inclination, the At the end of the calculation is made of the morphological features of each contour (area, perimeter, length, width, angle of inclination, the Minkowski dimension. Show schedule obtained by the method parameters destruction of samples of rubber products. The developed method allows you to automate assessment of the degree of aging of rubber products in telemetry systems, to study the dynamics of the aging process of polymers to

  7. A Comparison of the Effects of K-Anonymity on Machine Learning Algorithms

    Directory of Open Access Journals (Sweden)

    Hayden Wimmer

    2014-11-01

    Full Text Available While research has been conducted in machine learning algorithms and in privacy preserving in data mining (PPDM, a gap in the literature exists which combines the aforementioned areas to determine how PPDM affects common machine learning algorithms. The aim of this research is to narrow this literature gap by investigating how a common PPDM algorithm, K-Anonymity, affects common machine learning and data mining algorithms, namely neural networks, logistic regression, decision trees, and Bayesian classifiers. This applied research reveals practical implications for applying PPDM to data mining and machine learning and serves as a critical first step learning how to apply PPDM to machine learning algorithms and the effects of PPDM on machine learning. Results indicate that certain machine learning algorithms are more suited for use with PPDM techniques.

  8. A Semisupervised Support Vector Machines Algorithm for BCI Systems

    Directory of Open Access Journals (Sweden)

    Jianzhao Qin

    2007-07-01

    Full Text Available As an emerging technology, brain-computer interfaces (BCIs bring us new communication interfaces which translate brain activities into control signals for devices like computers, robots, and so forth. In this study, we propose a semisupervised support vector machine (SVM algorithm for brain-computer interface (BCI systems, aiming at reducing the time-consuming training process. In this algorithm, we apply a semisupervised SVM for translating the features extracted from the electrical recordings of brain into control signals. This SVM classifier is built from a small labeled data set and a large unlabeled data set. Meanwhile, to reduce the time for training semisupervised SVM, we propose a batch-mode incremental learning method, which can also be easily applied to the online BCI systems. Additionally, it is suggested in many studies that common spatial pattern (CSP is very effective in discriminating two different brain states. However, CSP needs a sufficient labeled data set. In order to overcome the drawback of CSP, we suggest a two-stage feature extraction method for the semisupervised learning algorithm. We apply our algorithm to two BCI experimental data sets. The offline data analysis results demonstrate the effectiveness of our algorithm.

  9. How the machine ‘thinks’: Understanding opacity in machine learning algorithms

    Directory of Open Access Journals (Sweden)

    Jenna Burrell

    2016-01-01

    Full Text Available This article considers the issue of opacity as a problem for socially consequential mechanisms of classification and ranking, such as spam filters, credit card fraud detection, search engines, news trends, market segmentation and advertising, insurance or loan qualification, and credit scoring. These mechanisms of classification all frequently rely on computational algorithms, and in many cases on machine learning algorithms to do this work. In this article, I draw a distinction between three forms of opacity: (1 opacity as intentional corporate or state secrecy, (2 opacity as technical illiteracy, and (3 an opacity that arises from the characteristics of machine learning algorithms and the scale required to apply them usefully. The analysis in this article gets inside the algorithms themselves. I cite existing literatures in computer science, known industry practices (as they are publicly presented, and do some testing and manipulation of code as a form of lightweight code audit. I argue that recognizing the distinct forms of opacity that may be coming into play in a given application is a key to determining which of a variety of technical and non-technical solutions could help to prevent harm.

  10. How the machine ‘thinks’: Understanding opacity in machine learning algorithms

    Directory of Open Access Journals (Sweden)

    Jenna Burrell

    2016-01-01

    Full Text Available This article considers the issue of opacity as a problem for socially consequential mechanisms of classification and ranking, such as spam filters, credit card fraud detection, search engines, news trends, market segmentation and advertising, insurance or loan qualification, and credit scoring. These mechanisms of classification all frequently rely on computational algorithms, and in many cases on machine learning algorithms to do this work. In this article, I draw a distinction between three forms of opacity: (1 opacity as intentional corporate or state secrecy, (2 opacity as technical illiteracy, and (3 an opacity that arises from the characteristics of machine learning algorithms and the scale required to apply them usefully. The analysis in this article gets inside the algorithms themselves. I cite existing literatures in computer science, known industry practices (as they are publicly presented, and do some testing and manipulation of code as a form of lightweight code audit. I argue that recognizing the distinct forms of opacity that may be coming into play in a given application is a key to determining which of a variety of technical and non-technical solutions could help to prevent harm.

  11. A bio-inspired apposition compound eye machine vision sensor system.

    Science.gov (United States)

    Davis, J D; Barrett, S F; Wright, C H G; Wilcox, M

    2009-12-01

    The Wyoming Information, Signal Processing, and Robotics Laboratory is developing a wide variety of bio-inspired vision sensors. We are interested in exploring the vision system of various insects and adapting some of their features toward the development of specialized vision sensors. We do not attempt to supplant traditional digital imaging techniques but rather develop sensor systems tailor made for the application at hand. We envision that many applications may require a hybrid approach using conventional digital imaging techniques enhanced with bio-inspired analogue sensors. In this specific project, we investigated the apposition compound eye and its characteristics commonly found in diurnal insects and certain species of arthropods. We developed and characterized an array of apposition compound eye-type sensors and tested them on an autonomous robotic vehicle. The robot exhibits the ability to follow a pre-defined target and avoid specified obstacles using a simple control algorithm.

  12. A bio-inspired apposition compound eye machine vision sensor system

    Energy Technology Data Exchange (ETDEWEB)

    Davis, J D [Applied Research Laboratories, University of Texas, 10000 Burnet Rd, Austin, TX 78757 (United States); Barrett, S F; Wright, C H G [Electrical and Computer Engineering, University of Wyoming, Dept 3295 1000 E. University Ave, Laramie, WY 82071 (United States); Wilcox, M, E-mail: steveb@uwyo.ed [Department of Biology, United States Air Force Academy, CO 80840 (United States)

    2009-12-15

    The Wyoming Information, Signal Processing, and Robotics Laboratory is developing a wide variety of bio-inspired vision sensors. We are interested in exploring the vision system of various insects and adapting some of their features toward the development of specialized vision sensors. We do not attempt to supplant traditional digital imaging techniques but rather develop sensor systems tailor made for the application at hand. We envision that many applications may require a hybrid approach using conventional digital imaging techniques enhanced with bio-inspired analogue sensors. In this specific project, we investigated the apposition compound eye and its characteristics commonly found in diurnal insects and certain species of arthropods. We developed and characterized an array of apposition compound eye-type sensors and tested them on an autonomous robotic vehicle. The robot exhibits the ability to follow a pre-defined target and avoid specified obstacles using a simple control algorithm.

  13. Machine learning based global particle indentification algorithms at LHCb experiment

    CERN Multimedia

    Derkach, Denis; Likhomanenko, Tatiana; Rogozhnikov, Aleksei; Ratnikov, Fedor

    2017-01-01

    One of the most important aspects of data processing at LHC experiments is the particle identification (PID) algorithm. In LHCb, several different sub-detector systems provide PID information: the Ring Imaging CHerenkov (RICH) detector, the hadronic and electromagnetic calorimeters, and the muon chambers. To improve charged particle identification, several neural networks including a deep architecture and gradient boosting have been applied to data. These new approaches provide higher identification efficiencies than existing implementations for all charged particle types. It is also necessary to achieve a flat dependency between efficiencies and spectator variables such as particle momentum, in order to reduce systematic uncertainties during later stages of data analysis. For this purpose, "flat” algorithms that guarantee the flatness property for efficiencies have also been developed. This talk presents this new approach based on machine learning and its performance.

  14. A Review of Machine-Vision-Based Analysis of Wireless Capsule Endoscopy Video

    Directory of Open Access Journals (Sweden)

    Yingju Chen

    2012-01-01

    Full Text Available Wireless capsule endoscopy (WCE enables a physician to diagnose a patient's digestive system without surgical procedures. However, it takes 1-2 hours for a gastroenterologist to examine the video. To speed up the review process, a number of analysis techniques based on machine vision have been proposed by computer science researchers. In order to train a machine to understand the semantics of an image, the image contents need to be translated into numerical form first. The numerical form of the image is known as image abstraction. The process of selecting relevant image features is often determined by the modality of medical images and the nature of the diagnoses. For example, there are radiographic projection-based images (e.g., X-rays and PET scans, tomography-based images (e.g., MRT and CT scans, and photography-based images (e.g., endoscopy, dermatology, and microscopic histology. Each modality imposes unique image-dependent restrictions for automatic and medically meaningful image abstraction processes. In this paper, we review the current development of machine-vision-based analysis of WCE video, focusing on the research that identifies specific gastrointestinal (GI pathology and methods of shot boundary detection.

  15. Three-dimensional microscope vision system based on micro laser line scanning and adaptive genetic algorithms

    Science.gov (United States)

    Apolinar, J.; Rodríguez, Muñoz

    2017-02-01

    A microscope vision system to retrieve small metallic surface via micro laser line scanning and genetic algorithms is presented. In this technique, a 36 μm laser line is projected on the metallic surface through a laser diode head, which is placed to a small distance away from the target. The micro laser line is captured by a CCD camera, which is attached to the microscope. The surface topography is computed by triangulation by means of the line position and microscope vision parameters. The calibration of the microscope vision system is carried out by an adaptive genetic algorithm based on the line position. In this algorithm, an objective function is constructed from the microscope geometry to determine the microscope vision parameters. Also, the genetic algorithm provides the search space to calculate the microscope vision parameters with high accuracy in fast form. This procedure avoids errors produced by the missing of references and physical measurements, which are employed by the traditional microscope vision systems. The contribution of the proposed system is corroborated by an evaluation via accuracy and speed of the traditional microscope vision systems, which retrieve micro-scale surface topography.

  16. A Study on the Optimization Performance of Fireworks and Cuckoo Search Algorithms in Laser Machining Processes

    Science.gov (United States)

    Goswami, D.; Chakraborty, S.

    2014-11-01

    Laser machining is a promising non-contact process for effective machining of difficult-to-process advanced engineering materials. Increasing interest in the use of lasers for various machining operations can be attributed to its several unique advantages, like high productivity, non-contact processing, elimination of finishing operations, adaptability to automation, reduced processing cost, improved product quality, greater material utilization, minimum heat-affected zone and green manufacturing. To achieve the best desired machining performance and high quality characteristics of the machined components, it is extremely important to determine the optimal values of the laser machining process parameters. In this paper, fireworks algorithm and cuckoo search (CS) algorithm are applied for single as well as multi-response optimization of two laser machining processes. It is observed that although almost similar solutions are obtained for both these algorithms, CS algorithm outperforms fireworks algorithm with respect to average computation time, convergence rate and performance consistency.

  17. Study on the Fruit Grading Recognition System Based on Machine Vision

    Directory of Open Access Journals (Sweden)

    Huan Ma

    2015-07-01

    Full Text Available The study proposed that the current development of fruit industry requires the fast and efficient methods to test the varieties of fruits, which can combine the image processing and computer machine vision technology together to be applied in the field of fruit varieties detection, so as to be consistent with this new trend. At present, the determination of these traits were mainly depended on visual grading and manual measurement, which existed the problems such as: slow speed, low accuracy and poor objectivity and so on.

  18. On-line welding quality inspection system for steel pipe based on machine vision

    Science.gov (United States)

    Yang, Yang

    2017-05-01

    In recent years, high frequency welding has been widely used in production because of its advantages of simplicity, reliability and high quality. In the production process, how to effectively control the weld penetration welding, ensure full penetration, weld uniform, so as to ensure the welding quality is to solve the problem of the present stage, it is an important research field in the field of welding technology. In this paper, based on the study of some methods of welding inspection, a set of on-line welding quality inspection system based on machine vision is designed.

  19. Tensor Voting A Perceptual Organization Approach to Computer Vision and Machine Learning

    CERN Document Server

    Mordohai, Philippos

    2006-01-01

    This lecture presents research on a general framework for perceptual organization that was conducted mainly at the Institute for Robotics and Intelligent Systems of the University of Southern California. It is not written as a historical recount of the work, since the sequence of the presentation is not in chronological order. It aims at presenting an approach to a wide range of problems in computer vision and machine learning that is data-driven, local and requires a minimal number of assumptions. The tensor voting framework combines these properties and provides a unified perceptual organiza

  20. Vision Algorithm for the Solar Aspect System of the High Energy Replicated Optics to Explore the Sun Mission

    Science.gov (United States)

    Cramer, Alexander Krishnan

    2014-01-01

    This work covers the design and test of a machine vision algorithm for generating high- accuracy pitch and yaw pointing solutions relative to the sun on a high altitude balloon. It describes how images were constructed by focusing an image of the sun onto a plate printed with a pattern of small cross-shaped fiducial markers. Images of this plate taken with an off-the-shelf camera were processed to determine relative position of the balloon payload to the sun. The algorithm is broken into four problems: circle detection, fiducial detection, fiducial identification, and image registration. Circle detection is handled by an "Average Intersection" method, fiducial detection by a matched filter approach, and identification with an ad-hoc method based on the spacing between fiducials. Performance is verified on real test data where possible, but otherwise uses artificially generated data. Pointing knowledge is ultimately verified to meet the 20 arcsecond requirement.

  1. Optimal Machine Tools Selection Using Interval-Valued Data FCM Clustering Algorithm

    Directory of Open Access Journals (Sweden)

    Yupeng Xin

    2014-01-01

    Full Text Available Machine tool selection directly affects production rates, accuracy, and flexibility. In order to quickly and accurately select the appropriate machine tools in machining process planning, this paper proposes an optimal machine tools selection method based on interval-valued data fuzzy C-means (FCM clustering algorithm. We define the machining capability meta (MAE as the smallest unit to describe machining capacity of machine tools and establish MAE library based on the MAE information model. According to the manufacturing process requirements, the MAEs can be queried from MAE library. Subsequently, interval-valued data FCM algorithm is used to select the appropriate machine tools for manufacturing process. Through computing matching degree between manufacturing process machining constraints and MAEs, we get the most appropriate MAEs and the corresponding machine tools. Finally, a case study of an exhaust duct part of the aeroengine is presented to demonstrate the applicability of the proposed method.

  2. Research on stereo vision path-planning algorithms for mobile robots autonomous navigation

    Institute of Scientific and Technical Information of China (English)

    ZHANG Guo-wei; LU Qiu-hong

    2009-01-01

    Using stereo vision for autonomous mobile robot path-planning is a hot technology. The environment mapping and path-planning algorithms were introduced, and they were applied in the autonomous mobile robot experiment platform. Through experiments in the robot platform, the effectiveness of these algorithms was verified.

  3. New vision system and navigation algorithm for an autonomous ground vehicle

    Science.gov (United States)

    Tann, Hokchhay; Shakya, Bicky; Merchen, Alex C.; Williams, Benjamin C.; Khanal, Abhishek; Zhao, Jiajia; Ahlgren, David J.

    2013-12-01

    Improvements were made to the intelligence algorithms of an autonomously operating ground vehicle, Q, which competed in the 2013 Intelligent Ground Vehicle Competition (IGVC). The IGVC required the vehicle to first navigate between two white lines on a grassy obstacle course, then pass through eight GPS waypoints, and pass through a final obstacle field. Modifications to Q included a new vision system with a more effective image processing algorithm for white line extraction. The path-planning algorithm adopted the vision system, creating smoother, more reliable navigation. With these improvements, Q successfully completed the basic autonomous navigation challenge, finishing tenth out of over 50 teams.

  4. Application of generalized Hough transform for detecting sugar beet plant from weed using machine vision method

    Directory of Open Access Journals (Sweden)

    A Bakhshipour Ziaratgahi

    2017-05-01

    Full Text Available Introduction Sugar beet (Beta vulgaris L. as the second most important world’s sugar source after sugarcane is one of the major industrial crops. The presence of weeds in sugar beet fields, especially at early growth stages, results in a substantial decrease in the crop yield. It is very important to efficiently eliminate weeds at early growing stages. The first step of precision weed control is accurate detection of weeds location in the field. This operation can be performed by machine vision techniques. Hough transform is one of the shape feature extraction methods for object tracking in image processing which is basically used to identify lines or other geometrical shapes in an image. Generalized Hough transform (GHT is a modified version of the Hough transform used not only for geometrical forms, but also for detecting any arbitrary shape. This method is based on a pattern matching principle that uses a set of vectors of feature points (usually object edge points to a reference point to construct a pattern. By comparing this pattern with a set pattern, the desired shape is detected. The aim of this study was to identify the sugar beet plant from some common weeds in a field using the GHT. Materials and Methods Images required for this study were taken at the four-leaf stage of sugar beet as the beginning of the critical period of weed control. A shelter was used to avoid direct sunlight and prevent leaf shadows on each other. The obtained images were then introduced to the Image Processing Toolbox of MATLAB programming software for further processing. Green and Red color components were extracted from primary RGB images. In the first step, binary images were obtained by applying the optimal threshold on the G-R images. A comprehensive study of several sugar beet images revealed that there is a unique feature in sugar beet leaves which makes them differentiable from the weeds. The feature observed in all sugar beet plants at the four

  5. Protein sequence classification with improved extreme learning machine algorithms.

    Science.gov (United States)

    Cao, Jiuwen; Xiong, Lianglin

    2014-01-01

    Precisely classifying a protein sequence from a large biological protein sequences database plays an important role for developing competitive pharmacological products. Comparing the unseen sequence with all the identified protein sequences and returning the category index with the highest similarity scored protein, conventional methods are usually time-consuming. Therefore, it is urgent and necessary to build an efficient protein sequence classification system. In this paper, we study the performance of protein sequence classification using SLFNs. The recent efficient extreme learning machine (ELM) and its invariants are utilized as the training algorithms. The optimal pruned ELM is first employed for protein sequence classification in this paper. To further enhance the performance, the ensemble based SLFNs structure is constructed where multiple SLFNs with the same number of hidden nodes and the same activation function are used as ensembles. For each ensemble, the same training algorithm is adopted. The final category index is derived using the majority voting method. Two approaches, namely, the basic ELM and the OP-ELM, are adopted for the ensemble based SLFNs. The performance is analyzed and compared with several existing methods using datasets obtained from the Protein Information Resource center. The experimental results show the priority of the proposed algorithms.

  6. [Development of a new position-recognition system for robotic radiosurgery systems using machine vision].

    Science.gov (United States)

    Mohri, Issai; Umezu, Yoshiyuki; Fukunaga, Junnichi; Tane, Hiroyuki; Nagata, Hironori; Hirashima, Hideaki; Nakamura, Katsumasa; Hirata, Hideki

    2014-08-01

    CyberKnife(®) provides continuous guidance through radiography, allowing instantaneous X-ray images to be obtained; it is also equipped with 6D adjustment for patient setup. Its disadvantage is that registration is carried out just before irradiation, making it impossible to perform stereo-radiography during irradiation. In addition, patient movement cannot be detected during irradiation. In this study, we describe a new registration system that we term "Machine Vision," which subjects the patient to no additional radiation exposure for registration purposes, can be set up promptly, and allows real-time registration during irradiation. Our technique offers distinct advantages over CyberKnife by enabling a safer and more precise mode of treatment. "Machine Vision," which we have designed and fabricated, is an automatic registration system that employs three charge coupled device cameras oriented in different directions that allow us to obtain a characteristic depiction of the shape of both sides of the fetal fissure and external ears in a human head phantom. We examined the degree of precision of this registration system and concluded it to be suitable as an alternative method of registration without radiation exposure when displacement is less than 1.0 mm in radiotherapy. It has potential for application to CyberKnife in clinical treatment.

  7. Vision-Based Perception and Classification of Mosquitoes Using Support Vector Machine

    Directory of Open Access Journals (Sweden)

    Masataka Fuchida

    2017-01-01

    Full Text Available The need for a novel automated mosquito perception and classification method is becoming increasingly essential in recent years, with steeply increasing number of mosquito-borne diseases and associated casualties. There exist remote sensing and GIS-based methods for mapping potential mosquito inhabitants and locations that are prone to mosquito-borne diseases, but these methods generally do not account for species-wise identification of mosquitoes in closed-perimeter regions. Traditional methods for mosquito classification involve highly manual processes requiring tedious sample collection and supervised laboratory analysis. In this research work, we present the design and experimental validation of an automated vision-based mosquito classification module that can deploy in closed-perimeter mosquito inhabitants. The module is capable of identifying mosquitoes from other bugs such as bees and flies by extracting the morphological features, followed by support vector machine-based classification. In addition, this paper presents the results of three variants of support vector machine classifier in the context of mosquito classification problem. This vision-based approach to the mosquito classification problem presents an efficient alternative to the conventional methods for mosquito surveillance, mapping and sample image collection. Experimental results involving classification between mosquitoes and a predefined set of other bugs using multiple classification strategies demonstrate the efficacy and validity of the proposed approach with a maximum recall of 98%.

  8. Machine Vision Analysis of Characteristics and Image Information Base Construction for Hybrid Rice Seed

    Institute of Scientific and Technical Information of China (English)

    2005-01-01

    Incompletely closed glumes, germination on panicle and disease ara thrae important factors causing poor seed quality of hybrid rice. To determine how many and which categories should be classified to meet the demand for seed in rice production, the effects of various degrees of incompletely closed glumes, germination on panicle and disease on germination percentage at the harvest and after storage for six months were studied by standard germination percentage test. Six categories of seeds with germ (germinated seeds), severe disease, incompletely closed glumes, spot disease, fine fissure and normal seeds were inspected and then treated separately. Images of the five hybrid rice seed (Jinyou 402, Shanyou 10, Zhongyou 27, Jiayou 99 and Ⅱ you 3207) were acquired with a self-developed machine vision system. Each image could be processed to get the feature values of seed region such as length, width, ratio of length to width, araa, solidity and hue. Then all the images of normal seeds were calculated to draw the feature value ranges of each hybrid rice variety. Finally, an image information base that stores typical images and related feature values of each variety was established. This image information base can help us to identify the classification limit of characteristics, and provide the reference of the threshold selection. The management of large numbers of pictures and the addition of new varieties have been supported. The research laid a foundation for extracting image features of hybrid rice seed, which is a key approach to futura quality inspection with machine vision.

  9. A comparative study of fast dense stereo vision algorithms

    NARCIS (Netherlands)

    Sunyoto, H.; Mark, W. van der; Gavrila, D.M.

    2004-01-01

    With recent hardware advances, real-time dense stereo vision becomes increasingly feasible for general-purpose processors. This has important benefits for the intelligent vehicles domain, alleviating object segmentation problems when sensing complex, cluttered traffic scenes. In this paper, we prese

  10. Understanding Neural Networks for Machine Learning using Microsoft Neural Network Algorithm

    National Research Council Canada - National Science Library

    Nagesh Ramprasad

    2016-01-01

    .... In this research, focus is on the Microsoft Neural System Algorithm. The Microsoft Neural System Algorithm is a simple implementation of the adaptable and popular neural networks that are used in the machine learning...

  11. Sparsity-based algorithm for detecting faults in rotating machines

    Science.gov (United States)

    He, Wangpeng; Ding, Yin; Zi, Yanyang; Selesnick, Ivan W.

    2016-05-01

    This paper addresses the detection of periodic transients in vibration signals so as to detect faults in rotating machines. For this purpose, we present a method to estimate periodic-group-sparse signals in noise. The method is based on the formulation of a convex optimization problem. A fast iterative algorithm is given for its solution. A simulated signal is formulated to verify the performance of the proposed approach for periodic feature extraction. The detection performance of comparative methods is compared with that of the proposed approach via RMSE values and receiver operating characteristic (ROC) curves. Finally, the proposed approach is applied to single fault diagnosis of a locomotive bearing and compound faults diagnosis of motor bearings. The processed results show that the proposed approach can effectively detect and extract the useful features of bearing outer race and inner race defect.

  12. Modeling the Swift BAT Trigger Algorithm with Machine Learning

    Science.gov (United States)

    Graff, Philip B.; Lien, Amy Y.; Baker, John G.; Sakamoto, Takanori

    2015-01-01

    To draw inferences about gamma-ray burst (GRB) source populations based on Swift observations, it is essential to understand the detection efficiency of the Swift burst alert telescope (BAT). This study considers the problem of modeling the Swift BAT triggering algorithm for long GRBs, a computationally expensive procedure, and models it using machine learning algorithms. A large sample of simulated GRBs from Lien et al. (2014) is used to train various models: random forests, boosted decision trees (with AdaBoost), support vector machines, and artificial neural networks. The best models have accuracies of approximately greater than 97% (approximately less than 3% error), which is a significant improvement on a cut in GRB flux which has an accuracy of 89:6% (10:4% error). These models are then used to measure the detection efficiency of Swift as a function of redshift z, which is used to perform Bayesian parameter estimation on the GRB rate distribution. We find a local GRB rate density of eta(sub 0) approximately 0.48(+0.41/-0.23) Gpc(exp -3) yr(exp -1) with power-law indices of eta(sub 1) approximately 1.7(+0.6/-0.5) and eta(sub 2) approximately -5.9(+5.7/-0.1) for GRBs above and below a break point of z(sub 1) approximately 6.8(+2.8/-3.2). This methodology is able to improve upon earlier studies by more accurately modeling Swift detection and using this for fully Bayesian model fitting. The code used in this is analysis is publicly available online.

  13. Modeling the Swift Bat Trigger Algorithm with Machine Learning

    Science.gov (United States)

    Graff, Philip B.; Lien, Amy Y.; Baker, John G.; Sakamoto, Takanori

    2016-01-01

    To draw inferences about gamma-ray burst (GRB) source populations based on Swift observations, it is essential to understand the detection efficiency of the Swift burst alert telescope (BAT). This study considers the problem of modeling the Swift / BAT triggering algorithm for long GRBs, a computationally expensive procedure, and models it using machine learning algorithms. A large sample of simulated GRBs from Lien et al. is used to train various models: random forests, boosted decision trees (with AdaBoost), support vector machines, and artificial neural networks. The best models have accuracies of greater than or equal to 97 percent (less than or equal to 3 percent error), which is a significant improvement on a cut in GRB flux, which has an accuracy of 89.6 percent (10.4 percent error). These models are then used to measure the detection efficiency of Swift as a function of redshift z, which is used to perform Bayesian parameter estimation on the GRB rate distribution. We find a local GRB rate density of n (sub 0) approaching 0.48 (sup plus 0.41) (sub minus 0.23) per cubic gigaparsecs per year with power-law indices of n (sub 1) approaching 1.7 (sup plus 0.6) (sub minus 0.5) and n (sub 2) approaching minus 5.9 (sup plus 5.7) (sub minus 0.1) for GRBs above and below a break point of z (redshift) (sub 1) approaching 6.8 (sup plus 2.8) (sub minus 3.2). This methodology is able to improve upon earlier studies by more accurately modeling Swift detection and using this for fully Bayesian model fitting.

  14. MODIS Aerosol Optical Depth Bias Adjustment Using Machine Learning Algorithms

    Science.gov (United States)

    Albayrak, A.; Wei, J. C.; Petrenko, M.; Lary, D. J.; Leptoukh, G. G.

    2011-12-01

    Over the past decade, global aerosol observations have been conducted by space-borne sensors, airborne instruments, and ground-base network measurements. Unfortunately, quite often we encounter the differences of aerosol measurements by different well-calibrated instruments, even with a careful collocation in time and space. The differences might be rather substantial, and need to be better understood and accounted for when merging data from many sensors. The possible causes for these differences come from instrumental bias, different satellite viewing geometries, calibration issues, dynamically changing atmospheric and the surface conditions, and other "regressors", resulting in random and systematic errors in the final aerosol products. In this study, we will concentrate on the subject of removing biases and the systematic errors from MODIS (both Terra and Aqua) aerosol product, using Machine Learning algorithms. While we are assessing our regressors in our system when comparing global aerosol products, the Aerosol Robotic Network of sun-photometers (AERONET) will be used as a baseline for evaluating the MODIS aerosol products (Dark Target for land and ocean, and Deep Blue retrieval algorithms). The results of bias adjustment for MODIS Terra and Aqua are planned to be incorporated into the AeroStat Giovanni as part of the NASA ACCESS funded AeroStat project.

  15. Support Vector Machine Ensemble Based on Genetic Algorithm

    Institute of Scientific and Technical Information of China (English)

    LI Ye; YIN Ru-po; CAI Yun-ze; XU Xiao-ming

    2006-01-01

    Support vector machines (SVMs) have been introduced as effective methods for solving classification problems.However, due to some limitations in practical applications,their generalization performance is sometimes far from the expected level. Therefore, it is meaningful to study SVM ensemble learning. In this paper, a novel genetic algorithm based ensemble learning method, namely Direct Genetic Ensemble (DGE), is proposed. DGE adopts the predictive accuracy of ensemble as the fitness function and searches a good ensemble from the ensemble space. In essence, DGE is also a selective ensemble learning method because the base classifiers of the ensemble are selected according to the solution of genetic algorithm. In comparison with other ensemble learning methods, DGE works on a higher level and is more direct. Different strategies of constructing diverse base classifiers can be utilized in DGE.Experimental results show that SVM ensembles constructed by DGE can achieve better performance than single SVMs,bagged and boosted SVM ensembles. In addition, some valuable conclusions are obtained.

  16. Parallel Machine Scheduling (PMS) in Manufacturing Systems Using the Ant Colonies Optimization Algorithmic Rule

    Science.gov (United States)

    Senthiil, P. V.; Selladurai, V.; Rajesh, R.

    This study introduces a new approach for decentralized scheduling in a parallel machine environment based on the ant colonies optimization algorithm. The algorithm extends the use of the traveling salesman problem for scheduling in one single machine, to a multiple machine problem. The results are presented using simple and illustrative examples and show that the algorithm is able to optimize the different scheduling problems. Using the same parameters, the completion time of the tasks is minimized and the processing time of the parallel machines is balanced.

  17. Infrared machine vision system for the automatic detection of olive fruit quality.

    Science.gov (United States)

    Guzmán, Elena; Baeten, Vincent; Pierna, Juan Antonio Fernández; García-Mesa, José A

    2013-11-15

    External quality is an important factor in the extraction of olive oil and the marketing of olive fruits. The appearance and presence of external damage are factors that influence the quality of the oil extracted and the perception of consumers, determining the level of acceptance prior to purchase in the case of table olives. The aim of this paper is to report on artificial vision techniques developed for the online estimation of olive quality and to assess the effectiveness of these techniques in evaluating quality based on detecting external defects. This method of classifying olives according to the presence of defects is based on an infrared (IR) vision system. Images of defects were acquired using a digital monochrome camera with band-pass filters on near-infrared (NIR). The original images were processed using segmentation algorithms, edge detection and pixel value intensity to classify the whole fruit. The detection of the defect involved a pixel classification procedure based on nonparametric models of the healthy and defective areas of olives. Classification tests were performed on olives to assess the effectiveness of the proposed method. This research showed that the IR vision system is a useful technology for the automatic assessment of olives that has the potential for use in offline inspection and for online sorting for defects and the presence of surface damage, easily distinguishing those that do not meet minimum quality requirements. Crown Copyright © 2013 Published by Elsevier B.V. All rights reserved.

  18. Effective and efficient optics inspection approach using machine learning algorithms

    Energy Technology Data Exchange (ETDEWEB)

    Abdulla, G; Kegelmeyer, L; Liao, Z; Carr, W

    2010-11-02

    The Final Optics Damage Inspection (FODI) system automatically acquires and utilizes the Optics Inspection (OI) system to analyze images of the final optics at the National Ignition Facility (NIF). During each inspection cycle up to 1000 images acquired by FODI are examined by OI to identify and track damage sites on the optics. The process of tracking growing damage sites on the surface of an optic can be made more effective by identifying and removing signals associated with debris or reflections. The manual process to filter these false sites is daunting and time consuming. In this paper we discuss the use of machine learning tools and data mining techniques to help with this task. We describe the process to prepare a data set that can be used for training and identifying hardware reflections in the image data. In order to collect training data, the images are first automatically acquired and analyzed with existing software and then relevant features such as spatial, physical and luminosity measures are extracted for each site. A subset of these sites is 'truthed' or manually assigned a class to create training data. A supervised classification algorithm is used to test if the features can predict the class membership of new sites. A suite of self-configuring machine learning tools called 'Avatar Tools' is applied to classify all sites. To verify, we used 10-fold cross correlation and found the accuracy was above 99%. This substantially reduces the number of false alarms that would otherwise be sent for more extensive investigation.

  19. Design Considerations for Scalable High-Performance Vision Systems Embedded in Industrial Print Inspection Machines

    Directory of Open Access Journals (Sweden)

    Rössler Peter

    2007-01-01

    Full Text Available This paper describes the design of a scalable high-performance vision system which is used in the application area of optical print inspection. The system is able to process hundreds of megabytes of image data per second coming from several high-speed/high-resolution cameras. Due to performance requirements, some functionality has been implemented on dedicated hardware based on a field programmable gate array (FPGA, which is coupled to a high-end digital signal processor (DSP. The paper discusses design considerations like partitioning of image processing algorithms between hardware and software. The main chapters focus on functionality implemented on the FPGA, including low-level image processing algorithms (flat-field correction, image pyramid generation, neighborhood operations and advanced processing units (programmable arithmetic unit, geometry unit. Verification issues for the complex system are also addressed. The paper concludes with a summary of the FPGA resource usage and some performance results.

  20. Fast and intuitive programming of adaptive laser cutting of lace enabled by machine vision

    Science.gov (United States)

    Vaamonde, Iago; Souto-López, Álvaro; García-Díaz, Antón

    2015-07-01

    A machine vision system has been developed, validated, and integrated in a commercial laser robot cell. It permits an offline graphical programming of laser cutting of lace. The user interface allows loading CAD designs and aligning them with images of lace pieces. Different thread widths are discriminated to generate proper cutting program templates. During online operation, the system aligns CAD models of pieces and lace images, pre-checks quality of lace cuts and adapts laser parameters to thread widths. For pieces detected with the required quality, the program template is adjusted by transforming the coordinates of every trajectory point. A low-cost lace feeding system was also developed for demonstration of full process automation.

  1. Development of a machine vision system for a real-time precision sprayer

    Science.gov (United States)

    Bossu, Jérémie; Gée, Christelle; Truchetet, Frédéric

    2007-01-01

    In the context of precision agriculture, we have developed a machine vision system for a real time precision sprayer. From a monochrome CCD camera located in front of the tractor, the discrimination between crop and weeds is obtained with an image processing based on spatial information using a Gabor filter. This method allows to detect the periodic signals from the non periodic one and it enables to enhance the crop rows whereas weeds have patchy distribution. Thus, weed patches were clearly identified by a blob-coloring method. Finally, we use a pinhole model to transform the weed patch coordinates image in world coordinates in order to activate the right electro-pneumatic valve of the sprayer at the right moment.

  2. Using machine vision and data mining techniques to identify cell properties via microfluidic flow analysis

    Science.gov (United States)

    Horowitz, Geoffrey; Bowie, Samuel; Liu, Anna; Stone, Nicholas; Sulchek, Todd; Alexeev, Alexander

    2016-11-01

    In order to quickly identify the wide range of mechanistic properties that are seen in cell populations, a coupled machine vision and data mining analysis is developed to examine high speed videos of cells flowing through a microfluidic device. The microfluidic device contains a microchannel decorated with a periodical array of diagonal ridges. The ridges compress flowing cells that results in complex cell trajectory and induces cell cross-channel drift, both depend on the cell intrinsic mechanical properties that can be used to characterize specific cell lines. Thus, the cell trajectory analysis can yield a parameter set that can serve as a unique identifier of a cell's membership to a specific cell population. By using the correlations between the cell populations and measured cell trajectories in the ridged microchannel, mechanical properties of individual cells and their specific populations can be identified via only information captured using video analysis. Financial support provided by National Science Foundation (NSF) Grant No. CMMI 1538161.

  3. On-line least squares support vector machine algorithm in gas prediction

    Institute of Scientific and Technical Information of China (English)

    ZHAO Xiao-hu; WANG Gang; ZHAO Ke-ke; TAN De-jian

    2009-01-01

    Traditional coal mine safety prediction methods are off-line and do not have dynamic prediction functions. The Support Vector Machine (SVM) is a new machine learning algorithm that has excellent properties. The least squares support vector machine (LS-SVM) algorithm is an improved algorithm of SVM. But the common LS-SVM algorithm, used directly in safety predictions, has some problems. We have first studied gas prediction problems and the basic theory of LS-SVM. Given these problems, we have investigated the affect of the time factor about safety prediction and present an on-line prediction algorithm, based on LS-SVM. Finally, given our observed data, we used the on-line algorithm to predict gas emissions and used other related algorithm to com- pare its performance. The simulation results have verified the validity of the new algorithm.

  4. Machine vision method for online surface inspection of easy open can ends

    Science.gov (United States)

    Mariño, Perfecto; Pastoriza, Vicente; Santamaría, Miguel

    2006-10-01

    Easy open can end manufacturing process in the food canning sector currently makes use of a manual, non-destructive testing procedure to guarantee can end repair coating quality. This surface inspection is based on a visual inspection made by human inspectors. Due to the high production rate (100 to 500 ends per minute) only a small part of each lot is verified (statistical sampling), then an automatic, online, inspection system, based on machine vision, has been developed to improve this quality control. The inspection system uses a fuzzy model to make the acceptance/rejection decision for each can end from the information obtained by the vision sensor. In this work, the inspection method is presented. This surface inspection system checks the total production, classifies the ends in agreement with an expert human inspector, supplies interpretability to the operators in order to find out the failure causes and reduce mean time to repair during failures, and allows to modify the minimum can end repair coating quality.

  5. A noninvasive technique for real-time detection of bruises in apple surface based on machine vision

    Science.gov (United States)

    Zhao, Juan; Peng, Yankun; Dhakal, Sagar; Zhang, Leilei; Sasao, Akira

    2013-05-01

    Apple is one of the highly consumed fruit item in daily life. However, due to its high damage potential and massive influence on taste and export, the quality of apple has to be detected before it reaches the consumer's hand. This study was aimed to develop a hardware and software unit for real-time detection of apple bruises based on machine vision technology. The hardware unit consisted of a light shield installed two monochrome cameras at different angles, LED light source to illuminate the sample, and sensors at the entrance of box to signal the positioning of sample. Graphical Users Interface (GUI) was developed in VS2010 platform to control the overall hardware and display the image processing result. The hardware-software system was developed to acquire the images of 3 samples from each camera and display the image processing result in real time basis. An image processing algorithm was developed in Opencv and C++ platform. The software is able to control the hardware system to classify the apple into two grades based on presence/absence of surface bruises with the size of 5mm. The experimental result is promising and the system with further modification can be applicable for industrial production in near future.

  6. Hybrid genetic algorithm for minimizing non productive machining ...

    African Journals Online (AJOL)

    user

    A Bi-criteria M-Machine SDST Flow Shop Scheduling using Modified Heuristic Genetic ... He has more than 35 research papers in international/national journals and ... supply chain management, inventory management, machine learning, etc.

  7. An Adaptive Algorithm for Dynamic Priority Based Virtual Machine Scheduling in Cloud

    Directory of Open Access Journals (Sweden)

    Subramanian S

    2012-11-01

    Full Text Available Cloud computing, a relatively new technology, has been gaining immense popularity over the last few years. The number of cloud users has been growing exponentially and apparently scheduling of virtual machines in the cloud becomes an important issue to analyze. This paper throws light on the various scheduling algorithms present for scheduling virtual machines and also proposes a new algorithm that combines the advantages of all the existing algorithms and overcomes their disadvantages.

  8. Duality-based algorithms for scheduling unrelated parallel machines

    NARCIS (Netherlands)

    S.L. van de Velde (Steef)

    1993-01-01

    textabstractWe consider the following parallel machine scheduling problem. Each of n independent jobs has to be scheduled on one of m unrelated parallel machines. The processing of job J[sub l] on machine Mi requires an uninterrupted period of positive length p[sub lj]. The objective is to find an a

  9. Performance Evaluation of Neuromorphic-Vision Object Recognition Algorithms

    Science.gov (United States)

    2014-08-01

    based on deformable part models [1]. The algorithm is implemented on a CUDA (Compute Unified Device Architecture) platform for GPUs trained to...message passing framework that operates on clusters of CPU, GPU , and FPGA hardware. Source code is freely available from the authors. Fig. 6

  10. A machine vision system for micro-EDM based on linux

    Science.gov (United States)

    Guo, Rui; Zhao, Wansheng; Li, Gang; Li, Zhiyong; Zhang, Yong

    2006-11-01

    Due to the high precision and good surface quality that it can give, Electrical Discharge Machining (EDM) is potentially an important process for the fabrication of micro-tools and micro-components. However, a number of issues remain unsolved before micro-EDM becomes a reliable process with repeatable results. To deal with the difficulties in micro electrodes on-line fabrication and tool wear compensation, a micro-EDM machine vision system is developed with a Charge Coupled Device (CCD) camera, with an optical resolution of 1.61μm and an overall magnification of 113~729. Based on the Linux operating system, an image capturing program is developed with the V4L2 API, and an image processing program is exploited by using OpenCV. The contour of micro electrodes can be extracted by means of the Canny edge detector. Through the system calibration, the micro electrodes diameter can be measured on-line. Experiments have been carried out to prove its performance, and the reasons of measurement error are also analyzed.

  11. A Novel Approach to Automatic Road-Accident Detection using Machine Vision Techniques

    Directory of Open Access Journals (Sweden)

    Vaishnavi Ravindran

    2016-11-01

    Full Text Available In this paper, a novel approach for automatic road accident detection is proposed. The approach is based on detecting damaged vehicles from footage received from surveillance cameras installed in roads and highways which would indicate the occurrence of a road accident. Detection of damaged cars falls under the category of object detection in the field of machine vision and has not been achieved so far. In this paper, a new supervised learning method comprising of three different stages which are combined into a single framework in a serial manner which successfully detects damaged cars from static images is proposed. The three stages use five support vector machines trained with Histogram of gradients (HOG and Gray level co-occurrence matrix (GLCM features. Since damaged car detection has not been attempted, two datasets of damaged cars - Damaged Cars Dataset-1 (DCD-1 and Damaged Cars Dataset-2 (DCD-2 – was compiled for public release. Experiments were conducted on DCD-1 and DCD-2 which differ based on the distance at which the image is captured and the quality of the images. The accuracy of the system is 81.83% for DCD-1 captured at approximately 2 meters with good quality and 64.37% for DCD-2 captured at approximately 20 meters with poor quality.

  12. Quality indexing by machine vision during fermentation in black tea manufacturing

    Science.gov (United States)

    Borah, S.; Bhuyan, M.

    2003-04-01

    Although the organoleptic method of tea testing has been traditionally used for quality monitoring, an alternative way by machine vision may be advantageous. Although, the three main quality descriptors estimate the overall quality of made-tea, viz., strength, briskness and brightness of tea liquor, the exact colour detection in fermenting process leads to a good quality-monitoring tool. The use of digital image processing technique for this purpose is reported to play an effective role towards the production of good quality tea though it is not the only quality determining parameter. In this paper, it has been tried to compare the contribution of the chemical constituents towards the final product with the visual appearance in the processing stage by imaging. The use of machine intelligence supports the process somewhat invariantly in comparison to the human decision and colorimetric approach. The captured images are processed for colour matching with a standard image database using HSI colour model. The application of colour dissimilarity and perceptron learning for the standard images and the test images is ensured. Moreover, the performance of the system is being tried to correlate with the decision made by the organoleptic panel assigned for the tea testing and chemical test results on the final product. However, it should be noted that the optimized result could be achieved only when the other quality parameters such as withering, flavour (aroma) detection, drying status etc. are properly maintained.

  13. Multi-classification algorithm and its realization based on least square support vector machine algorithm

    Institute of Scientific and Technical Information of China (English)

    Fan Youping; Chen Yunping; Sun Wansheng; Li Yu

    2005-01-01

    As a new type of learning machine developed on the basis of statistics learning theory, support vector machine (SVM) plays an important role in knowledge discovering and knowledge updating by constructing non-linear optimal classifier. However, realizing SVM requires resolving quadratic programming under constraints of inequality, which results in calculation difficulty while learning samples gets larger. Besides, standard SVM is incapable of tackling multi-classification. To overcome the bottleneck of populating SVM, with training algorithm presented, the problem of quadratic programming is converted into that of resolving a linear system of equations composed of a group of equation constraints by adopting the least square SVM(LS-SVM) and introducing a modifying variable which can change inequality constraints into equation constraints, which simplifies the calculation. With regard to multi-classification, an LS-SVM applicable in multi-classification is deduced. Finally, efficiency of the algorithm is checked by using universal Circle in square and two-spirals to measure the performance of the classifier.

  14. High-speed Robot Auto-sorting System Based on Machine Vision%高速机器人分拣系统机器视觉技术的研究

    Institute of Scientific and Technical Information of China (English)

    晏祖根; 李明; 徐克非; 孙小华; 闫志鹏; 孙智慧

    2014-01-01

    针对我国食品生产行业的实际需求,基于并联机器人、机器视觉等先进技术,构建了面向食品生产包装的高速机器人分拣系统,研究了输送带上运动食品的机器视觉定位算法,设计了运动食品分级与定位的机器视觉硬件系统,基于专业图像处理软件Sherlock,研发了自动分拣机器视觉软件系统,以提高我国食品生产效率、保证食品卫生、降低劳动强度。%In view of the actual demand of the food industry in China,the high-speed parallel robot auto-sorting system is constructed.The machine vision positioning algorithm of the moving food on the conveyor belt is studied.The machine vision hardware system for food sorting and positioning is designed.Based on second-ary development for Sherlock in VC programming environment,the machine vision software system is devel-oped.By applied the machine vision system,it can improve the production efficiency and reduce the labor’s intensity in our food and packaging industry.

  15. Computer vision algorithm for diabetic foot injury identification and evaluation

    Energy Technology Data Exchange (ETDEWEB)

    Castaneda M, C. L.; Solis S, L. O.; Martinez B, M. R.; Ortiz R, J. M.; Garza V, I.; Martinez F, M.; Castaneda M, R.; Vega C, H. R., E-mail: lsolis@uaz.edu.mx [Universidad Autonoma de Zacatecas, 98000 Zacatecas, Zac. (Mexico)

    2016-10-15

    Diabetic foot is one of the most devastating consequences related to diabetes. It is relevant because of its incidence and the elevated percentage of amputations and deaths that the disease implies. Given the fact that the existing tests and laboratories designed to diagnose it are limited and expensive, the most common evaluation is still based on signs and symptoms. This means that the specialist completes a questionnaire based solely on observation and an invasive wound measurement. Using the questionnaire, the physician issues a diagnosis. In the sense, the diagnosis relies only on the criteria and the specialists experience. For some variables such as the lesions area or their location, this dependency is not acceptable. Currently bio-engineering has played a key role on the diagnose of different chronic degenerative diseases. A timely diagnose has proven to be the best tool against diabetic foot. The diabetics foot clinical evaluation, increases the possibility to identify risks and further complications. The main goal of this paper is to present the development of an algorithm based on digital image processing techniques, which enables to optimize the results on the diabetics foot lesion evaluation. Using advanced techniques for object segmentation and adjusting the sensibility parameter, allows the correlation between the algorithms identified wounds and those observed by the physician. Using the developed algorithm it is possible to identify and assess the wounds, their size, and location, in a non-invasive way. (Author)

  16. Preemptive Semi-online Algorithms for Parallel Machine Scheduling with Known Total Size

    Institute of Scientific and Technical Information of China (English)

    Yong HE; Hao ZHOU; Yi Wei JIANG

    2006-01-01

    This paper investigates preemptive semi-online scheduling problems on m identical parallel machines, where the total size of all jobs is known in advance. The goal is to minimize the maximum machine completion time or maximize the minimum machine completion time. For the first objective,we present an optimal semi-online algorithm with competitive ratio 1. For the second objective, we show that the competitive ratio of any semi-online algorithm is at least 2m-3/m-1 for any m > 2 and presentoptimal semi-online algorithms for m = 2,3.

  17. Matrix Multiplication Algorithm Selection with Support Vector Machines

    Science.gov (United States)

    2015-05-01

    approach for linear algebra algorithms, as we achieve up to a 26% performance improvement over selecting a single algorithm in advance. III. Related...different algorithms (such as industry-standard linear algebra algorithms and their communication-avoiding counterparts) [5]. One way to tackle algorithm...we conclude that using weighted SVM for algorithm selection is superior to using unweighted SVM. D. Properties of Misclassifications For any complex

  18. Research and Implementation of Algorithm for Image Enhancement and Unwrapped Distortion Correction for SLVF Panoramic Night Vision Image

    Institute of Scientific and Technical Information of China (English)

    2008-01-01

    Based on digital signal processor(DSP) and field programmable gate array(FPGA) techniques, the architecture of super large view field(SLVF) panoramic night vision image processing hardware platform was established. The panoramic unwrapping and correcting algorithm, up to a full 360°, based on coordinate rotation digital computer (CORDIC) and night vision image enhancement algorithm, based on histogram equalization theory and edge detection theory, was presented in this paper, with the purpose of processing night vision dynamic panoramic annular image. The annular image can be unwrapped and corrected to conventional rectangular panorama by the panoramic image processing algorithm, which uses the pipelined CORDIC configuration to realize a trigonometric function generator with high speed and high precision. Histogram equalization algorithm can perfectly enhance the contrast of the night vision image. Edge detection algorithm can be propitious to find and detect small dim dynamic targets in night vision circumstances. After abundant experiment, the algorithm for panoramic image processing and night vision image enhancement is successfully implemented in FPGA and DSP. The panoramic night vision image system is a compact device, with no external rotating parts. And the system can reliably and dynamically detect 360° SLVF panoramic night vision image.

  19. Validating Machine Learning Algorithms for Twitter Data Against Established Measures of Suicidality.

    Science.gov (United States)

    Braithwaite, Scott R; Giraud-Carrier, Christophe; West, Josh; Barnes, Michael D; Hanson, Carl Lee

    2016-05-16

    One of the leading causes of death in the United States (US) is suicide and new methods of assessment are needed to track its risk in real time. Our objective is to validate the use of machine learning algorithms for Twitter data against empirically validated measures of suicidality in the US population. Using a machine learning algorithm, the Twitter feeds of 135 Mechanical Turk (MTurk) participants were compared with validated, self-report measures of suicide risk. Our findings show that people who are at high suicidal risk can be easily differentiated from those who are not by machine learning algorithms, which accurately identify the clinically significant suicidal rate in 92% of cases (sensitivity: 53%, specificity: 97%, positive predictive value: 75%, negative predictive value: 93%). Machine learning algorithms are efficient in differentiating people who are at a suicidal risk from those who are not. Evidence for suicidality can be measured in nonclinical populations using social media data.

  20. Estimating Position of Mobile Robots From Omnidirectional Vision Using an Adaptive Algorithm.

    Science.gov (United States)

    Li, Luyang; Liu, Yun-Hui; Wang, Kai; Fang, Mu

    2015-08-01

    This paper presents a novel and simple adaptive algorithm for estimating the position of a mobile robot with high accuracy in an unknown and unstructured environment by fusing images of an omnidirectional vision system with measurements of odometry and inertial sensors. Based on a new derivation where the omnidirectional projection can be linearly parameterized by the positions of the robot and natural feature points, we propose a novel adaptive algorithm, which is similar to the Slotine-Li algorithm in model-based adaptive control, to estimate the robot's position by using the tracked feature points in image sequence, the robot's velocity, and orientation angles measured by odometry and inertial sensors. It is proved that the adaptive algorithm leads to global exponential convergence of the position estimation errors to zero. Simulations and real-world experiments are performed to demonstrate the performance of the proposed algorithm.

  1. [Comparative Efficiency of Algorithms Based on Support Vector Machines for Regression].

    Science.gov (United States)

    Kadyrova, N O; Pavlova, L V

    2015-01-01

    Methods of construction of support vector machines do not require additional a priori information and can be used to process large scale data set. It is especially important for various problems in computational biology. The main set of algorithms of support vector machines for regression is presented. The comparative efficiency of a number of support-vector-algorithms for regression is investigated. A thorough analysis of the study results found the most efficient support vector algorithms for regression. The description of the presented algorithms, sufficient for their practical implementation is given.

  2. Development of a model of machine hand eye coordination and program specifications for a topological machine vision system

    Science.gov (United States)

    1972-01-01

    A unified approach to computer vision and manipulation is developed which is called choreographic vision. In the model, objects to be viewed by a projected robot in the Viking missions to Mars are seen as objects to be manipulated within choreographic contexts controlled by a multimoded remote, supervisory control system on Earth. A new theory of context relations is introduced as a basis for choreographic programming languages. A topological vision model is developed for recognizing objects by shape and contour. This model is integrated with a projected vision system consisting of a multiaperture image dissector TV camera and a ranging laser system. System program specifications integrate eye-hand coordination and topological vision functions and an aerospace multiprocessor implementation is described.

  3. Tomato grading system using machine vision technology and neuro-fuzzy networks (ANFIS

    Directory of Open Access Journals (Sweden)

    H Izadi

    2016-04-01

    Full Text Available Introduction: The quality of agricultural products is associated with their color, size and health, grading of fruits is regarded as an important step in post-harvest processing. In most cases, manual sorting inspections depends on available manpower, time consuming and their accuracy could not be guaranteed. Machine Vision is known to be a useful tool for external features measurement (e.g. size, shape, color and defects and in recent century, Machine Vision technology has been used for shape sorting. The main purpose of this study was to develop new method for tomato grading and sorting using Neuro-fuzzy system (ANFIS and to compare the accuracies of the ANFIS predicted results with those suggested by a human expert. Materials and Methods: In this study, a total of 300 image of tomatoes (Rev ground was randomly harvested, classified in 3 ripeness stage, 3 sizes and 2 health. The grading and sorting mechanism consisted of a lighting chamber (cloudy sky, lighting source and a digital camera connected to a computer. The images were recorded in a special chamber with an indirect radiation (cloudy sky with four florescent lampson each sides and camera lens was entire to lighting chamber by a hole which was only entranced to outer and covered by a camera lens. Three types of features were extracted from final images; Shap, color and texture. To receive these features, we need to have images both in color and binary format in procedure shown in Figure 1. For the first group; characteristics of the images were analysis that could offer information an surface area (S.A., maximum diameter (Dmax, minimum diameter (Dmin and average diameters. Considering to the importance of the color in acceptance of food quality by consumers, the following classification was conducted to estimate the apparent color of the tomato; 1. Classified as red (red > 90% 2. Classified as red light (red or bold pink 60-90% 3. Classified as pink (red 30-60% 4. Classified as Turning

  4. Rolling optimization algorithm based on collision window for single machine scheduling problem

    Institute of Scientific and Technical Information of China (English)

    Wang Changjun; Xi Yugeng

    2005-01-01

    Focusing on the single machine scheduling problem which minimizes the total completion time in the presence of dynamic job arrivals, a rolling optimization scheduling algorithm is proposed based on the analysis of the character and structure of scheduling. An optimal scheduling strategy in collision window is presented. Performance evaluation of this algorithm is given. Simulation indicates that the proposed algorithm is better than other common heuristic algorithms on both the total performance and stability.

  5. Experimental analysis of the performance of machine learning algorithms in the classification of navigation accident records

    Directory of Open Access Journals (Sweden)

    REIS, M V. S. de A.

    2017-06-01

    Full Text Available This paper aims to evaluate the use of machine learning techniques in a database of marine accidents. We analyzed and evaluated the main causes and types of marine accidents in the Northern Fluminense region. For this, machine learning techniques were used. The study showed that the modeling can be done in a satisfactory manner using different configurations of classification algorithms, varying the activation functions and training parameters. The SMO (Sequential Minimal Optimization algorithm showed the best performance result.

  6. A Novel Generic Ball Recognition Algorithm Based on Omnidirectional Vision for Soccer Robots

    Directory of Open Access Journals (Sweden)

    Hui Zhang

    2013-11-01

    Full Text Available It is significant for the final goal of RoboCup to realize the recognition of generic balls for soccer robots. In this paper, a novel generic ball recognition algorithm based on omnidirectional vision is proposed by combining the modified Haar-like features and AdaBoost learning algorithm. The algorithm is divided into offline training and online recognition. During the phase of offline training, numerous sub-images are acquired from various panoramic images, including generic balls, and then the modified Haar-like features are extracted from them and used as the input of the AdaBoost learning algorithm to obtain a classifier. During the phase of online recognition, and according to the imaging characteristics of our omnidirectional vision system, rectangular windows are defined to search for the generic ball along the rotary and radial directions in the panoramic image, and the learned classifier is used to judge whether a ball is included in the window. After the ball has been recognized globally, ball tracking is realized by integrating a ball velocity estimation algorithm to reduce the computational cost. The experimental results show that good performance can be achieved using our algorithm, and that the generic ball can be recognized and tracked effectively.

  7. Research into the Architecture of CAD Based Robot Vision Systems

    Science.gov (United States)

    1988-02-09

    Vision 󈨚 and "Automatic Generation of Recognition Features for Com- puter Vision," Mudge, Turney and Volz, published in Robotica (1987). All of the...Occluded Parts," (T.N. Mudge, J.L. Turney, and R.A. Volz), Robotica , vol. 5, 1987, pp. 117-127. 5. "Vision Algorithms for Hypercube Machines," (T.N. Mudge

  8. A new optical flat surface measurement method based on machine vision and deflectometry

    Science.gov (United States)

    Kewei, E.; Li, Dahai; Yang, Lijie; Guo, Guangrao; Li, Mengyang; Wang, Xuemin; Zhang, Tao; Xiong, Zhao

    2016-10-01

    Phase Measuring Deflectometry(PMD) is a non-contact, high dynamic-range and full-field metrology which becomes a serious competitor to interferometry. However, the accuracy of deflectometry metrology is strongly influenced by the level of the calibrations. Our paper presents a calibration-based PMD method to test optical flat surface with a high accuracy. In our method, a pin-hole camera was set next to the LCD screen which is used to project sinusoidal fringes to the test flat. And the test flat was placed parallel to the direction of the LCD screen, which makes the geometry calibration process are simplified. The photogrammetric methods used in computer vision science was used to calibrate the pin-hole camera by using a checker pattern shown on another LCD display at six different orientations, the intrinsic parameters can be obtained by processing the obtained image of checker patterns. Further, by making the last orientation of checker pattern is aligned at the same position as the test optical flat, the algorithms used in this paper can obtain the mapping relationship between the CCD pixels and the subaperture coordinates on the test optical flat. We test a optical flat with a size of 50mm in diameter using our setup and algorithm. Our experimental results of optical flat figure from low to high order aberrations show a good agreement with that from the Fizeau interferometer.

  9. Feature-Free Activity Classification of Inertial Sensor Data With Machine Vision Techniques: Method, Development, and Evaluation.

    Science.gov (United States)

    Dominguez Veiga, Jose Juan; O'Reilly, Martin; Whelan, Darragh; Caulfield, Brian; Ward, Tomas E

    2017-08-04

    Inertial sensors are one of the most commonly used sources of data for human activity recognition (HAR) and exercise detection (ED) tasks. The time series produced by these sensors are generally analyzed through numerical methods. Machine learning techniques such as random forests or support vector machines are popular in this field for classification efforts, but they need to be supported through the isolation of a potentially large number of additionally crafted features derived from the raw data. This feature preprocessing step can involve nontrivial digital signal processing (DSP) techniques. However, in many cases, the researchers interested in this type of activity recognition problems do not possess the necessary technical background for this feature-set development. The study aimed to present a novel application of established machine vision methods to provide interested researchers with an easier entry path into the HAR and ED fields. This can be achieved by removing the need for deep DSP skills through the use of transfer learning. This can be done by using a pretrained convolutional neural network (CNN) developed for machine vision purposes for exercise classification effort. The new method should simply require researchers to generate plots of the signals that they would like to build classifiers with, store them as images, and then place them in folders according to their training label before retraining the network. We applied a CNN, an established machine vision technique, to the task of ED. Tensorflow, a high-level framework for machine learning, was used to facilitate infrastructure needs. Simple time series plots generated directly from accelerometer and gyroscope signals are used to retrain an openly available neural network (Inception), originally developed for machine vision tasks. Data from 82 healthy volunteers, performing 5 different exercises while wearing a lumbar-worn inertial measurement unit (IMU), was collected. The ability of the

  10. A distortion-correction method for workshop machine vision measurement system

    Science.gov (United States)

    Chen, Ruwen; Huang, Ren; Zhang, Zhisheng; Shi, Jinfei; Chen, Zixin

    2008-12-01

    The application of machine vision measurement system is developing rapidly in industry for its non-contact, high speed, and automation characteristics. However, there are nonlinear distortions in the images which are vital to measuring precision, for the object dimensions are determined by the image properties. People are interested in this problem and put forward some physical model based correction methods which are widely applied in engineering. However, these methods are difficult to be realized in workshop for the images are non-repetitive interfered by the coupled dynamic factors, which means the real imaging is a stochastic process. A new nonlinear distortion correction method based on a VNAR model (Volterra series based nonlinear auto-regressive time series model) is proposed to describe the distorted image edge series. The model parameter vectors are achieved by the laws of data. The distortion-free edges are obtained after model filtering and the image dimensions are transformed to measuring dimensions. Experimental results show that the method is reliable and can be applied to engineering.

  11. X-ray-based machine vision system for distal locking of intramedullary nails.

    Science.gov (United States)

    Juneho, F; Bouazza-Marouf, K; Kerr, D; Taylor, A J; Taylor, G J S

    2007-05-01

    In surgical procedures for femoral shaft fracture treatment, current techniques for locking the distal end of intramedullary nails, using two screws, rely heavily on the use of two-dimensional X-ray images to guide three-dimensional bone drilling processes. Therefore, a large number of X-ray images are required, as the surgeon uses his/her skills and experience to locate the distal hole axes on the intramedullary nail. The long-term effects of X-ray radiation and their relation to different types of cancer still remain uncertain. Therefore, there is a need to develop a surgical technique that can limit the use of X-rays during the distal locking procedure. A robotic-assisted orthopaedic surgery system has been developed at Loughborough University to assist orthopaedic surgeons by reducing the irradiation involved in such operations. The system simplifies the current approach as it uses only two near-orthogonal X-ray images to determine the drilling trajectory of the distal locking holes, thereby considerably reducing irradiation to both the surgeon and patient. Furthermore, the system uses robust machine vision features to reduce the surgeon's interaction with the system, thus reducing the overall operating time. Laboratory test results have shown that the proposed system is very robust in the presence of variable noise and contrast in the X-ray images.

  12. Colour Model for Outdoor Machine Vision for Tropical Regions and its Comparison with the CIE Model

    Energy Technology Data Exchange (ETDEWEB)

    Sahragard, Nasrolah; Ramli, Abdul Rahman B [Institute of Advanced Technology, Universiti Putra Malaysia 43400 Serdang, Selangor (Malaysia); Marhaban, Mohammad Hamiruce [Department of Electrical and Electronic Engineering, Faculty of Engineering, Universiti Putra Malaysia 43400 Serdang, Selangor (Malaysia); Mansor, Shattri B, E-mail: sahragard@yahoo.com [Department of Civil Engineering, Faculty of Engineering, Universiti Putra Malaysia 43400 Serdang, Selangor (Malaysia)

    2011-02-15

    Accurate modeling of daylight and surface reflectance are very useful for most outdoor machine vision applications specifically those which are based on color recognition. Existing daylight CIE model has drawbacks that limit its ability to predict the color of incident light. These limitations include lack of considering ambient light, effects of light reflected off the ground, and context specific information. Previously developed color model is only tested for a few geographical places in North America and its accountability is under question for other places in the world. Besides, existing surface reflectance models are not easily applied to outdoor images. A reflectance model with combined diffuse and specular reflection in normalized HSV color space could be used to predict color. In this paper, a new daylight color model showing the color of daylight for a broad range of sky conditions is developed which will suit weather conditions of tropical places such as Malaysia. A comparison of this daylight color model and daylight CIE model will be discussed. The colors of matte and specular surfaces have been estimated by use of the developed color model and surface reflection function in this paper. The results are shown to be highly reliable.

  13. Ordinal scheduling problem and its asymptotically optimal algorithms on parallel machine system

    Institute of Scientific and Technical Information of China (English)

    TAN Zhiyi; HE Yong

    2004-01-01

    Focusing on the ordinal scheduling problem on a parallel machine system, we discuss the background of ordinal scheduling and the motivation of ordinal algorithms. In addition, for the ordinal scheduling problem on identical parallel machines with the objective to maximize the minimum machine load, we then give two asymptotically optimal algorithm classes which have worst-case ratios very close to the upper bound of the problem for any given m. These results greatly improve the results proposed by He Yong and Tan Zhiyi in 2002.

  14. Machine vision assisted analysis of structure-localization relationships in a combinatorial library of prospective bioimaging probes

    OpenAIRE

    Shedden, Kerby; Li, Qian; Liu, Fangyi; Chang, Young Tae; Rosania, Gus R.

    2009-01-01

    With a combinatorial library of bioimaging probes, it is now possible to use machine vision to analyze the contribution of different building blocks of the molecules to their cell-associated visual signals. For athis purpose, cell-permeant, fluorescent styryl molecules were synthesized by condensation of 168 aldehyde with 8 pyridinium/quinolinium building blocks. Images of cells incubated with fluorescent molecules were acquired with a high content screening instrument. Chemical and image fea...

  15. Integral Images: Efficient Algorithms for Their Computation and Storage in Resource-Constrained Embedded Vision Systems

    Directory of Open Access Journals (Sweden)

    Shoaib Ehsan

    2015-07-01

    Full Text Available The integral image, an intermediate image representation, has found extensive use in multi-scale local feature detection algorithms, such as Speeded-Up Robust Features (SURF, allowing fast computation of rectangular features at constant speed, independent of filter size. For resource-constrained real-time embedded vision systems, computation and storage of integral image presents several design challenges due to strict timing and hardware limitations. Although calculation of the integral image only consists of simple addition operations, the total number of operations is large owing to the generally large size of image data. Recursive equations allow substantial decrease in the number of operations but require calculation in a serial fashion. This paper presents two new hardware algorithms that are based on the decomposition of these recursive equations, allowing calculation of up to four integral image values in a row-parallel way without significantly increasing the number of operations. An efficient design strategy is also proposed for a parallel integral image computation unit to reduce the size of the required internal memory (nearly 35% for common HD video. Addressing the storage problem of integral image in embedded vision systems, the paper presents two algorithms which allow substantial decrease (at least 44.44% in the memory requirements. Finally, the paper provides a case study that highlights the utility of the proposed architectures in embedded vision systems.

  16. Integral Images: Efficient Algorithms for Their Computation and Storage in Resource-Constrained Embedded Vision Systems.

    Science.gov (United States)

    Ehsan, Shoaib; Clark, Adrian F; Naveed ur Rehman; McDonald-Maier, Klaus D

    2015-07-10

    The integral image, an intermediate image representation, has found extensive use in multi-scale local feature detection algorithms, such as Speeded-Up Robust Features (SURF), allowing fast computation of rectangular features at constant speed, independent of filter size. For resource-constrained real-time embedded vision systems, computation and storage of integral image presents several design challenges due to strict timing and hardware limitations. Although calculation of the integral image only consists of simple addition operations, the total number of operations is large owing to the generally large size of image data. Recursive equations allow substantial decrease in the number of operations but require calculation in a serial fashion. This paper presents two new hardware algorithms that are based on the decomposition of these recursive equations, allowing calculation of up to four integral image values in a row-parallel way without significantly increasing the number of operations. An efficient design strategy is also proposed for a parallel integral image computation unit to reduce the size of the required internal memory (nearly 35% for common HD video). Addressing the storage problem of integral image in embedded vision systems, the paper presents two algorithms which allow substantial decrease (at least 44.44%) in the memory requirements. Finally, the paper provides a case study that highlights the utility of the proposed architectures in embedded vision systems.

  17. Solar Flare Prediction Model with Three Machine-Learning Algorithms Using Ultraviolet Brightening and Vector Magnetogram

    CERN Document Server

    Nishizuka, N; Kubo, Y; Den, M; Watari, S; Ishii, M

    2016-01-01

    We developed a flare prediction model using machine learning, which is optimized to predict the maximum class of flares occurring in the following 24 h. Machine learning is used to devise algorithms that can learn from and make decisions on a huge amount of data. We used solar observation data during the period 2010-2015, such as vector magnetogram, ultraviolet (UV) emission, and soft X-ray emission taken by the Solar Dynamics Observatory and the Geostationary Operational Environmental Satellite. We detected active regions from the full-disk magnetogram, from which 60 features were extracted with their time differentials, including magnetic neutral lines, the current helicity, the UV brightening, and the flare history. After standardizing the feature database, we fully shuffled and randomly separated it into two for training and testing. To investigate which algorithm is best for flare prediction, we compared three machine learning algorithms: the support vector machine (SVM), k-nearest neighbors (k-NN), and ...

  18. Advanced Credit-Assignment CMAC Algorithm for Robust Self-Learning and Self-Maintenance Machine

    Institute of Scientific and Technical Information of China (English)

    ZHANG Lei(张蕾); LEE Jay; CAO Qixin(曹其新); WANG Lei(王磊)

    2004-01-01

    Smart machine necessitates self-learning capabilities to assess its own performance and predict its behavior. To achieve self-maintenance intelligence, robust and fast learning algorithms need to be embedded in machine for real-time decision. This paper presents a credit-assignment cerebellar model articulation controller (CA-CMAC) algorithm to reduce learning interference in machine learning. The developed algorithms on credit matrix and the credit correlation matrix are presented. The error of the training sample distributed to the activated memory cell is proportional to the cell's credibility, which is determined by its activated times. The convergence processes of CA-CMAC in cyclic learning are further analyzed with two convergence theorems. In addition, simulation results on the inverse kinematics of 2-degree-of-freedom planar robot arm are used to prove the convergence theorems and show that CA-CMAC converges faster than conventional machine learning.

  19. Vision Autonomous Relative Positioning and Orientating Algorithm for Distributed Micro/Nanosatellite Earth Observation System Based on Dual Quaternion

    Directory of Open Access Journals (Sweden)

    Kezhao Li

    2010-01-01

    Full Text Available It is a valid way to analyze the space object real-time movement by using distributed satellite earth observation system, which can provide the stereographic image through the collaboration of the satellites. That relative position and pose estimation is one of the key technologies for distributed micro/nanosatellite earth observation system (DMSEOS. In this paper, on the basis of the attitude dynamics of spacecrafts and the theory of machine vision, an autonomous positioning and orientating algorithm for distributed micro/nanosatellites based on dual quaternion and EKF (extended Kalman filtering is proposed. Firstly, how to represent a line transform unit using dual quaternion is introduced. Then, the feature line point of the line transform unit is defined. And then, on the basis of the attitude dynamics of spacecrafts and the theory of EKF, we build the state and observation equations. Finally, the simulations show that this algorithm is an accurate valid method in positioning and orientating of distributed micro/nanosatellite earth observation system.

  20. Quantum algorithms for biomolecular solutions of the satisfiability problem on a quantum machine.

    Science.gov (United States)

    Chang, Weng-Long; Ren, Ting-Ting; Luo, Jun; Feng, Mang; Guo, Minyi; Weicheng Lin, Kawuu

    2008-09-01

    In this paper, we demonstrate that the logic computation performed by the DNA-based algorithm for solving general cases of the satisfiability problem can be implemented more efficiently by our proposed quantum algorithm on the quantum machine proposed by Deutsch. To test our theory, we carry out a three-quantum bit nuclear magnetic resonance experiment for solving the simplest satisfiability problem.

  1. A method for classification of network traffic based on C5.0 Machine Learning Algorithm

    DEFF Research Database (Denmark)

    Bujlow, Tomasz; Riaz, M. Tahir; Pedersen, Jens Myrup

    2012-01-01

    current network traffic. To overcome the drawbacks of existing methods for traffic classification, usage of C5.0 Machine Learning Algorithm (MLA) was proposed. On the basis of statistical traffic information received from volunteers and C5.0 algorithm we constructed a boosted classifier, which was shown...

  2. Two Projection Pursuit Algorithms for Machine Learning under Non-Stationarity

    CERN Document Server

    Blythe, Duncan A J

    2011-01-01

    This thesis derives, tests and applies two linear projection algorithms for machine learning under non-stationarity. The first finds a direction in a linear space upon which a data set is maximally non-stationary. The second aims to robustify two-way classification against non-stationarity. The algorithm is tested on a key application scenario, namely Brain Computer Interfacing.

  3. The Acceleration/Deceleration Control Algorithm Based on Trapezoid-Curve Jerk in CNC Machining

    Directory of Open Access Journals (Sweden)

    Guoyong Zhao

    2013-02-01

    Full Text Available In this study, we propose the acceleration/deceleration control algorithm based on trapezoid-curve jerk in CNC machining. In aviation and mould and die industry, it is much significant to achieve high accuracy CNC machining on complex profile parts. The unsmooth Acceleration/Deceleration (ab. Acc/Dec control in feed movement is one of the main reasons to bring about machine tools impact and vibration in practical machining. After analyzing the CNC machine tools dynamic model, an Acc/Dec control algorithm based on trapezoid-curve jerk is put forward in order to avoid step change in jerk curve in the study; Moreover, the motion profile smooth control approach based on continuous jerk is developed in details to decrease machine tools impact according to various kinematics constraint conditions, such as the maximum acceleration, the maximum jerk, the machining program segment displacement, the instruction feedrate and so on; Finally, the developed Acc/Dec approach and the traditional linear Acc/Dec approach are compared in the CNC experimental table. The results reveal that the developed approach can achieve more smooth and flexible motion profile, which is helpful to minish machine tools impact and enhance parts machining surface quality.

  4. [Comparative efficiency of algorithms based on support vector machines for binary classification].

    Science.gov (United States)

    Kadyrova, N O; Pavlova, L V

    2015-01-01

    Methods of construction of support vector machines require no further a priori infoimation and provide big data processing, what is especially important for various problems in computational biology. The question of the quality of learning algorithms is considered. The main algorithms of support vector machines for binary classification are reviewed and they were comparatively explored for their efficiencies. The critical analysis of the results of this study revealed the most effective support-vector-classifiers. The description of the recommended algorithms, sufficient for their practical implementation, is presented.

  5. The Ways of Fuzzy Control Algorithms Using for Harvesting Machines Tracking

    Directory of Open Access Journals (Sweden)

    L. Tóth

    2013-09-01

    Full Text Available This contribution is oriented to ways of a fuzzy regulation using for machine tracking of the harvest machines. The main aim of this work was to practice verify and evaluate of functionality of control fuzzy algorithms for an Ackerman’s chassis which are generally used in agriculture machines for the crops harvesting. Design of the fuzzy control algorithm was focused to the wall following algorithm and obstacle avoidance. To achieve of the reliable results was made the real model of vehicle with Ackerman’s chassis type, which was controlled by PC with using development board Stellaris LM3S8962 based on ARM processor. Fuzzy control algorithms were developed in LabView application. Deviations were up to 0.2 m, which can be reduced to 0.1 m by hardware changing.

  6. 3D Visualization of Machine Learning Algorithms with Astronomical Data

    Science.gov (United States)

    Kent, Brian R.

    2016-01-01

    We present innovative machine learning (ML) methods using unsupervised clustering with minimum spanning trees (MSTs) to study 3D astronomical catalogs. Utilizing Python code to build trees based on galaxy catalogs, we can render the results with the visualization suite Blender to produce interactive 360 degree panoramic videos. The catalogs and their ML results can be explored in a 3D space using mobile devices, tablets or desktop browsers. We compare the statistics of the MST results to a number of machine learning methods relating to optimization and efficiency.

  7. New Dandelion Algorithm Optimizes Extreme Learning Machine for Biomedical Classification Problems

    Directory of Open Access Journals (Sweden)

    Xiguang Li

    2017-01-01

    Full Text Available Inspired by the behavior of dandelion sowing, a new novel swarm intelligence algorithm, namely, dandelion algorithm (DA, is proposed for global optimization of complex functions in this paper. In DA, the dandelion population will be divided into two subpopulations, and different subpopulations will undergo different sowing behaviors. Moreover, another sowing method is designed to jump out of local optimum. In order to demonstrate the validation of DA, we compare the proposed algorithm with other existing algorithms, including bat algorithm, particle swarm optimization, and enhanced fireworks algorithm. Simulations show that the proposed algorithm seems much superior to other algorithms. At the same time, the proposed algorithm can be applied to optimize extreme learning machine (ELM for biomedical classification problems, and the effect is considerable. At last, we use different fusion methods to form different fusion classifiers, and the fusion classifiers can achieve higher accuracy and better stability to some extent.

  8. An Algorithm of Quantum Restricted Boltzmann Machine Network Based on Quantum Gates and Its Application

    Directory of Open Access Journals (Sweden)

    Peilin Zhang

    2015-01-01

    Full Text Available We present an algorithm of quantum restricted Boltzmann machine network based on quantum gates. The algorithm is used to initialize the procedure that adjusts the qubit and weights. After adjusting, the network forms an unsupervised generative model that gives better classification performance than other discriminative models. In addition, we show how the algorithm can be constructed with quantum circuit for quantum computer.

  9. The evaluation of functional heart condition with machine learning algorithms

    Science.gov (United States)

    Overchuk, K. V.; Lezhnina, I. A.; Uvarov, A. A.; Perchatkin, V. A.; Lvova, A. B.

    2017-08-01

    This paper is considering the most suitable algorithms to build a classifier for evaluating of the functional heart condition with the ability to estimate the direction and progress of the patient’s treatment. The cons and pros of algorithms was analyzed with respect to the problem posed. The most optimal solution has been given and justified.

  10. POWER OPTIMIZATION OF FINITE STATE MACHINE BASED ON GENETIC ALGORITHM

    Institute of Scientific and Technical Information of China (English)

    Xia Yinshui; A.E.A. Almaini; Wu Xunwei

    2003-01-01

    Using state assignment to minimize power dissipation and area for finite state ma-chines is computationally hard. Most of published results show that the reduction of switchingactivity often trades with area penalty. In this paper, a new approach is proposed. Experimentalresults show a significant reduction of switching activity without area penalty compared withprevious publications.

  11. Simultaneous Multi-vehicle Detection and Tracking Framework with Pavement Constraints Based on Machine Learning and Particle Filter Algorithm

    Institute of Scientific and Technical Information of China (English)

    WANG Ke; HUANG Zhi; ZHONG Zhihua

    2014-01-01

    Due to the large variations of environment with ever-changing background and vehicles with different shapes, colors and appearances, to implement a real-time on-board vehicle recognition system with high adaptability, efficiency and robustness in complicated environments, remains challenging. This paper introduces a simultaneous detection and tracking framework for robust on-board vehicle recognition based on monocular vision technology. The framework utilizes a novel layered machine learning and particle filter to build a multi-vehicle detection and tracking system. In the vehicle detection stage, a layered machine learning method is presented, which combines coarse-search and fine-search to obtain the target using the AdaBoost-based training algorithm. The pavement segmentation method based on characteristic similarity is proposed to estimate the most likely pavement area. Efficiency and accuracy are enhanced by restricting vehicle detection within the downsized area of pavement. In vehicle tracking stage, a multi-objective tracking algorithm based on target state management and particle filter is proposed. The proposed system is evaluated by roadway video captured in a variety of traffics, illumination, and weather conditions. The evaluating results show that, under conditions of proper illumination and clear vehicle appearance, the proposed system achieves 91.2% detection rate and 2.6% false detection rate. Experiments compared to typical algorithms show that, the presented algorithm reduces the false detection rate nearly by half at the cost of decreasing 2.7%–8.6% detection rate. This paper proposes a multi-vehicle detection and tracking system, which is promising for implementation in an on-board vehicle recognition system with high precision, strong robustness and low computational cost.

  12. Parametric optimization of ultrasonic machining process using gravitational search and fireworks algorithms

    Directory of Open Access Journals (Sweden)

    Debkalpa Goswami

    2015-03-01

    Full Text Available Ultrasonic machining (USM is a mechanical material removal process used to erode holes and cavities in hard or brittle workpieces by using shaped tools, high-frequency mechanical motion and an abrasive slurry. Unlike other non-traditional machining processes, such as laser beam and electrical discharge machining, USM process does not thermally damage the workpiece or introduce significant levels of residual stress, which is important for survival of materials in service. For having enhanced machining performance and better machined job characteristics, it is often required to determine the optimal control parameter settings of an USM process. The earlier mathematical approaches for parametric optimization of USM processes have mostly yielded near optimal or sub-optimal solutions. In this paper, two almost unexplored non-conventional optimization techniques, i.e. gravitational search algorithm (GSA and fireworks algorithm (FWA are applied for parametric optimization of USM processes. The optimization performance of these two algorithms is compared with that of other popular population-based algorithms, and the effects of their algorithm parameters on the derived optimal solutions and computational speed are also investigated. It is observed that FWA provides the best optimal results for the considered USM processes.

  13. Design and implementation of a vision-based hovering and feature tracking algorithm for a quadrotor

    Science.gov (United States)

    Lee, Y. H.; Chahl, J. S.

    2016-10-01

    This paper demonstrates an approach to the vision-based control of the unmanned quadrotors for hover and object tracking. The algorithms used the Speed Up Robust Features (SURF) algorithm to detect objects. The pose of the object in the image was then calculated in order to pass the pose information to the flight controller. Finally, the flight controller steered the quadrotor to approach the object based on the calculated pose data. The above processes was run using standard onboard resources found in the 3DR Solo quadrotor in an embedded computing environment. The obtained results showed that the algorithm behaved well during its missions, tracking and hovering, although there were significant latencies due to low CPU performance of the onboard image processing system.

  14. The Use of Computer Vision Algorithms for Automatic Orientation of Terrestrial Laser Scanning Data

    Science.gov (United States)

    Markiewicz, Jakub Stefan

    2016-06-01

    The paper presents analysis of the orientation of terrestrial laser scanning (TLS) data. In the proposed data processing methodology, point clouds are considered as panoramic images enriched by the depth map. Computer vision (CV) algorithms are used for orientation, which are applied for testing the correctness of the detection of tie points and time of computations, and for assessing difficulties in their implementation. The BRISK, FASRT, MSER, SIFT, SURF, ASIFT and CenSurE algorithms are used to search for key-points. The source data are point clouds acquired using a Z+F 5006h terrestrial laser scanner on the ruins of Iłża Castle, Poland. Algorithms allowing combination of the photogrammetric and CV approaches are also presented.

  15. Machine Learning Algorithms for Automatic Classification of Marmoset Vocalizations

    Science.gov (United States)

    Ribeiro, Sidarta; Pereira, Danillo R.; Papa, João P.; de Albuquerque, Victor Hugo C.

    2016-01-01

    Automatic classification of vocalization type could potentially become a useful tool for acoustic the monitoring of captive colonies of highly vocal primates. However, for classification to be useful in practice, a reliable algorithm that can be successfully trained on small datasets is necessary. In this work, we consider seven different classification algorithms with the goal of finding a robust classifier that can be successfully trained on small datasets. We found good classification performance (accuracy > 0.83 and F1-score > 0.84) using the Optimum Path Forest classifier. Dataset and algorithms are made publicly available. PMID:27654941

  16. Comparison between Genetic Algorithms and Particle Swarm Optimization Methods on Standard Test Functions and Machine Design

    DEFF Research Database (Denmark)

    Nica, Florin Valentin Traian; Ritchie, Ewen; Leban, Krisztina Monika

    2013-01-01

    , genetic algorithm and particle swarm are shortly presented in this paper. These two algorithms are tested to determine their performance on five different benchmark test functions. The algorithms are tested based on three requirements: precision of the result, number of iterations and calculation time......Nowadays the requirements imposed by the industry and economy ask for better quality and performance while the price must be maintained in the same range. To achieve this goal optimization must be introduced in the design process. Two of the best known optimization algorithms for machine design....... Both algorithms are also tested on an analytical design process of a Transverse Flux Permanent Magnet Generator to observe their performances in an electrical machine design application....

  17. Comparison between Genetic Algorithms and Particle Swarm Optimization Methods on Standard Test Functions and Machine Design

    DEFF Research Database (Denmark)

    Nica, Florin Valentin Traian; Ritchie, Ewen; Leban, Krisztina Monika

    2013-01-01

    , genetic algorithm and particle swarm are shortly presented in this paper. These two algorithms are tested to determine their performance on five different benchmark test functions. The algorithms are tested based on three requirements: precision of the result, number of iterations and calculation time......Nowadays the requirements imposed by the industry and economy ask for better quality and performance while the price must be maintained in the same range. To achieve this goal optimization must be introduced in the design process. Two of the best known optimization algorithms for machine design....... Both algorithms are also tested on an analytical design process of a Transverse Flux Permanent Magnet Generator to observe their performances in an electrical machine design application....

  18. Optimal online algorithms for scheduling on two identical machines under a grade of service

    Institute of Scientific and Technical Information of China (English)

    2006-01-01

    This work is aimed at investigating the online scheduling problem on two parallel and identical machines with a new feature that service requests from various customers are entitled to many different grade of service (GoS) levels, so each job and machine are labelled with the GoS levels, and each job can be processed by a particular machine only when its GoS level is no less than that of the machine. The goal is to minimize the makespan. For non-preemptive version, we propose an optimal online algorithm with competitive ratio 5/3. For preemptive version, we propose an optimal online algorithm with competitive ratio 3/2.

  19. Protein domain boundary prediction by combining support vector machine and domain guess by size algorithm

    Institute of Scientific and Technical Information of China (English)

    Dong Qiwen; Wang Xiaolong; Lin Lei

    2007-01-01

    Successful prediction of protein domain boundaries provides valuable information not only for the computational structure prediction of multi-domain proteins but also for the experimental structure determination. A novel method for domain boundary prediction has been presented, which combines the support vector machine with domain guess by size algorithm. Since the evolutional information of multiple domains can be detected by position specific score matrix, the support vector machine method is trained and tested using the values of position specific score matrix generated by PSI-BLAST. The candidate domain boundaries are selected from the output of support vector machine, and are then inputted to domain guess by size algorithm to give the final results of domain boundary prediction. The experimental results show that the combined method outperforms the individual method of both support vector machine and domain guess by size.

  20. 机器视觉辅助的插头锥套式无人机自主空中加油仿真%Machine Vision Aided Simulation of Probe and Drogue Unmanned Aerial Vehicle Autonomous Aerial Refueling

    Institute of Scientific and Technical Information of China (English)

    王旭峰; 董新民; 孔星炜

    2013-01-01

    In order to precisely obtain the relative pose between probe and drogue during unmanned aerial vehicle (UAV) autonomous aerial refueling docking,a machine vision aided simulation scheme of probe-drogue UAV autonomous aerial refueling is proposed.Based on the machine vision,the recognition and tracking algorithm of refueling drogue is investigated.The relative pose between UAV and refueling drogue is estimated by using of Kalman filter algorithm.Experiment results show that the machine vision aided image processing algorithm can recognize and track refueling drogue precisely and the convergence of relative pose errors estimated by the filter algorithm is proved,fulfilling the demand of probe-drogue UAV autonomous aerial refueling.%为准确获取无人机自主空中加油对接阶段受油插头与加油锥套的相对位姿信息,提出一种机器视觉辅助的插头锥套式无人机自主空中加油方案.研究了机器视觉识别跟踪加油锥套的算法,利用卡尔曼滤波算法估计无人机与加油锥套的相对位姿.实验结果表明:机器视觉图像处理算法可精确识别跟踪加油锥套,滤波器估计的相对位姿误差收敛速度较快,满足插头锥套式无人机自主空中加油的需要.

  1. Robust Vision-Based Pose Estimation Algorithm for AN Uav with Known Gravity Vector

    Science.gov (United States)

    Kniaz, V. V.

    2016-06-01

    Accurate estimation of camera external orientation with respect to a known object is one of the central problems in photogrammetry and computer vision. In recent years this problem is gaining an increasing attention in the field of UAV autonomous flight. Such application requires a real-time performance and robustness of the external orientation estimation algorithm. The accuracy of the solution is strongly dependent on the number of reference points visible on the given image. The problem only has an analytical solution if 3 or more reference points are visible. However, in limited visibility conditions it is often needed to perform external orientation with only 2 visible reference points. In such case the solution could be found if the gravity vector direction in the camera coordinate system is known. A number of algorithms for external orientation estimation for the case of 2 known reference points and a gravity vector were developed to date. Most of these algorithms provide analytical solution in the form of polynomial equation that is subject to large errors in the case of complex reference points configurations. This paper is focused on the development of a new computationally effective and robust algorithm for external orientation based on positions of 2 known reference points and a gravity vector. The algorithm implementation for guidance of a Parrot AR.Drone 2.0 micro-UAV is discussed. The experimental evaluation of the algorithm proved its computational efficiency and robustness against errors in reference points positions and complex configurations.

  2. A Genetic Algorithm for Single Machine Scheduling with Fuzzy Processing Time and Multiple Objectives

    Institute of Scientific and Technical Information of China (English)

    吴超超; 顾幸生

    2004-01-01

    In this paper, by considering the fuzzy nature of the data in real-life problems, single machine scheduling problems with fuzzy processing time and multiple objectives are formulated and an efficient genetic algorithm which is suitable for solving these problems is proposed. As illustrative numerical examples, twenty jobs processing on a machine is considered. The feasibility and effectiveness of the proposed method have been demonstrated in the simulation.

  3. Machine Learning

    CERN Document Server

    CERN. Geneva

    2017-01-01

    Machine learning, which builds on ideas in computer science, statistics, and optimization, focuses on developing algorithms to identify patterns and regularities in data, and using these learned patterns to make predictions on new observations. Boosted by its industrial and commercial applications, the field of machine learning is quickly evolving and expanding. Recent advances have seen great success in the realms of computer vision, natural language processing, and broadly in data science. Many of these techniques have already been applied in particle physics, for instance for particle identification, detector monitoring, and the optimization of computer resources. Modern machine learning approaches, such as deep learning, are only just beginning to be applied to the analysis of High Energy Physics data to approach more and more complex problems. These classes will review the framework behind machine learning and discuss recent developments in the field.

  4. A new machine learning algorithm for removal of salt and pepper noise

    Science.gov (United States)

    Wang, Yi; Adhami, Reza; Fu, Jian

    2015-07-01

    Supervised machine learning algorithm has been extensively studied and applied to different fields of image processing in past decades. This paper proposes a new machine learning algorithm, called margin setting (MS), for restoring images that are corrupted by salt and pepper impulse noise. Margin setting generates decision surface to classify the noise pixels and non-noise pixels. After the noise pixels are detected, a modified ranked order mean (ROM) filter is used to replace the corrupted pixels for images reconstruction. Margin setting algorithm is tested with grayscale and color images for different noise densities. The experimental results are compared with those of the support vector machine (SVM) and standard median filter (SMF). The results show that margin setting outperforms these methods with higher Peak Signal-to-Noise Ratio (PSNR), lower mean square error (MSE), higher image enhancement factor (IEF) and higher Structural Similarity Index (SSIM).

  5. A Point Cloud Alignment Algorithm Based on Stereo Vision Using Random Pattern Projection

    Directory of Open Access Journals (Sweden)

    Chen-Sheng Chen

    2016-03-01

    Full Text Available This paper proposes a point cloud alignment algorithm based on stereo vision using Random Pattern Projection (RPP. In the application of stereo vision, it is rather difficult to find correspondences between stereo images of texture-less objects. To overcome this issue, RPP is used to enhance the object’s features, thus increasing the accuracy of the identified correspondences of the stereo images. In the 3D alignment algorithm, the down sample technique is used to filter out the outliers of the point cloud data to improve system efficiency. Furthermore, the extracted features of the down sample point cloud data were applied in the matching process. Finally, the object’s pose was estimated by the alignment algorithm based on object features. In experiments, the maximum error and standard deviation of rotation are respectively about 0.031°and 0.199°, while the maximum error and standard deviation of translation are respectively about 0.565 mm and 0.902 mm . The execution time for pose estimation is about 230ms.

  6. Empirical Investigation of Optimization Algorithms in Neural Machine Translation

    Directory of Open Access Journals (Sweden)

    Bahar Parnia

    2017-06-01

    Full Text Available Training neural networks is a non-convex and a high-dimensional optimization problem. In this paper, we provide a comparative study of the most popular stochastic optimization techniques used to train neural networks. We evaluate the methods in terms of convergence speed, translation quality, and training stability. In addition, we investigate combinations that seek to improve optimization in terms of these aspects. We train state-of-the-art attention-based models and apply them to perform neural machine translation. We demonstrate our results on two tasks: WMT 2016 En→Ro and WMT 2015 De→En.

  7. Machine vision approach to auto-generation of high resolution, continental-scale geomorphometric map from DEM

    Science.gov (United States)

    Jasiewicz, J.; Stepinski, T. F.

    2012-04-01

    Geomorphometric map (GM) is a map of landforms delineated exclusively on the basis of their morphology; it depicts a classification of landscape into its constituent elements. GM is a valuable tool for visual terrain analysis, but more importantly, it's a perfect terrain representation for its further algorithmic analysis. GMs themselves are auto-generated from DEM. We have developed a new technique for auto-generation of GMs that is based on the principle of machine vision. Such approach approximates more closely the mapping process of human analyst and results in an efficient generation of GMs having quality and utility superior to maps generated by a standard technique based on differential geometry. The core of the new technique is a notion of geomorphon. A geomorphon is a relief-invariant, orientation-invariant, and size-flexible abstracted elementary unit of terrain. It is calculated from DEM using simple ternary patterns defined on a neighborhood which size adapts to the character of local terrain. Geomorphons are both terrain attributes and landform types at the same time; they allow for a direct and highly efficient, single-step classification and mapping of landforms. There are 498 unique geomorphons but only a small fraction of them are found in typical natural terrain. The geomorphon-based mapping technique is implemented as a GRASS GIS extension written in ANSI C and will be available in the public domain. In order to showcase the capabilities of geomorphons we have calculated the GM for the entire conterminous United States from the 30m/pixel NED DEM. The map shows ten most abundant landforms: flat, peak, ridge, shoulder, spur, slope, hollow, footslope, valley, and pit; a lookup table was used to assign each of the remaining 488 infrequent forms to a morphologically closest mapped form. The result is a unique, never before seen, type of map that clearly shows multiple geomorphic features and indicates the underlying geologic processes. The auto

  8. An effective estimation of distribution algorithm for parallel litho machine scheduling with reticle constraints

    Institute of Scientific and Technical Information of China (English)

    周炳海

    2016-01-01

    In order to improve the scheduling efficiency of photolithography, bottleneck process of wafer fabrications in the semiconductor industry, an effective estimation of distribution algorithm is pro-posed for scheduling problems of parallel litho machines with reticle constraints, where multiple reti-cles are available for each reticle type.First, the scheduling problem domain of parallel litho ma-chines is described with reticle constraints and mathematical programming formulations are put for-ward with the objective of minimizing total weighted completion time.Second, estimation of distribu-tion algorithm is developed with a decoding scheme specially designed to deal with the reticle con-straints.Third, an insert-based local search with the first move strategy is introduced to enhance the local exploitation ability of the algorithm.Finally, simulation experiments and analysis demonstrate the effectiveness of the proposed algorithm.

  9. Machine-Learning Algorithms to Automate Morphological and Functional Assessments in 2D Echocardiography.

    Science.gov (United States)

    Narula, Sukrit; Shameer, Khader; Salem Omar, Alaa Mabrouk; Dudley, Joel T; Sengupta, Partho P

    2016-11-29

    Machine-learning models may aid cardiac phenotypic recognition by using features of cardiac tissue deformation. This study investigated the diagnostic value of a machine-learning framework that incorporates speckle-tracking echocardiographic data for automated discrimination of hypertrophic cardiomyopathy (HCM) from physiological hypertrophy seen in athletes (ATH). Expert-annotated speckle-tracking echocardiographic datasets obtained from 77 ATH and 62 HCM patients were used for developing an automated system. An ensemble machine-learning model with 3 different machine-learning algorithms (support vector machines, random forests, and artificial neural networks) was developed and a majority voting method was used for conclusive predictions with further K-fold cross-validation. Feature selection using an information gain (IG) algorithm revealed that volume was the best predictor for differentiating between HCM ands. ATH (IG = 0.24) followed by mid-left ventricular segmental (IG = 0.134) and average longitudinal strain (IG = 0.131). The ensemble machine-learning model showed increased sensitivity and specificity compared with early-to-late diastolic transmitral velocity ratio (p 13 mm. In this subgroup analysis, the automated model continued to show equal sensitivity, but increased specificity relative to early-to-late diastolic transmitral velocity ratio, e', and strain. Our results suggested that machine-learning algorithms can assist in the discrimination of physiological versus pathological patterns of hypertrophic remodeling. This effort represents a step toward the development of a real-time, machine-learning-based system for automated interpretation of echocardiographic images, which may help novice readers with limited experience. Copyright © 2016 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.

  10. Tomato grading system using machine vision technology and neuro-fuzzy networks (ANFIS

    Directory of Open Access Journals (Sweden)

    H Izadi

    2016-04-01

    Full Text Available Introduction: The quality of agricultural products is associated with their color, size and health, grading of fruits is regarded as an important step in post-harvest processing. In most cases, manual sorting inspections depends on available manpower, time consuming and their accuracy could not be guaranteed. Machine Vision is known to be a useful tool for external features measurement (e.g. size, shape, color and defects and in recent century, Machine Vision technology has been used for shape sorting. The main purpose of this study was to develop new method for tomato grading and sorting using Neuro-fuzzy system (ANFIS and to compare the accuracies of the ANFIS predicted results with those suggested by a human expert. Materials and Methods: In this study, a total of 300 image of tomatoes (Rev ground was randomly harvested, classified in 3 ripeness stage, 3 sizes and 2 health. The grading and sorting mechanism consisted of a lighting chamber (cloudy sky, lighting source and a digital camera connected to a computer. The images were recorded in a special chamber with an indirect radiation (cloudy sky with four florescent lampson each sides and camera lens was entire to lighting chamber by a hole which was only entranced to outer and covered by a camera lens. Three types of features were extracted from final images; Shap, color and texture. To receive these features, we need to have images both in color and binary format in procedure shown in Figure 1. For the first group; characteristics of the images were analysis that could offer information an surface area (S.A., maximum diameter (Dmax, minimum diameter (Dmin and average diameters. Considering to the importance of the color in acceptance of food quality by consumers, the following classification was conducted to estimate the apparent color of the tomato; 1. Classified as red (red > 90% 2. Classified as red light (red or bold pink 60-90% 3. Classified as pink (red 30-60% 4. Classified as Turning

  11. A NOVEL MULTICLASS SUPPORT VECTOR MACHINE ALGORITHM USING MEAN REVERSION AND COEFFICIENT OF VARIANCE

    Directory of Open Access Journals (Sweden)

    Bhusana Premanode

    2013-01-01

    Full Text Available Inaccuracy of a kernel function used in Support Vector Machine (SVM can be found when simulated with nonlinear and stationary datasets. To minimise the error, we propose a new multiclass SVM model using mean reversion and coefficient of variance algorithm to partition and classify imbalance in datasets. By introducing a series of test statistic, simulations of the proposed algorithm outperformed the performance of the SVM model without using multiclass SVM model.

  12. A New Hybrid Algorithm for Bankruptcy Prediction Using Switching Particle Swarm Optimization and Support Vector Machines

    OpenAIRE

    2015-01-01

    Bankruptcy prediction has been extensively investigated by data mining techniques since it is a critical issue in the accounting and finance field. In this paper, a new hybrid algorithm combining switching particle swarm optimization (SPSO) and support vector machine (SVM) is proposed to solve the bankruptcy prediction problem. In particular, a recently developed SPSO algorithm is exploited to search the optimal parameter values of radial basis function (RBF) kernel of the SVM. The new algori...

  13. A High-Speed Vision-Based Sensor for Dynamic Vibration Analysis Using Fast Motion Extraction Algorithms.

    Science.gov (United States)

    Zhang, Dashan; Guo, Jie; Lei, Xiujun; Zhu, Changan

    2016-04-22

    The development of image sensor and optics enables the application of vision-based techniques to the non-contact dynamic vibration analysis of large-scale structures. As an emerging technology, a vision-based approach allows for remote measuring and does not bring any additional mass to the measuring object compared with traditional contact measurements. In this study, a high-speed vision-based sensor system is developed to extract structure vibration signals in real time. A fast motion extraction algorithm is required for this system because the maximum sampling frequency of the charge-coupled device (CCD) sensor can reach up to 1000 Hz. Two efficient subpixel level motion extraction algorithms, namely the modified Taylor approximation refinement algorithm and the localization refinement algorithm, are integrated into the proposed vision sensor. Quantitative analysis shows that both of the two modified algorithms are at least five times faster than conventional upsampled cross-correlation approaches and achieve satisfactory error performance. The practicability of the developed sensor is evaluated by an experiment in a laboratory environment and a field test. Experimental results indicate that the developed high-speed vision-based sensor system can extract accurate dynamic structure vibration signals by tracking either artificial targets or natural features.

  14. 机器视觉的构造及应用%A Summary of the Construction and Application of Machine Vision

    Institute of Scientific and Technical Information of China (English)

    徐仲勋; 黄科程

    2015-01-01

    文章介绍了机器视觉在处理图像时的几种方法以及目标跟踪的原理,分析了机器视觉在农业、工业、医学等领域的实际应用情况,探讨了现阶段机器视觉在各个领域存在的一些问题和解决的方法,最后对机器视觉技术的应用和发展前景做了展望。%This paper introduces the concept of machine vision, structure, expounds the developing situation, such as light source, optical lens, camera, etc. This paper introduces the application of machine vision in the several methods of processing the image. Machine vision were analyzed in the agricultural, industrial, medical and other fields of application. Discusses the present some problems of machine vision in various fields and the solution method. Finally, the application of machine vision technology and development prospects were discussed.

  15. Relative Performance Evaluation of Single Chip CFA Color Reconstruction Algorithms Used in Embedded Vision Devices

    Directory of Open Access Journals (Sweden)

    B. Mahesh

    2013-02-01

    Full Text Available – Most digital cameras use a color filter array to capture the colors of the scene. Sub-sampled (Down sampled versions of the red, green, and blue components are acquired using Single Sensor Embedded vision devices with the help of Color Filter Array (CFA[1]. Hence Interpolation of the missing color samples is necessary to reconstruct a full color image. This method of interpolation is called as Demosaicing (Demosaicking. Least-Square Luma–Chroma demulti-plexing algorithm for Bayer demosaicking [2] is the most effective and efficient demosaicking technique available in the literature. As almost all companies of commercial cameras make use of this cost effective way for interpolating the missing colors and reconstructing the original image, the demosaicking arena has become a vital domain of research of embedded color vision devices[3].Hence, in this paper ,the authors aim is to analyze ,implement and evaluate the relative performance of the best known algorithms. Objective empirical value prove that LSLCDA is superior in performance

  16. Algorithm for Modeling Wire Cut Electrical Discharge Machine Parameters using Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    G.Sankara Narayanan

    2014-03-01

    Full Text Available Unconventional machining process finds lot of application in aerospace and precision industries. It is preferred over other conventional methods because of the advent of composite and high strength to weight ratio materials, complex parts and also because of its high accuracy and precision. Usually in unconventional machine tools, trial and error method is used to fix the values of process parameters which increase the production time and material wastage. A mathematical model functionally relating process parameters and operating parameters of a wire cut electric discharge machine (WEDM is developed incorporating Artificial neural network (ANN and the work piece material is SKD11 tool steel. This is accomplished by training a feed forward neural network with back propagation learning Levenberg-Marquardt algorithm. The required data used for training and testing the ANN are obtained by conducting trial runs in wire cut electric discharge machine in a small scale industry from South India. The programs for training and testing the neural network are developed, using matlab 7.0.1 package. In this work, we have considered the parameters such as thickness, time and wear as the input values and from that the values of the process parameters are related and a algorithm is arrived. Hence, the proposed algorithm reduces the time taken by trial runs to set the input process parameters of WEDM and thus reduces the production time along with reduction in material wastage. Thus the cost of machining processes is reduced and thereby increases the overall productivity.

  17. An Improved Ant Colony Algorithm for a Single-machine Scheduling Problem with Setup Times

    Institute of Scientific and Technical Information of China (English)

    YE Qiang; LIU Xinbao; LIU Lin; YANG Shanglin

    2006-01-01

    Motivated by industrial applications we study a single-machine scheduling problem in which all the jobs are mutually independent and available at time zero. The machine processes the jobs sequentially and it is not idle if there is any job to be processed. The operation of each job cannot be interrupted. The machine cannot process more than one job at a time. A setup time is needed if the machine switches from one type of job to another. The objective is to find an optimal schedule with the minimal total jobs' completion time. While the sum of jobs' processing time is always a constant, the objective is to minimize the sum of setup times. Ant colony optimization (ACO) is a meta-heuristic that has recently been applied to scheduling problem. In this paper we propose an improved ACO-Branching Ant Colony with Dynamic Perturbation (DPBAC) algorithm for the single-machine scheduling problem. DPBAC improves traditional ACO in following aspects: introducing Branching Method to choose starting points; improving state transition rules; introducing Mutation Method to shorten tours; improving pheromone updating rules and introducing Conditional Dynamic Perturbation Strategy. Computational results show that DPBAC algorithm is superior to the traditional ACO algorithm.

  18. Performance Evaluation of Machine Learning Algorithms for Urban Pattern Recognition from Multi-spectral Satellite Images

    Directory of Open Access Journals (Sweden)

    Marc Wieland

    2014-03-01

    Full Text Available In this study, a classification and performance evaluation framework for the recognition of urban patterns in medium (Landsat ETM, TM and MSS and very high resolution (WorldView-2, Quickbird, Ikonos multi-spectral satellite images is presented. The study aims at exploring the potential of machine learning algorithms in the context of an object-based image analysis and to thoroughly test the algorithm’s performance under varying conditions to optimize their usage for urban pattern recognition tasks. Four classification algorithms, Normal Bayes, K Nearest Neighbors, Random Trees and Support Vector Machines, which represent different concepts in machine learning (probabilistic, nearest neighbor, tree-based, function-based, have been selected and implemented on a free and open-source basis. Particular focus is given to assess the generalization ability of machine learning algorithms and the transferability of trained learning machines between different image types and image scenes. Moreover, the influence of the number and choice of training data, the influence of the size and composition of the feature vector and the effect of image segmentation on the classification accuracy is evaluated.

  19. 无人机视觉SLAM算法及仿真%UAV vision SLAM algorithm and simulation

    Institute of Scientific and Technical Information of China (English)

    王希彬; 赵国荣; 寇昆湖

    2012-01-01

    为将SLAM算法从地面机器人的两维空间扩展到无人机的三维环境,研究了SLAM的发展现状,指出无人机视觉SLAM面临的挑战,利用摄像机和惯导传感器的组合,建立了无人机视觉SLAM算法的数学模型;并提出了一种改进的延迟地标初始化的方法,为了便于理解和实施,简化了系统的状态和方差阵的结构,减少了存储和计算量.用EKF滤波对无人机视觉SLAM进行了仿真研究,得到了较好的估计效果,表明了SLAM在无人机上应用的可行性.%In order to expand SLAM algorithm from 2D space of ground robot to 3D environment of uninhabited aerial vehicle (UAV), the latest progress on simultaneous localization and mapping was researched, the challenge that UAV vision SLAM faces up with was pointed out, and the mathematic model of UAV vision SLAM algorithm was built with the integration of camera and IMU, Furthermore, an improved delayed landmark initialization method was proposed which was easily understood and implemented, thus the system structure of state and variance matrix was simplified, and the storage and computation quality were reduced. Simulation research was carried out by extended Kalman filter on UAV vision SLAM, and better estimation effect was gotten, demonstrating the feasibility of SLAM on UAV.

  20. Best Possible Approximation Algorithms for Single Machine Scheduling with Increasing Linear Maintenance Durations

    Directory of Open Access Journals (Sweden)

    Xuefei Shi

    2014-01-01

    Full Text Available We consider a single machine scheduling problem with multiple maintenance activities, where the maintenance duration function is of the linear form ft=a+bt with a≥0 and b>1. We propose an approximation algorithm named FFD-LS2I with a worst-case bound of 2 for problem. We also show that there is no polynomial time approximation algorithm with a worst-case bound less than 2 for the problem with b≥0 unless P=NP, which implies that the FFD-LS2I algorithm is the best possible algorithm for the case b>1 and that the FFD-LS algorithm, which is proposed in the literature, is the best possible algorithm for the case b≤1 both from the worst-case bound point of view.

  1. Parametric Optimization of Nd:YAG Laser Beam Machining Process Using Artificial Bee Colony Algorithm

    Directory of Open Access Journals (Sweden)

    Rajarshi Mukherjee

    2013-01-01

    Full Text Available Nd:YAG laser beam machining (LBM process has a great potential to manufacture intricate shaped microproducts with its unique characteristics. In practical applications, such as drilling, grooving, cutting, or scribing, the optimal combination of Nd:YAG LBM process parameters needs to be sought out to provide the desired machining performance. Several mathematical techniques, like Taguchi method, desirability function, grey relational analysis, and genetic algorithm, have already been applied for parametric optimization of Nd:YAG LBM processes, but in most of the cases, suboptimal or near optimal solutions have been reached. This paper focuses on the application of artificial bee colony (ABC algorithm to determine the optimal Nd:YAG LBM process parameters while considering both single and multiobjective optimization of the responses. A comparative study with other population-based algorithms, like genetic algorithm, particle swarm optimization, and ant colony optimization algorithm, proves the global applicability and acceptability of ABC algorithm for parametric optimization. In this algorithm, exchange of information amongst the onlooker bees minimizes the search iteration for the global optimal and avoids generation of suboptimal solutions. The results of two sample paired t-tests also demonstrate its superiority over the other optimization algorithms.

  2. A Heuristic Algorithm for the Two-Machine Flowshop Group Scheduling Problem

    Institute of Scientific and Technical Information of China (English)

    王秀利; 吴惕华

    2002-01-01

    This paper presents the two-machine flowshop group scheduling problem with the optimal objective ofmaximum lateness. A dominance rule within group and a dominance rule between groups are established. Thesedominance rules along with a previously established dominance rule are used to develop a heuristic algorithm.Experimental results are given and analyzed.

  3. Optimal Design of the Transverse Flux Machine Using a Fitted Genetic Algorithm with Real Parameters

    DEFF Research Database (Denmark)

    Argeseanu, Alin; Ritchie, Ewen; Leban, Krisztina Monika

    2012-01-01

    This paper applies a fitted genetic algorithm (GA) to the optimal design of transverse flux machine (TFM). The main goal is to provide a tool for the optimal design of TFM that is an easy to use. The GA optimizes the analytic basic design of two TFM topologies: the C-core and the U-core. First...

  4. Optimal Preemptive Online Algorithms for Scheduling with Known Largest Size on Two Uniform Machines

    Institute of Scientific and Technical Information of China (English)

    Yong HE; Yi Wei JIANG; Hao ZHOU

    2007-01-01

    In this paper, we consider the semi-online preemptive scheduling problem with known largest job sizes on two uniform machines. Our goal is to maximize the continuous period of time (starting from time zero) when both machines are busy, which is equivalent to maximizing the minimummachine completion time if idle time is not introduced. We design optimal deterministic semi-onlinealgorithms for every machine speed ratio s ∈ [1, ∞), and show that idle time is required to achieve the optimality during the assignment procedure of the algorithm for any s (s2 + 3s + 1)/(s2 + 2s + 1).The competitive ratio of the algorithms is (s2 + 3s + 1)/(s2 + 2s + 1), which matches the randomized lower bound for every s ≥ 1. Hence randomization does not help for the discussed preemptive scheduling problem.

  5. Optimization of Operation Sequence in CNC Machine Tools Using Genetic Algorithm

    Science.gov (United States)

    Abu Qudeiri, Jaber; Yamamoto, Hidehiko; Ramli, Rizauddin

    The productivity of machine tools is significantly improved by using microcomputer based CAD/CAM systems for NC program generation. Currently, many commercial CAD/CAM packages that provide automatic NC programming have been developed and applied to various cutting processes. Many cutting processes machined by CNC machine tools. In this paper, we attempt to find an efficient solution approach to determine the best sequence of operations for a set of operations that located in asymmetrical locations and different levels. In order to find the best sequence of operations that achieves the shortest cutting tool travel path (CTTP), genetic algorithm is introduced. After the sequence is optimized, the G-codes that use to code for the travel time is created. CTTP can be formulated as a special case of the traveling salesman problem (TSP). The incorporation of genetic algorithm and TSP can be included in the commercial CAD/CAM packages to optimize the CTTP during automatic generation of NC programs.

  6. Conformal interpolating algorithm based on B-spline for aspheric ultra-precision machining

    Science.gov (United States)

    Li, Chenggui; Sun, Dan; Wang, Min

    2006-02-01

    Numeric control machining and on-line compensation for aspheric surface are key techniques for ultra-precision machining. In this paper, conformal cubic B-spline interpolating curve is first applied to fit the character curve of aspheric surface. Its algorithm and process are also proposed and imitated by Matlab7.0 software. To evaluate the performance of the conformal B-spline interpolation, comparison was made between linear and circular interpolations. The result verifies this method can ensure smoothness of interpolating spline curve and preserve original shape characters. The surface quality interpolated by B-spline is higher than by line and by circle arc. The algorithm is benefit to increasing the surface form precision of workpiece during ultra-precision machining.

  7. Ant System Based Optimization Algorithm and Its Applications in Identical Parallel Machine Scheduling

    Institute of Scientific and Technical Information of China (English)

    陈义保; 姚建初; 钟毅芳

    2002-01-01

    Identical parallel machine scheduling problem for minimizing the makespan is a very important productionscheduling problem. When its scale is large, many difficulties will arise in the course of solving identical parallel machinescheduling problem. Ant system based optimization algorithm (ASBOA) has shown great advantages in solving thecombinatorial optimization problem in view of its characteristics of high efficiency and suitability for practical applications.In this paper, an ASBOA for minimizing the makespan in identical machine scheduling problem is presented. Twodifferent scale numerical examples demonstrate that the ASBOA proposed is efficient and fit for large-scale identicalparallel machine scheduling problem for minimizing the makespan, the quality of its solution has advantages over heuristicprocedure and simulated annealing method, as well as genetic algorithm.

  8. Breast Cancer Diagnosis Using Machine Learning Algorithms - A Survey

    Directory of Open Access Journals (Sweden)

    B.M.Gayathri

    2013-06-01

    Full Text Available Breast cancer has become a common factor now-a-days. Despite the fact, not all general hospitalshave the facilities to diagnose breast cancer through mammograms. Waiting for diagnosing a breastcancer for a long time may increase the possibility of the cancer spreading. Therefore a computerizedbreast cancer diagnosis has been developed to reduce the time taken to diagnose the breast cancer andreduce the death rate. This paper summarizes the survey on breast cancer diagnosis using various machinelearning algorithms and methods, which are used to improve the accuracy of predicting cancer. This surveycan also help us to know about number of papers that are implemented to diagnose the breast cancer.

  9. Prediction of Employee Turnover in Organizations using Machine Learning Algorithms

    Directory of Open Access Journals (Sweden)

    Rohit Punnoose

    2016-10-01

    Full Text Available Employee turnover has been identified as a key issue for organizations because of its adverse impact on work place productivity and long term growth strategies. To solve this problem, organizations use machine learning techniques to predict employee turnover. Accurate predictions enable organizations to take action for retention or succession planning of employees. However, the data for this modeling problem comes from HR Information Systems (HRIS; these are typically under-funded compared to the Information Systems of other domains in the organization which are directly related to its priorities. This leads to the prevalence of noise in the data that renders predictive models prone to over-fitting and hence inaccurate. This is the key challenge that is the focus of this paper, and one that has not been addressed historically. The novel contribution of this paper is to explore the application of Extreme Gradient Boosting (XGBoost technique which is more robust because of its regularization formulation. Data from the HRIS of a global retailer is used to compare XGBoost against six historically used supervised classifiers and demonstrate its significantly higher accuracy for predicting employee turnover.

  10. Network Intrusion Detection System Based On Machine Learning Algorithms

    Directory of Open Access Journals (Sweden)

    Vipin Das

    2010-12-01

    Full Text Available Network and system security is of paramount importance in the present data communication environment. Hackers and intruders can create many successful attempts to cause the crash of the networks and web services by unauthorized intrusion. New threats and associated solutions to prevent these threats are emerging together with the secured system evolution. Intrusion Detection Systems (IDS are one of these solutions. The main function of Intrusion Detection System is to protect the resources from threats. It analyzes and predicts the behaviours of users, and then these behaviours will be considered an attack or a normal behaviour. We use Rough Set Theory (RST and Support Vector Machine (SVM to detect network intrusions. First, packets are captured from the network, RST is used to pre-process the data and reduce the dimensions. The features selected by RST will be sent to SVM model to learn and test respectively. The method is effective to decrease the space density of data. The experiments compare the results with Principal Component Analysis (PCA and show RST and SVM schema could reduce the false positive rate and increase the accuracy.

  11. Real-time drogue recognition and 3D locating for UAV autonomous aerial refueling based on monocular machine vision

    Institute of Scientific and Technical Information of China (English)

    Wang Xufeng; Kong Xingwei; Zhi Jianhui; Chen Yong; Dong Xinmin

    2015-01-01

    Drogue recognition and 3D locating is a key problem during the docking phase of the autonomous aerial refueling (AAR). To solve this problem, a novel and effective method based on monocular vision is presented in this paper. Firstly, by employing computer vision with red-ring-shape feature, a drogue detection and recognition algorithm is proposed to guarantee safety and ensure the robustness to the drogue diversity and the changes in environmental condi-tions, without using a set of infrared light emitting diodes (LEDs) on the parachute part of the dro-gue. Secondly, considering camera lens distortion, a monocular vision measurement algorithm for drogue 3D locating is designed to ensure the accuracy and real-time performance of the system, with the drogue attitude provided. Finally, experiments are conducted to demonstrate the effective-ness of the proposed method. Experimental results show the performances of the entire system in contrast with other methods, which validates that the proposed method can recognize and locate the drogue three dimensionally, rapidly and precisely.

  12. Power Aware Reliable Virtual Machine Coordinator Election Algorithm in Service Oriented Systems

    Directory of Open Access Journals (Sweden)

    DanialRahdari

    2013-09-01

    Full Text Available Service oriented systems such as cloud computing are emerging widely even in people’s daily life due to its magnificent advantages for enterprise and clients. However these computing paradigms are challenged in many aspects such as power usage, availability, reliability and especially security. Hence a central controller existence is crucial in order to coordinate Virtual Machines (VM placed on physical resources. In this paper an algorithm is proposed to elect this controller among various VM which is able to tolerate multiple numbers of faults in the system and reduce power usage as well. Moreover the algorithm exchanges dramatically fewer messages than other relevant proposed algorithms.

  13. Approximation Algorithms and an FPTAS for the Single Machine Problem with Biased Tardiness Penalty

    Directory of Open Access Journals (Sweden)

    G. Moslehi

    2014-01-01

    Full Text Available This paper addresses a new performance measure for scheduling problems, entitled “biased tardiness penalty.” We study the approximability of minimum biased tardiness on a single machine, provided that all the due dates are equal. Two heuristic algorithms are developed for this problem, and it is shown that one of them has a worst-case ratio bound of 2. Then, we propose a dynamic programming algorithm and use it to design an FPTAS. The FPTAS is generated by cleaning up some states in the dynamic programming algorithm, and it requires On3/ε time.

  14. Clustering and Candidate Motif Detection in Exosomal miRNAs by Application of Machine Learning Algorithms.

    Science.gov (United States)

    Gaur, Pallavi; Chaturvedi, Anoop

    2017-07-22

    The clustering pattern and motifs give immense information about any biological data. An application of machine learning algorithms for clustering and candidate motif detection in miRNAs derived from exosomes is depicted in this paper. Recent progress in the field of exosome research and more particularly regarding exosomal miRNAs has led much bioinformatic-based research to come into existence. The information on clustering pattern and candidate motifs in miRNAs of exosomal origin would help in analyzing existing, as well as newly discovered miRNAs within exosomes. Along with obtaining clustering pattern and candidate motifs in exosomal miRNAs, this work also elaborates the usefulness of the machine learning algorithms that can be efficiently used and executed on various programming languages/platforms. Data were clustered and sequence candidate motifs were detected successfully. The results were compared and validated with some available web tools such as 'BLASTN' and 'MEME suite'. The machine learning algorithms for aforementioned objectives were applied successfully. This work elaborated utility of machine learning algorithms and language platforms to achieve the tasks of clustering and candidate motif detection in exosomal miRNAs. With the information on mentioned objectives, deeper insight would be gained for analyses of newly discovered miRNAs in exosomes which are considered to be circulating biomarkers. In addition, the execution of machine learning algorithms on various language platforms gives more flexibility to users to try multiple iterations according to their requirements. This approach can be applied to other biological data-mining tasks as well.

  15. Characterization of robotics parallel algorithms and mapping onto a reconfigurable SIMD machine

    Science.gov (United States)

    Lee, C. S. G.; Lin, C. T.

    1989-01-01

    The kinematics, dynamics, Jacobian, and their corresponding inverse computations are six essential problems in the control of robot manipulators. Efficient parallel algorithms for these computations are discussed and analyzed. Their characteristics are identified and a scheme on the mapping of these algorithms to a reconfigurable parallel architecture is presented. Based on the characteristics including type of parallelism, degree of parallelism, uniformity of the operations, fundamental operations, data dependencies, and communication requirement, it is shown that most of the algorithms for robotic computations possess highly regular properties and some common structures, especially the linear recursive structure. Moreover, they are well-suited to be implemented on a single-instruction-stream multiple-data-stream (SIMD) computer with reconfigurable interconnection network. The model of a reconfigurable dual network SIMD machine with internal direct feedback is introduced. A systematic procedure internal direct feedback is introduced. A systematic procedure to map these computations to the proposed machine is presented. A new scheduling problem for SIMD machines is investigated and a heuristic algorithm, called neighborhood scheduling, that reorders the processing sequence of subtasks to reduce the communication time is described. Mapping results of a benchmark algorithm are illustrated and discussed.

  16. Autoclassification of the Variable 3XMM Sources Using the Random Forest Machine Learning Algorithm

    CERN Document Server

    Farrell, Sean A; Lo, Kitty K

    2015-01-01

    In the current era of large surveys and massive data sets, autoclassification of astrophysical sources using intelligent algorithms is becoming increasingly important. In this paper we present the catalog of variable sources in the Third XMM-Newton Serendipitous Source catalog (3XMM) autoclassified using the Random Forest machine learning algorithm. We used a sample of manually classified variable sources from the second data release of the XMM-Newton catalogs (2XMMi-DR2) to train the classifier, obtaining an accuracy of ~92%. We also evaluated the effectiveness of identifying spurious detections using a sample of spurious sources, achieving an accuracy of ~95%. Manual investigation of a random sample of classified sources confirmed these accuracy levels and showed that the Random Forest machine learning algorithm is highly effective at automatically classifying 3XMM sources. Here we present the catalog of classified 3XMM variable sources. We also present three previously unidentified unusual sources that wer...

  17. A Hybrid Genetic Algorithm to Minimize Total Tardiness for Unrelated Parallel Machine Scheduling with Precedence Constraints

    Directory of Open Access Journals (Sweden)

    Chunfeng Liu

    2013-01-01

    Full Text Available The paper presents a novel hybrid genetic algorithm (HGA for a deterministic scheduling problem where multiple jobs with arbitrary precedence constraints are processed on multiple unrelated parallel machines. The objective is to minimize total tardiness, since delays of the jobs may lead to punishment cost or cancellation of orders by the clients in many situations. A priority rule-based heuristic algorithm, which schedules a prior job on a prior machine according to the priority rule at each iteration, is suggested and embedded to the HGA for initial feasible schedules that can be improved in further stages. Computational experiments are conducted to show that the proposed HGA performs well with respect to accuracy and efficiency of solution for small-sized problems and gets better results than the conventional genetic algorithm within the same runtime for large-sized problems.

  18. Solving machine loading problem of flexible manufacturing systems using a modified discrete firefly algorithm

    Directory of Open Access Journals (Sweden)

    Eleonora Bottani

    2017-06-01

    Full Text Available This paper proposes a modified discrete firefly algorithm (DFA applied to the machine loading problem of the flexible manufacturing systems (FMSs starting from the mathematical formulation adopted by Swarnkar & Tiwari (2004. The aim of the problem is to identify the optimal jobs sequence that simultaneously maximizes the throughput and minimizes the system unbalance according to given technological constraints (e.g. available tool slots and machining time. The results of the algorithm proposed have been compared with the existing and most recent swarm-based approaches available in the open literature using as benchmark the set of ten problems proposed by Mukhopadhyay et al. (1992. The algorithm shows results that are comparable and sometimes even better than most of the other approaches considering both the quality of the results provided and the computational times obtained.

  19. Developing Fire Detection Algorithms by Geostationary Orbiting Platforms and Machine Learning Techniques

    Science.gov (United States)

    Salvador, Pablo; Sanz, Julia; Garcia, Miguel; Casanova, Jose Luis

    2016-08-01

    Fires in general and forest fires specific are a major concern in terms of economical and biological loses. Remote sensing technologies have been focusing on developing several algorithms, adapted to a large kind of sensors, platforms and regions in order to obtain hotspots as faster as possible. The aim of this study is to establish an automatic methodology to develop hotspots detection algorithms with Spinning Enhanced Visible and Infrared Imager (SEVIRI) sensor on board Meteosat Second Generation platform (MSG) based on machine learning techniques that can be exportable to others geostationary platforms and sensors and to any area of the Earth. The sensitivity (SE), specificity (SP) and accuracy (AC) parameters have been analyzed in order to develop the final machine learning algorithm taking into account the preferences and final use of the predicted data.

  20. MIP models and hybrid algorithms for simultaneous job splitting and scheduling on unrelated parallel machines.

    Science.gov (United States)

    Eroglu, Duygu Yilmaz; Ozmutlu, H Cenk

    2014-01-01

    We developed mixed integer programming (MIP) models and hybrid genetic-local search algorithms for the scheduling problem of unrelated parallel machines with job sequence and machine-dependent setup times and with job splitting property. The first contribution of this paper is to introduce novel algorithms which make splitting and scheduling simultaneously with variable number of subjobs. We proposed simple chromosome structure which is constituted by random key numbers in hybrid genetic-local search algorithm (GAspLA). Random key numbers are used frequently in genetic algorithms, but it creates additional difficulty when hybrid factors in local search are implemented. We developed algorithms that satisfy the adaptation of results of local search into the genetic algorithms with minimum relocation operation of genes' random key numbers. This is the second contribution of the paper. The third contribution of this paper is three developed new MIP models which are making splitting and scheduling simultaneously. The fourth contribution of this paper is implementation of the GAspLAMIP. This implementation let us verify the optimality of GAspLA for the studied combinations. The proposed methods are tested on a set of problems taken from the literature and the results validate the effectiveness of the proposed algorithms.

  1. Inrush Current Simulation of Power Transformer using Machine Parameters Estimated by Design Procedure of Winding Structure and Genetic Algorithm

    Science.gov (United States)

    Tokunaga, Yoshitaka

    This paper presents estimation techniques of machine parameters for power transformer using design procedure of transformer and genetic algorithm with real coding. Especially, it is very difficult to obtain machine parameters for transformers in customers' facilities. Using estimation techniques, machine parameters could be calculated from the only nameplate data of these transformers. Subsequently, EMTP-ATP simulation of the inrush current was carried out using machine parameters estimated by techniques developed in this study and simulation results were reproduced measured waveforms.

  2. Process planning optimization on turning machine tool using a hybrid genetic algorithm with local search approach

    Directory of Open Access Journals (Sweden)

    Yuliang Su

    2015-04-01

    Full Text Available A turning machine tool is a kind of new type of machine tool that is equipped with more than one spindle and turret. The distinctive simultaneous and parallel processing abilities of turning machine tool increase the complexity of process planning. The operations would not only be sequenced and satisfy precedence constraints, but also should be scheduled with multiple objectives such as minimizing machining cost, maximizing utilization of turning machine tool, and so on. To solve this problem, a hybrid genetic algorithm was proposed to generate optimal process plans based on a mixed 0-1 integer programming model. An operation precedence graph is used to represent precedence constraints and help generate a feasible initial population of hybrid genetic algorithm. Encoding strategy based on data structure was developed to represent process plans digitally in order to form the solution space. In addition, a local search approach for optimizing the assignments of available turrets would be added to incorporate scheduling with process planning. A real-world case is used to prove that the proposed approach could avoid infeasible solutions and effectively generate a global optimal process plan.

  3. Measuring the modulation-transfer function of radiation-tolerant machine-vision system using the sum of harmonic components of different frequency

    Science.gov (United States)

    Perezyabov, Oleg A.; Maltseva, Nadezhda K.; Ilinski, Aleksandr V.

    2017-05-01

    There are a number of robotic systems that are used for nuclear power plant maintenance and it is important to ensure the necessary safety level. The machine-vision systems are applied for this purpose. There are special requirements for the image quality of these systems. To estimate the resolution of a video-system one should determine the impact of the system on the special test pattern. In this paper we describe the procedure of determining the number of the modulation transfer function values of the radiation-tolerant machine-vision systems using the test pattern, containing the sum of the harmonic functions of different frequency.

  4. Robust algorithm for arrhythmia classification in ECG using extreme learning machine

    Directory of Open Access Journals (Sweden)

    Shin Kwangsoo

    2009-10-01

    Full Text Available Abstract Background Recently, extensive studies have been carried out on arrhythmia classification algorithms using artificial intelligence pattern recognition methods such as neural network. To improve practicality, many studies have focused on learning speed and the accuracy of neural networks. However, algorithms based on neural networks still have some problems concerning practical application, such as slow learning speeds and unstable performance caused by local minima. Methods In this paper we propose a novel arrhythmia classification algorithm which has a fast learning speed and high accuracy, and uses Morphology Filtering, Principal Component Analysis and Extreme Learning Machine (ELM. The proposed algorithm can classify six beat types: normal beat, left bundle branch block, right bundle branch block, premature ventricular contraction, atrial premature beat, and paced beat. Results The experimental results of the entire MIT-BIH arrhythmia database demonstrate that the performances of the proposed algorithm are 98.00% in terms of average sensitivity, 97.95% in terms of average specificity, and 98.72% in terms of average accuracy. These accuracy levels are higher than or comparable with those of existing methods. We make a comparative study of algorithm using an ELM, back propagation neural network (BPNN, radial basis function network (RBFN, or support vector machine (SVM. Concerning the aspect of learning time, the proposed algorithm using ELM is about 290, 70, and 3 times faster than an algorithm using a BPNN, RBFN, and SVM, respectively. Conclusion The proposed algorithm shows effective accuracy performance with a short learning time. In addition we ascertained the robustness of the proposed algorithm by evaluating the entire MIT-BIH arrhythmia database.

  5. A Monocular Vision Sensor-Based Obstacle Detection Algorithm for Autonomous Robots

    Science.gov (United States)

    Lee, Tae-Jae; Yi, Dong-Hoon; Cho, Dong-Il “Dan”

    2016-01-01

    This paper presents a monocular vision sensor-based obstacle detection algorithm for autonomous robots. Each individual image pixel at the bottom region of interest is labeled as belonging either to an obstacle or the floor. While conventional methods depend on point tracking for geometric cues for obstacle detection, the proposed algorithm uses the inverse perspective mapping (IPM) method. This method is much more advantageous when the camera is not high off the floor, which makes point tracking near the floor difficult. Markov random field-based obstacle segmentation is then performed using the IPM results and a floor appearance model. Next, the shortest distance between the robot and the obstacle is calculated. The algorithm is tested by applying it to 70 datasets, 20 of which include nonobstacle images where considerable changes in floor appearance occur. The obstacle segmentation accuracies and the distance estimation error are quantitatively analyzed. For obstacle datasets, the segmentation precision and the average distance estimation error of the proposed method are 81.4% and 1.6 cm, respectively, whereas those for a conventional method are 57.5% and 9.9 cm, respectively. For nonobstacle datasets, the proposed method gives 0.0% false positive rates, while the conventional method gives 17.6%. PMID:26938540

  6. A Monocular Vision Sensor-Based Obstacle Detection Algorithm for Autonomous Robots

    Directory of Open Access Journals (Sweden)

    Tae-Jae Lee

    2016-03-01

    Full Text Available This paper presents a monocular vision sensor-based obstacle detection algorithm for autonomous robots. Each individual image pixel at the bottom region of interest is labeled as belonging either to an obstacle or the floor. While conventional methods depend on point tracking for geometric cues for obstacle detection, the proposed algorithm uses the inverse perspective mapping (IPM method. This method is much more advantageous when the camera is not high off the floor, which makes point tracking near the floor difficult. Markov random field-based obstacle segmentation is then performed using the IPM results and a floor appearance model. Next, the shortest distance between the robot and the obstacle is calculated. The algorithm is tested by applying it to 70 datasets, 20 of which include nonobstacle images where considerable changes in floor appearance occur. The obstacle segmentation accuracies and the distance estimation error are quantitatively analyzed. For obstacle datasets, the segmentation precision and the average distance estimation error of the proposed method are 81.4% and 1.6 cm, respectively, whereas those for a conventional method are 57.5% and 9.9 cm, respectively. For nonobstacle datasets, the proposed method gives 0.0% false positive rates, while the conventional method gives 17.6%.

  7. TURING MACHINE AS UNIVERSAL ALGORITHM EXECUTOR AND ITS APPLICATION IN THE PROCESS OF HIGH-SCHOOL STUDENTS` ADVANCED STUDY OF ALGORITHMIZATION AND PROGRAMMING FUNDAMENTALS

    Directory of Open Access Journals (Sweden)

    Oleksandr B. Yashchyk

    2016-05-01

    Full Text Available The article discusses the importance of studying the notion of algorithm and its formal specification using Turing machines. In the article it was identified the basic hypothesis of the theory of algorithms for Turing as well as reviewed scientific research of modern scientists devoted to this issue and found the main principles of the Turing machine as an abstract mathematical model. The process of forming information competencies components, information culture and students` logical thinking development with the inclusion of the topic “Study and Application of Turing machine as Universal Algorithm Executor” in the course of Informatics was analyzed.

  8. Design on special robot used for colliery based on machine vision and independent suspension system%基于机器视觉和独立悬挂系统的煤矿特种机器人设计

    Institute of Scientific and Technical Information of China (English)

    赵建伟; 班钰; 王俊懿

    2016-01-01

    由于煤矿井下煤尘存在爆炸性、煤层中溢出的瓦斯也具有威胁性,所以采煤工作环境十分恶劣和危险。为此利用 LabVIEW 和 IMAQ Vision 构建了基于彩色图像二值化的机器视觉图像处理算法,提出了一种基于机器视觉和独立悬挂系统的煤矿特种机器人。该机器人四轮能够独立运动,同时兼备灵活的探测抓取和越障能力,应用于煤矿勘探以及利用视觉进行物体识别方面,并通过抓球和爬楼梯试验验证了该机构的可行性。%In view of severe and dangerous surroundings of coal mining operation, the paper applied LabVIEW and IMAQ Vision to construct the machine vision image processing algorithm based on color image binarization, and proposed a special robot used for colliery based on machine vision and independent suspension system. The four wheels of the robot moved independently, and the robot possessed flexible grasping ability and obstacle climbing ability. It could be applied to colliery exploration and object recognition via vision. Finally, the robot proved feasible via ball grasping and stair climbing test.

  9. Hybrid Swarm Algorithms for Parameter Identification of an Actuator Model in an Electrical Machine

    Directory of Open Access Journals (Sweden)

    Ying Wu

    2011-01-01

    Full Text Available Efficient identification and control algorithms are needed, when active vibration suppression techniques are developed for industrial machines. In the paper a new actuator for reducing rotor vibrations in electrical machines is investigated. Model-based control is needed in designing the algorithm for voltage input, and therefore proper models for the actuator must be available. In addition to the traditional prediction error method a new knowledge-based Artificial Fish-Swarm optimization algorithm (AFA with crossover, CAFAC, is proposed to identify the parameters in the new model. Then, in order to obtain a fast convergence of the algorithm in the case of a 30 kW two-pole squirrel cage induction motor, we combine the CAFAC and Particle Swarm Optimization (PSO to identify parameters of the machine to construct a linear time-invariant(LTI state-space model. Besides that, the prediction error method (PEM is also employed to identify the induction motor to produce a black box model with correspondence to input-output measurements.

  10. Artificial immune algorithm implementation for optimized multi-axis sculptured surface CNC machining

    Science.gov (United States)

    Fountas, N. A.; Kechagias, J. D.; Vaxevanidis, N. M.

    2016-11-01

    This paper presents the results obtained by the implementation of an artificial immune algorithm to optimize standard multi-axis tool-paths applied to machine free-form surfaces. The investigation for its applicability was based on a full factorial experimental design addressing the two additional axes for tool inclination as independent variables whilst a multi-objective response was formulated by taking into consideration surface deviation and tool path time; objectives assessed directly from computer-aided manufacturing environment A standard sculptured part was developed by scratch considering its benchmark specifications and a cutting-edge surface machining tool-path was applied to study the effects of the pattern formulated when dynamically inclining a toroidal end-mill and guiding it towards the feed direction under fixed lead and tilt inclination angles. The results obtained form the series of the experiments were used for the fitness function creation the algorithm was about to sequentially evaluate. It was found that the artificial immune algorithm employed has the ability of attaining optimal values for inclination angles facilitating thus the complexity of such manufacturing process and ensuring full potentials in multi-axis machining modelling operations for producing enhanced CNC manufacturing programs. Results suggested that the proposed algorithm implementation may reduce the mean experimental objective value to 51.5%

  11. 基于支持向量机的分段线性学习方法%A Subsection Learning Algorithm Based on Support Vector Machines

    Institute of Scientific and Technical Information of China (English)

    杨强; 吴中福; 王茜

    2003-01-01

    In this paper, we discuss drawback of traditional subsection learning algorithm in pattern recognition and exiting support vector machines (including kernel functions), the necessity of using subsection learning algorithm based on support vector machines as well as. In turn, a subsection learning algorithm based on support vector machines, is proposed in this paper.

  12. A Machine-Checked Proof of A State-Space Construction Algorithm

    Science.gov (United States)

    Catano, Nestor; Siminiceanu, Radu I.

    2010-01-01

    This paper presents the correctness proof of Saturation, an algorithm for generating state spaces of concurrent systems, implemented in the SMART tool. Unlike the Breadth First Search exploration algorithm, which is easy to understand and formalise, Saturation is a complex algorithm, employing a mutually-recursive pair of procedures that compute a series of non-trivial, nested local fixed points, corresponding to a chaotic fixed point strategy. A pencil-and-paper proof of Saturation exists, but a machine checked proof had never been attempted. The key element of the proof is the characterisation theorem of saturated nodes in decision diagrams, stating that a saturated node represents a set of states encoding a local fixed-point with respect to firing all events affecting only the node s level and levels below. For our purpose, we have employed the Prototype Verification System (PVS) for formalising the Saturation algorithm, its data structures, and for conducting the proofs.

  13. Makespan Minimization for The Identical Machine Parallel Shop with Sequence Dependent Setup Times Using a Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    Salazar-Hornig E.

    2013-01-01

    Full Text Available A genetic algorithm for the parallel shop with identical machines scheduling problem with sequence dependent setup times and makespan (Cmáx minimization is presented. The genetic algorithm is compared with other heuristic methods using a randomly generated test problem set. A local improvement procedure in the evolutionary process of the genetic algorithm is introduced, which significantly improves its performance.

  14. Selected Operations, Algorithms, and Applications of n-Tape Weighted Finite-State Machines

    CERN Document Server

    Kempe, André

    2011-01-01

    A weighted finite-state machine with n tapes (n-WFSM) defines a rational relation on n strings. It is a generalization of weighted acceptors (one tape) and transducers (two tapes). After recalling some basic definitions about n-ary weighted rational relations and n-WFSMs, we summarize some central operations on these relations and machines, such as join and auto-intersection. Unfortunately, due to Post's Correspondence Problem, a fully general join or auto-intersection algorithm cannot exist. We recall a restricted algorithm for a class of n-WFSMs. Through a series of practical applications, we finally investigate the augmented descriptive power of n-WFSMs and their join, compared to classical transducers and their composition. Some applications are not feasible with the latter. The series includes: the morphological analysis of Semitic languages, the preservation of intermediate results in transducer cascades, the induction of morphological rules from corpora, the alignment of lexicon entries, the automatic ...

  15. Hybrid Black Hole Algorithm for Bi-Criteria Job Scheduling on Parallel Machines

    Directory of Open Access Journals (Sweden)

    Kawal Jeet

    2016-04-01

    Full Text Available Nature-inspired algorithms are recently being appreciated for solving complex optimization and engineering problems. Black hole algorithm is one of the recent nature-inspired algorithms that have obtained inspiration from black hole theory of universe. In this paper, four formulations of multi-objective black hole algorithm have been developed by using combination of weighted objectives, use of secondary storage for managing possible solutions and use of Genetic Algorithm (GA. These formulations are further applied for scheduling jobs on parallel machines while optimizing bi-criteria namely maximum tardiness and weighted flow time. It has been empirically verified that GA based multi-objective Black Hole algorithms leads to better results as compared to their counterparts. Also the use of combination of secondary storage and GA further improves the resulting job sequence. The proposed algorithms are further compared to some of the existing algorithms, and empirically found to be better. The results have been validated by numerical illustrations and statistical tests.

  16. Heuristic algorithms for solving of the tool routing problem for CNC cutting machines

    Science.gov (United States)

    Chentsov, P. A.; Petunin, A. A.; Sesekin, A. N.; Shipacheva, E. N.; Sholohov, A. E.

    2015-11-01

    The article is devoted to the problem of minimizing the path of the cutting tool to shape cutting machines began. This problem can be interpreted as a generalized traveling salesman problem. Earlier version of the dynamic programming method to solve this problem was developed. Unfortunately, this method allows to process an amount not exceeding thirty circuits. In this regard, the task of constructing quasi-optimal route becomes relevant. In this paper we propose options for quasi-optimal greedy algorithms. Comparison of the results of exact and approximate algorithms is given.

  17. Identifying presence of correlated errors in GRACE monthly harmonic coefficients using machine learning algorithms

    Science.gov (United States)

    Piretzidis, Dimitrios; Sra, Gurveer; Karantaidis, George; Sideris, Michael G.

    2017-04-01

    A new method for identifying correlated errors in Gravity Recovery and Climate Experiment (GRACE) monthly harmonic coefficients has been developed and tested. Correlated errors are present in the differences between monthly GRACE solutions, and can be suppressed using a de-correlation filter. In principle, the de-correlation filter should be implemented only on coefficient series with correlated errors to avoid losing useful geophysical information. In previous studies, two main methods of implementing the de-correlation filter have been utilized. In the first one, the de-correlation filter is implemented starting from a specific minimum order until the maximum order of the monthly solution examined. In the second one, the de-correlation filter is implemented only on specific coefficient series, the selection of which is based on statistical testing. The method proposed in the present study exploits the capabilities of supervised machine learning algorithms such as neural networks and support vector machines (SVMs). The pattern of correlated errors can be described by several numerical and geometric features of the harmonic coefficient series. The features of extreme cases of both correlated and uncorrelated coefficients are extracted and used for the training of the machine learning algorithms. The trained machine learning algorithms are later used to identify correlated errors and provide the probability of a coefficient series to be correlated. Regarding SVMs algorithms, an extensive study is performed with various kernel functions in order to find the optimal training model for prediction. The selection of the optimal training model is based on the classification accuracy of the trained SVM algorithm on the same samples used for training. Results show excellent performance of all algorithms with a classification accuracy of 97% - 100% on a pre-selected set of training samples, both in the validation stage of the training procedure and in the subsequent use of

  18. An Optimal Algorithm for a Class of Parallel Machines Scheduling Problem

    Institute of Scientific and Technical Information of China (English)

    常俊林; 邵惠鹤

    2004-01-01

    This paper considers the parallel machines scheduling problem where jobs are subject to different release times. A constructive heuristic is first proposed to solve the problem in a modest amount of computer time. In general, the quality of the solutions provided by heuristics degrades with the increase of the probiem's scale. Combined the global search ability of genetic algorithm, this paper proposed a hybrid heuristic to improve the quality of solutions further. The computational results show that the hybrid heuristic combines the advantages of heuristic and genetic algorithm effectively and can provide very good solutions to some large problems in a reasonable amount of computer time.

  19. Processing of rock core microtomography images: Using seven different machine learning algorithms

    Science.gov (United States)

    Chauhan, Swarup; Rühaak, Wolfram; Khan, Faisal; Enzmann, Frieder; Mielke, Philipp; Kersten, Michael; Sass, Ingo

    2016-01-01

    The abilities of machine learning algorithms to process X-ray microtomographic rock images were determined. The study focused on the use of unsupervised, supervised, and ensemble clustering techniques, to segment X-ray computer microtomography rock images and to estimate the pore spaces and pore size diameters in the rocks. The unsupervised k-means technique gave the fastest processing time and the supervised least squares support vector machine technique gave the slowest processing time. Multiphase assemblages of solid phases (minerals and finely grained minerals) and the pore phase were found on visual inspection of the images. In general, the accuracy in terms of porosity values and pore size distribution was found to be strongly affected by the feature vectors selected. Relative porosity average value of 15.92±1.77% retrieved from all the seven machine learning algorithm is in very good agreement with the experimental results of 17±2%, obtained using gas pycnometer. Of the supervised techniques, the least square support vector machine technique is superior to feed forward artificial neural network because of its ability to identify a generalized pattern. In the ensemble classification techniques boosting technique converged faster compared to bragging technique. The k-means technique outperformed the fuzzy c-means and self-organized maps techniques in terms of accuracy and speed.

  20. SEMI-DEFINITE RELAXATION ALGORITHM FOR SINGLE MACHINE SCHEDULING WITH CONTROLLABLE PROCESSING TIMES

    Institute of Scientific and Technical Information of China (English)

    CHEN FENG; ZHANG LIANSHENG

    2005-01-01

    The authors present a semi-definite relaxation algorithm for the scheduling problem with controllable times on a single machine. Their approach shows how to relate this problem with the maximum vertex-cover problem with kernel constraints (MKVC).The established relationship enables to transfer the approximate solutions of MKVCinto the approximate solutions for the scheduling problem. Then, they show how to obtain an integer approximate solution for MKVC based on the semi-definite relaxation and randomized rounding technique.

  1. Solar Flare Prediction Model with Three Machine-learning Algorithms using Ultraviolet Brightening and Vector Magnetograms

    Science.gov (United States)

    Nishizuka, N.; Sugiura, K.; Kubo, Y.; Den, M.; Watari, S.; Ishii, M.

    2017-02-01

    We developed a flare prediction model using machine learning, which is optimized to predict the maximum class of flares occurring in the following 24 hr. Machine learning is used to devise algorithms that can learn from and make decisions on a huge amount of data. We used solar observation data during the period 2010–2015, such as vector magnetograms, ultraviolet (UV) emission, and soft X-ray emission taken by the Solar Dynamics Observatory and the Geostationary Operational Environmental Satellite. We detected active regions (ARs) from the full-disk magnetogram, from which ∼60 features were extracted with their time differentials, including magnetic neutral lines, the current helicity, the UV brightening, and the flare history. After standardizing the feature database, we fully shuffled and randomly separated it into two for training and testing. To investigate which algorithm is best for flare prediction, we compared three machine-learning algorithms: the support vector machine, k-nearest neighbors (k-NN), and extremely randomized trees. The prediction score, the true skill statistic, was higher than 0.9 with a fully shuffled data set, which is higher than that for human forecasts. It was found that k-NN has the highest performance among the three algorithms. The ranking of the feature importance showed that previous flare activity is most effective, followed by the length of magnetic neutral lines, the unsigned magnetic flux, the area of UV brightening, and the time differentials of features over 24 hr, all of which are strongly correlated with the flux emergence dynamics in an AR.

  2. USE OF GENETIC ALGORITHMS TO SEQUENCE THE MACHINING OPERATIONS OF PARTS

    Institute of Scientific and Technical Information of China (English)

    1998-01-01

    Genetic algorithms are used to determine the sequence of parts\\' machining operations.First, the feature operation units are encoded to a genetic string with natural digits, and the sequencing constraint knowledge is represented by the fitness functions based on four types of constraints, and then, through the implementation of the genetic operators including reproduction, crossover and mutation, a rational sequence of operations is being searched and can be found finally.Such sequence is also optimal.

  3. Performance Evaluation of Machine Learning Algorithms for Urban Pattern Recognition from Multi-spectral Satellite Images

    OpenAIRE

    Marc Wieland; Massimiliano Pittore

    2014-01-01

    In this study, a classification and performance evaluation framework for the recognition of urban patterns in medium (Landsat ETM, TM and MSS) and very high resolution (WorldView-2, Quickbird, Ikonos) multi-spectral satellite images is presented. The study aims at exploring the potential of machine learning algorithms in the context of an object-based image analysis and to thoroughly test the algorithm’s performance under varying conditions to optimize their usage for urban pattern recognitio...

  4. Implementing linear algebra algorithms for dense matrices on a vector pipeline machine

    Energy Technology Data Exchange (ETDEWEB)

    Dongarra, J.J.; Gustavson, F.G.; Karp, A.

    1984-01-01

    The authors examine common implementations of linear algebra algorithms, such as matrix-vector multiplication, matrix-matrix multiplication and the solution of linear equations. The different versions are examined for efficiency on a computer architecture which uses vector processing and has pipelined instruction execution. By using the advanced architectural features of such machines, one can usually achieve maximum performance, and tremendous improvements in terms of execution speed can be seen over conventional computers. 17 references.

  5. Teaching a Machine to Feel Postoperative Pain: Combining High-Dimensional Clinical Data with Machine Learning Algorithms to Forecast Acute Postoperative Pain.

    Science.gov (United States)

    Tighe, Patrick J; Harle, Christopher A; Hurley, Robert W; Aytug, Haldun; Boezaart, Andre P; Fillingim, Roger B

    2015-07-01

    Given their ability to process highly dimensional datasets with hundreds of variables, machine learning algorithms may offer one solution to the vexing challenge of predicting postoperative pain. Here, we report on the application of machine learning algorithms to predict postoperative pain outcomes in a retrospective cohort of 8,071 surgical patients using 796 clinical variables. Five algorithms were compared in terms of their ability to forecast moderate to severe postoperative pain: Least Absolute Shrinkage and Selection Operator (LASSO), gradient-boosted decision tree, support vector machine, neural network, and k-nearest neighbor (k-NN), with logistic regression included for baseline comparison. In forecasting moderate to severe postoperative pain for postoperative day (POD) 1, the LASSO algorithm, using all 796 variables, had the highest accuracy with an area under the receiver-operating curve (ROC) of 0.704. Next, the gradient-boosted decision tree had an ROC of 0.665 and the k-NN algorithm had an ROC of 0.643. For POD 3, the LASSO algorithm, using all variables, again had the highest accuracy, with an ROC of 0.727. Logistic regression had a lower ROC of 0.5 for predicting pain outcomes on POD 1 and 3. Machine learning algorithms, when combined with complex and heterogeneous data from electronic medical record systems, can forecast acute postoperative pain outcomes with accuracies similar to methods that rely only on variables specifically collected for pain outcome prediction. Wiley Periodicals, Inc.

  6. Discrete-State-Based Vision Navigation Control Algorithm for One Bipedal Robot

    Directory of Open Access Journals (Sweden)

    Dunwen Wei

    2015-01-01

    Full Text Available Navigation with the specific objective can be defined by specifying desired timed trajectory. The concept of desired direction field is proposed to deal with such navigation problem. To lay down a principled discussion of the accuracy and efficiency of navigation algorithms, strictly quantitative definitions of tracking error, actuator effect, and time efficiency are established. In this paper, one vision navigation control method based on desired direction field is proposed. This proposed method uses discrete image sequences to form discrete state space, which is especially suitable for bipedal walking robots with single camera walking on a free-barrier plane surface to track the specific objective without overshoot. The shortest path method (SPM is proposed to design such direction field with the highest time efficiency. However, one improved control method called canonical piecewise-linear function (PLF is proposed. In order to restrain the noise disturbance from the camera sensor, the band width control method is presented to significantly decrease the error influence. The robustness and efficiency of the proposed algorithm are illustrated through a number of computer simulations considering the error from camera sensor. Simulation results show that the robustness and efficiency can be balanced by choosing the proper controlling value of band width.

  7. Semi-supervised least squares support vector machine algorithm: application to offshore oil reservoir

    Science.gov (United States)

    Luo, Wei-Ping; Li, Hong-Qi; Shi, Ning

    2016-06-01

    At the early stages of deep-water oil exploration and development, fewer and further apart wells are drilled than in onshore oilfields. Supervised least squares support vector machine algorithms are used to predict the reservoir parameters but the prediction accuracy is low. We combined the least squares support vector machine (LSSVM) algorithm with semi-supervised learning and established a semi-supervised regression model, which we call the semi-supervised least squares support vector machine (SLSSVM) model. The iterative matrix inversion is also introduced to improve the training ability and training time of the model. We use the UCI data to test the generalization of a semi-supervised and a supervised LSSVM models. The test results suggest that the generalization performance of the LSSVM model greatly improves and with decreasing training samples the generalization performance is better. Moreover, for small-sample models, the SLSSVM method has higher precision than the semi-supervised K-nearest neighbor (SKNN) method. The new semisupervised LSSVM algorithm was used to predict the distribution of porosity and sandstone in the Jingzhou study area.

  8. Machines first, humans second: on the importance of algorithmic interpretation of open chemistry data.

    Science.gov (United States)

    Clark, Alex M; Williams, Antony J; Ekins, Sean

    2015-01-01

    The current rise in the use of open lab notebook techniques means that there are an increasing number of scientists who make chemical information freely and openly available to the entire community as a series of micropublications that are released shortly after the conclusion of each experiment. We propose that this trend be accompanied by a thorough examination of data sharing priorities. We argue that the most significant immediate benefactor of open data is in fact chemical algorithms, which are capable of absorbing vast quantities of data, and using it to present concise insights to working chemists, on a scale that could not be achieved by traditional publication methods. Making this goal practically achievable will require a paradigm shift in the way individual scientists translate their data into digital form, since most contemporary methods of data entry are designed for presentation to humans rather than consumption by machine learning algorithms. We discuss some of the complex issues involved in fixing current methods, as well as some of the immediate benefits that can be gained when open data is published correctly using unambiguous machine readable formats. Graphical AbstractLab notebook entries must target both visualisation by scientists and use by machine learning algorithms.

  9. A Comparison Study of Machine Learning Based Algorithms for Fatigue Crack Growth Calculation.

    Science.gov (United States)

    Wang, Hongxun; Zhang, Weifang; Sun, Fuqiang; Zhang, Wei

    2017-05-18

    The relationships between the fatigue crack growth rate ( d a / d N ) and stress intensity factor range ( Δ K ) are not always linear even in the Paris region. The stress ratio effects on fatigue crack growth rate are diverse in different materials. However, most existing fatigue crack growth models cannot handle these nonlinearities appropriately. The machine learning method provides a flexible approach to the modeling of fatigue crack growth because of its excellent nonlinear approximation and multivariable learning ability. In this paper, a fatigue crack growth calculation method is proposed based on three different machine learning algorithms (MLAs): extreme learning machine (ELM), radial basis function network (RBFN) and genetic algorithms optimized back propagation network (GABP). The MLA based method is validated using testing data of different materials. The three MLAs are compared with each other as well as the classical two-parameter model ( K * approach). The results show that the predictions of MLAs are superior to those of K * approach in accuracy and effectiveness, and the ELM based algorithms show overall the best agreement with the experimental data out of the three MLAs, for its global optimization and extrapolation ability.

  10. DEVELOPMENT OF A MACHINE VISION SYSTEM FOR WEED DETECTION DURING BOTH OF OFF-SEASON AND IN-SEASON IN BROADACRE NO-TILLAGE CROPPING LANDS

    Directory of Open Access Journals (Sweden)

    Huajian Liu

    2014-01-01

    Full Text Available More than half of the Australian cropping land is no-tillage and weed control within continuous no-tillage agricultural cropping area is becoming more and more difficult. A major problem is that the heavy herbicide usage causes some of more prolific weeds becoming more resistant to the regular herbicides and therefore more powerful and more expensive options are being pursued. To overcome such problems with aiming at the reduction of herbicide usage, this proposed research focuses on developing a machine vision system which can detect and mapping weeds or do spot spray. The weed detection methods described in this study include three aspects which are image acquisition, a new green plant detection algorithm using hybrid spectral indices and a new inter-row weed detection method taking the advantage of the location of the crop rows. The developed method could detect the weeds both during the non-growing summer period and also within the growing season until the canopy of the crop has closed. The design of the methods focuses on overcoming the challenges of the complex no-tillage background, the faster image acquisition speed and quicker processing time for real-time spot spray. The experiment results show that the proposed method are more suitable for the weed detection in the no-tillage background than the existing methods and could be used as a powerful tool for the weed control.

  11. SURFACE QUALITY INSPECTION OF CERAMIC TILES BY MACHINE VISION%陶瓷砖表面质量视觉检测系统研究

    Institute of Scientific and Technical Information of China (English)

    李庆利; 郭彩玲; 张向红

    2011-01-01

    重点介绍了机器视觉技术在陶瓷砖表面质量检测中的应用.系统采用面阵摄象机作为测量工具,应用方向算子进行对目标边缘的定位和跟踪,以便获得完整、精确、封闭的目标边缘.实现了对陶瓷砖的边直度、直角度、缺边和缺角等项目的非接触检测.%In this paper, an on-line machine vision system for surface quality inspection of ceramic tiles is introduced, which grabs the images scanned by area-array cameras. The image processing algorithm which uses direction masks is used to locate the edge points exactly. The surface quality of ceramic tiles is measured by the real-time, high precision and non-contact method, which can detect straightness of sides, deviation from rectangularity, rough edge, chip and so on.

  12. STUDY ON SURFACE QUALITY OF CERAMIC TILES BY MACHINE VISION%陶瓷砖表面质量视觉检测系统研究

    Institute of Scientific and Technical Information of China (English)

    李庆利; 郭彩玲; 张向红

    2011-01-01

    In this paper an on-line machine vision system for surface quality of ceramic tiles is introduced, which grabs the images scanned by area-array camera.The image processing algorithm which uses direction masks is used to locate the edge points exactly.The surface quality of ceramic tiles is measured with the real-time, high precision and non-contact method, which includes straightness of sides,deviation from rectangularity, rough edge,chip and so on.%重点介绍了机器视觉技术在陶瓷砖表面质量检测中的应用.系统采用面阵摄像机作为测量工具,应用方向算子进行对目标边缘的定位和跟踪,以便获得完整、精确、封闭的目标边缘.实现了对陶瓷砖的边直度、直角度、缺边和缺角等项目的非接触检测.

  13. Machine vision process monitoring on a poultry processing kill line: results from an implementation

    Science.gov (United States)

    Usher, Colin; Britton, Dougl; Daley, Wayne; Stewart, John

    2005-11-01

    Researchers at the Georgia Tech Research Institute designed a vision inspection system for poultry kill line sorting with the potential for process control at various points throughout a processing facility. This system has been successfully operating in a plant for over two and a half years and has been shown to provide multiple benefits. With the introduction of HACCP-Based Inspection Models (HIMP), the opportunity for automated inspection systems to emerge as viable alternatives to human screening is promising. As more plants move to HIMP, these systems have the great potential for augmenting a processing facilities visual inspection process. This will help to maintain a more consistent and potentially higher throughput while helping the plant remain within the HIMP performance standards. In recent years, several vision systems have been designed to analyze the exterior of a chicken and are capable of identifying Food Safety 1 (FS1) type defects under HIMP regulatory specifications. This means that a reliable vision system can be used in a processing facility as a carcass sorter to automatically detect and divert product that is not suitable for further processing. This improves the evisceration line efficiency by creating a smaller set of features that human screeners are required to identify. This can reduce the required number of screeners or allow for faster processing line speeds. In addition to identifying FS1 category defects, the Georgia Tech vision system can also identify multiple "Other Consumer Protection" (OCP) category defects such as skin tears, bruises, broken wings, and cadavers. Monitoring this data in an almost real-time system allows the processing facility to address anomalies as soon as they occur. The Georgia Tech vision system can record minute-by-minute averages of the following defects: Septicemia Toxemia, cadaver, over-scald, bruises, skin tears, and broken wings. In addition to these defects, the system also records the length and

  14. Machine learning algorithm for automatic detection of CT-identifiable hyperdense lesions associated with traumatic brain injury

    Science.gov (United States)

    Keshavamurthy, Krishna N.; Leary, Owen P.; Merck, Lisa H.; Kimia, Benjamin; Collins, Scott; Wright, David W.; Allen, Jason W.; Brock, Jeffrey F.; Merck, Derek

    2017-03-01

    Traumatic brain injury (TBI) is a major cause of death and disability in the United States. Time to treatment is often related to patient outcome. Access to cerebral imaging data in a timely manner is a vital component of patient care. Current methods of detecting and quantifying intracranial pathology can be time-consuming and require careful review of 2D/3D patient images by a radiologist. Additional time is needed for image protocoling, acquisition, and processing. These steps often occur in series, adding more time to the process and potentially delaying time-dependent management decisions for patients with traumatic brain injury. Our team adapted machine learning and computer vision methods to develop a technique that rapidly and automatically detects CT-identifiable lesions. Specifically, we use scale invariant feature transform (SIFT)1 and deep convolutional neural networks (CNN)2 to identify important image features that can distinguish TBI lesions from background data. Our learning algorithm is a linear support vector machine (SVM)3. Further, we also employ tools from topological data analysis (TDA) for gleaning insights into the correlation patterns between healthy and pathological data. The technique was validated using 409 CT scans of the brain, acquired via the Progesterone for the Treatment of Traumatic Brain Injury phase III clinical trial (ProTECT_III) which studied patients with moderate to severe TBI4. CT data were annotated by a central radiologist and included patients with positive and negative scans. Additionally, the largest lesion on each positive scan was manually segmented. We reserved 80% of the data for training the SVM and used the remaining 20% for testing. Preliminary results are promising with 92.55% prediction accuracy (sensitivity = 91.15%, specificity = 93.45%), indicating the potential usefulness of this technique in clinical scenarios.

  15. A fast 3-D object recognition algorithm for the vision system of a special-purpose dexterous manipulator

    Science.gov (United States)

    Hung, Stephen H. Y.

    1989-01-01

    A fast 3-D object recognition algorithm that can be used as a quick-look subsystem to the vision system for the Special-Purpose Dexterous Manipulator (SPDM) is described. Global features that can be easily computed from range data are used to characterize the images of a viewer-centered model of an object. This algorithm will speed up the processing by eliminating the low level processing whenever possible. It may identify the object, reject a set of bad data in the early stage, or create a better environment for a more powerful algorithm to carry the work further.

  16. An Automated Recognition of Fake or Destroyed Indian Currency Notes in Machine Vision

    Directory of Open Access Journals (Sweden)

    Sanjana

    2012-04-01

    Full Text Available Almost every country in the world face the problem of counterfeitcurrency notes, but in India the problem is acute as the country ishit hard by this evil practice. Fake notes in India in denominationsof Rs.100, 500 and 1000 are being flooded into the system. Inorder to deal with such type of problems, an automated recognitionof currency notes in introduced by with the help of featureextraction, classification based in SVM, Neural Nets, and heuristicapproach. This technique is also subjected with the computervision where all processing with the image is done by machine.The machine is fitted with a CDD camera which will scan theimage of the currency note considering the dimensions of thebanknote and software will process the image segments with thehelp of SVM and character recognition methods. ANN is alsointroduced in this paper to train the data and classify the segmentsusing its datasets. To implement this design we are dealing withMATLAB Tool.

  17. Are you bleeding? Validation of a machine-learning algorithm for determination of blood volume status: application to remote triage

    National Research Council Canada - National Science Library

    Caroline A. Rickards; Nisarg Vyas; Kathy L. Ryan; Kevin R. Ward; David Andre; Gennifer M. Hurst; Chelsea R. Barrera; Victor A. Convertino

    2014-01-01

    .... The purpose of this study was to test the hypothesis that low-level physiological signals can be used to develop a machine-learning algorithm for tracking changes in central blood volume that will...

  18. Sensitivity study using machine learning algorithms on simulated r-mode gravitational wave signals from newborn neutron stars

    CERN Document Server

    Mytidis, Antonis; Panagopoulos, Orestis P; Whiting, Bernard

    2015-01-01

    This is a follow-up sensitivity study on r-mode gravitational wave signals from newborn neutron stars illustrating the applicability of machine learning algorithms for the detection of long-lived gravitational-wave transients. In this sensitivity study we examine three machine learning algorithms (MLAs): artificial neural networks (ANNs), support vector machines (SVMs) and constrained subspace classifiers (CSCs). The objective of this study is to compare the detection efficiency that MLAs can achieve with the efficiency of conventional detection algorithms discussed in an earlier paper. Comparisons are made using 2 distinct r-mode waveforms. For the training of the MLAs we assumed that some information about the distance to the source is given so that the training was performed over distance ranges not wider than half an order of magnitude. The results of this study suggest that machine learning algorithms are suitable for the detection of long-lived gravitational-wave transients and that when assuming knowle...

  19. A Planar-Dimensions Machine Vision Measurement Method Based on Lens Distortion Correction

    Directory of Open Access Journals (Sweden)

    Qiucheng Sun

    2013-01-01

    Full Text Available Lens distortion practically presents in a real optical imaging system causing nonuniform geometric distortion in the images and gives rise to additional errors in the vision measurement. In this paper, a planar-dimensions vision measurement method is proposed by improving camera calibration, in which the lens distortion is corrected on the pixel plane of image. The method can be divided into three steps: firstly, the feature points, only in the small central region of the image, are used to get a more accurate perspective projection model; secondly, rather than defining a uniform model, the smoothing spline function is used to describe the lens distortion in the measurement region of image, and two correction functions can be obtained by fitting two deviation surfaces; finally, a measurement method for planar dimensions is proposed, in which accurate magnification factor of imaging system can be obtained by using the correction functions. The effectiveness of the method is demonstrated by applying the proposed method to the test of measuring shaft diameter. Experimental data prove that the accurate planar-dimensions measurements can be performed using the proposed method even if images are deformed by lens distortion.

  20. Application of two machine learning algorithms to genetic association studies in the presence of covariates

    Directory of Open Access Journals (Sweden)

    Foulkes Andrea S

    2008-11-01

    Full Text Available Abstract Background Population-based investigations aimed at uncovering genotype-trait associations often involve high-dimensional genetic polymorphism data as well as information on multiple environmental and clinical parameters. Machine learning (ML algorithms offer a straightforward analytic approach for selecting subsets of these inputs that are most predictive of a pre-defined trait. The performance of these algorithms, however, in the presence of covariates is not well characterized. Methods and Results In this manuscript, we investigate two approaches: Random Forests (RFs and Multivariate Adaptive Regression Splines (MARS. Through multiple simulation studies, the performance under several underlying models is evaluated. An application to a cohort of HIV-1 infected individuals receiving anti-retroviral therapies is also provided. Conclusion Consistent with more traditional regression modeling theory, our findings highlight the importance of considering the nature of underlying gene-covariate-trait relationships before applying ML algorithms, particularly when there is potential confounding or effect mediation.

  1. Application of two machine learning algorithms to genetic association studies in the presence of covariates.

    Science.gov (United States)

    Nonyane, Bareng A S; Foulkes, Andrea S

    2008-11-14

    Population-based investigations aimed at uncovering genotype-trait associations often involve high-dimensional genetic polymorphism data as well as information on multiple environmental and clinical parameters. Machine learning (ML) algorithms offer a straightforward analytic approach for selecting subsets of these inputs that are most predictive of a pre-defined trait. The performance of these algorithms, however, in the presence of covariates is not well characterized. In this manuscript, we investigate two approaches: Random Forests (RFs) and Multivariate Adaptive Regression Splines (MARS). Through multiple simulation studies, the performance under several underlying models is evaluated. An application to a cohort of HIV-1 infected individuals receiving anti-retroviral therapies is also provided. Consistent with more traditional regression modeling theory, our findings highlight the importance of considering the nature of underlying gene-covariate-trait relationships before applying ML algorithms, particularly when there is potential confounding or effect mediation.

  2. Research of Real-time Grabbing Yarn Tube System Based on Machine Vision

    Directory of Open Access Journals (Sweden)

    Cui Shigang

    2015-01-01

    Full Text Available The current yarn tube manipulator just finishes yarn tube grabbing work according to the fixed coordinates. In the actual production process, equipment problems or human factors which make the spindles not on fixed coordinates cause the damage of the manipulator. Real-time grabbing yarn tube system with visual sensing has been designed and a extraction algorithm of spindles coordinates based on a mixed image morphology and Hough transform algorithm has been proposed. Through the combination of the yarn tube image characteristics which are extracted by the algorithm and the visual measurement model which is established by pinhole imaging principle, the mapping relation of yarn tube image coordinates and world coordinates has been gained to get the location information of yarn tube in real time. Results show that the proposed method could make the robot complete the grabbing job precisely and efficiently, under which the system meet the requirement of spinning and dyeing production line.

  3. A hybrid multi-objective evolutionary algorithm approach for handling sequence- and machine-dependent set-up times in unrelated parallel machine scheduling problem

    Indian Academy of Sciences (India)

    V K MANUPATI; G RAJYALAKSHMI; FELIX T S CHAN; J J THAKKAR

    2017-03-01

    This paper addresses a fuzzy mixed-integer non-linear programming (FMINLP) model by considering machine-dependent and job-sequence-dependent set-up times that minimize the total completion time,the number of tardy jobs, the total flow time and the machine load variation in the context of unrelated parallel machine scheduling (UPMS) problem. The above-mentioned multi-objectives were considered based on nonzero ready times, machine- and sequence-dependent set-up times and secondary resource constraints for jobs.The proposed approach considers unrelated parallel machines with inherent uncertainty in processing times and due dates. Since the problem is shown to be NP-hard in nature, it is a challenging task to find the optimal/nearoptimal solutions for conflicting objectives simultaneously in a reasonable time. Therefore, we introduced a new multi-objective-based evolutionary artificial immune non-dominated sorting genetic algorithm (AI-NSGA-II) to resolve the above-mentioned complex problem. The performance of the proposed multi-objective AI-NSGA-II algorithm has been compared to that of multi-objective particle swarm optimization (MOPSO) and conventionalnon-dominated sorting genetic algorithm (CNSGA-II), and it is found that the proposed multi-objective-based hybrid meta-heuristic produces high-quality solutions. Finally, the results obtained from benchmark instances and randomly generated instances as test problems evince the robust performance of the proposed multiobjective algorithm.

  4. Monthly prediction of air temperature in Australia and New Zealand with machine learning algorithms

    Science.gov (United States)

    Salcedo-Sanz, S.; Deo, R. C.; Carro-Calvo, L.; Saavedra-Moreno, B.

    2016-07-01

    Long-term air temperature prediction is of major importance in a large number of applications, including climate-related studies, energy, agricultural, or medical. This paper examines the performance of two Machine Learning algorithms (Support Vector Regression (SVR) and Multi-layer Perceptron (MLP)) in a problem of monthly mean air temperature prediction, from the previous measured values in observational stations of Australia and New Zealand, and climate indices of importance in the region. The performance of the two considered algorithms is discussed in the paper and compared to alternative approaches. The results indicate that the SVR algorithm is able to obtain the best prediction performance among all the algorithms compared in the paper. Moreover, the results obtained have shown that the mean absolute error made by the two algorithms considered is significantly larger for the last 20 years than in the previous decades, in what can be interpreted as a change in the relationship among the prediction variables involved in the training of the algorithms.

  5. Optimization process planning using hybrid genetic algorithm and intelligent search for job shop machining.

    Science.gov (United States)

    Salehi, Mojtaba; Bahreininejad, Ardeshir

    2011-08-01

    Optimization of process planning is considered as the key technology for computer-aided process planning which is a rather complex and difficult procedure. A good process plan of a part is built up based on two elements: (1) the optimized sequence of the operations of the part; and (2) the optimized selection of the machine, cutting tool and Tool Access Direction (TAD) for each operation. In the present work, the process planning is divided into preliminary planning, and secondary/detailed planning. In the preliminary stage, based on the analysis of order and clustering constraints as a compulsive constraint aggregation in operation sequencing and using an intelligent searching strategy, the feasible sequences are generated. Then, in the detailed planning stage, using the genetic algorithm which prunes the initial feasible sequences, the optimized operation sequence and the optimized selection of the machine, cutting tool and TAD for each operation based on optimization constraints as an additive constraint aggregation are obtained. The main contribution of this work is the optimization of sequence of the operations of the part, and optimization of machine selection, cutting tool and TAD for each operation using the intelligent search and genetic algorithm simultaneously.

  6. Classification and authentication of unknown water samples using machine learning algorithms.

    Science.gov (United States)

    Kundu, Palash K; Panchariya, P C; Kundu, Madhusree

    2011-07-01

    This paper proposes the development of water sample classification and authentication, in real life which is based on machine learning algorithms. The proposed techniques used experimental measurements from a pulse voltametry method which is based on an electronic tongue (E-tongue) instrumentation system with silver and platinum electrodes. E-tongue include arrays of solid state ion sensors, transducers even of different types, data collectors and data analysis tools, all oriented to the classification of liquid samples and authentication of unknown liquid samples. The time series signal and the corresponding raw data represent the measurement from a multi-sensor system. The E-tongue system, implemented in a laboratory environment for 6 numbers of different ISI (Bureau of Indian standard) certified water samples (Aquafina, Bisleri, Kingfisher, Oasis, Dolphin, and McDowell) was the data source for developing two types of machine learning algorithms like classification and regression. A water data set consisting of 6 numbers of sample classes containing 4402 numbers of features were considered. A PCA (principal component analysis) based classification and authentication tool was developed in this study as the machine learning component of the E-tongue system. A proposed partial least squares (PLS) based classifier, which was dedicated as well; to authenticate a specific category of water sample evolved out as an integral part of the E-tongue instrumentation system. The developed PCA and PLS based E-tongue system emancipated an overall encouraging authentication percentage accuracy with their excellent performances for the aforesaid categories of water samples.

  7. Evaluating data distribution and drift vulnerabilities of machine learning algorithms in secure and adversarial environments

    Science.gov (United States)

    Nelson, Kevin; Corbin, George; Blowers, Misty

    2014-05-01

    Machine learning is continuing to gain popularity due to its ability to solve problems that are difficult to model using conventional computer programming logic. Much of the current and past work has focused on algorithm development, data processing, and optimization. Lately, a subset of research has emerged which explores issues related to security. This research is gaining traction as systems employing these methods are being applied to both secure and adversarial environments. One of machine learning's biggest benefits, its data-driven versus logic-driven approach, is also a weakness if the data on which the models rely are corrupted. Adversaries could maliciously influence systems which address drift and data distribution changes using re-training and online learning. Our work is focused on exploring the resilience of various machine learning algorithms to these data-driven attacks. In this paper, we present our initial findings using Monte Carlo simulations, and statistical analysis, to explore the maximal achievable shift to a classification model, as well as the required amount of control over the data.

  8. Implementation of algorithms based on support vector machine (SVM for electric systems: topic review

    Directory of Open Access Journals (Sweden)

    Jefferson Jara Estupiñan

    2016-06-01

    Full Text Available Objective: To perform a review of implementation of algorithms based on support vectore machine applied to electric systems. Method: A paper search is done mainly on Biblio­graphic Indexes (BI and Bibliographic Bases with Selection Committee (BBSC about support vector machine. This work shows a qualitative and/or quan­titative description about advances and applications in the electrical environment, approaching topics such as: electrical market prediction, demand predic­tion, non-technical losses (theft, alternative energy source and transformers, among others, in each work the respective citation is done in order to guarantee the copy right and allow to the reader a dynamic mo­vement between the reading and the cited works. Results: A detailed review is done, focused on the searching of implemented algorithms in electric sys­tems and innovating application areas. Conclusion: Support vector machines have a lot of applications due to their multiple benefits, however in the electric energy area; they have not been tota­lly applied, this allow to identify a promising area of researching.

  9. Theory research of seam recognition and welding torch pose control based on machine vision

    Science.gov (United States)

    Long, Qiang; Zhai, Peng; Liu, Miao; He, Kai; Wang, Chunyang

    2017-03-01

    At present, the automation requirement of the welding become higher, so a method of the welding information extraction by vision sensor is proposed in this paper, and the simulation with the MATLAB has been conducted. Besides, in order to improve the quality of robot automatic welding, an information retrieval method for welding torch pose control by visual sensor is attempted. Considering the demands of welding technology and engineering habits, the relative coordinate systems and variables are strictly defined, and established the mathematical model of the welding pose, and verified its feasibility by using the MATLAB simulation in the paper, these works lay a foundation for the development of welding off-line programming system with high precision and quality.

  10. Minimisation of total tardiness for identical parallel machine scheduling using genetic algorithm

    Indian Academy of Sciences (India)

    IMRAN ALI CHAUDHRY; ISAM A Q ELBADAWI

    2017-01-01

    In recent years research on parallel machine scheduling has received an increased attention. This paper considers minimisation of total tardiness for scheduling of n jobs on a set of m parallel machines. A spread-sheet-based genetic algorithm (GA) approach is proposed for the problem. The proposed approach is a domain-independent general purpose approach, which has been effectively used to solve this class of problem.The performance of GA is compared with branch and bound and particle swarm optimisation approaches. Two set of problems having 20 and 25 jobs with number of parallel machines equal to 2, 4, 6, 8 and 10 are solved with the proposed approach. Each combination of number of jobs and machines consists of 125 benchmark problems; thus a total for 2250 problems are solved. The results obtained by the proposed approach are comparable with two earlier approaches. It is also demonstrated that a simple GA can be used to produce results that are comparable with problem-specific approach. The proposed approach can also be used to optimise any objectivefunction without changing the basic GA routine.

  11. Research of Vision Detection System on PCB

    Institute of Scientific and Technical Information of China (English)

    CHENG Songlin; ZHOU Zude; HU Wenjuan

    2006-01-01

    Machine vision is applied in defect detection system on PCB. The whole system structure and the principle of vision detection are introduced, while the detection method including image processing, detection and recognition algorithms are detailed. The simulation results demonstrate that through this method, four types of defects including short circuit, open circuit, protuberance and concavity on PCB circuit can be effectively inspected, located and recognized.

  12. Implementation of Computer Vision Based Industrial Fire Safety Automation by Using Neuro-Fuzzy Algorithms

    Directory of Open Access Journals (Sweden)

    Manjunatha K.C.

    2015-03-01

    Full Text Available A computer vision-based automated fire detection and suppression system for manufacturing industries is presented in this paper. Automated fire suppression system plays a very significant role in Onsite Emergency System (OES as it can prevent accidents and losses to the industry. A rule based generic collective model for fire pixel classification is proposed for a single camera with multiple fire suppression chemical control valves. Neuro-Fuzzy algorithm is used to identify the exact location of fire pixels in the image frame. Again the fuzzy logic is proposed to identify the valve to be controlled based on the area of the fire and intensity values of the fire pixels. The fuzzy output is given to supervisory control and data acquisition (SCADA system to generate suitable analog values for the control valve operation based on fire characteristics. Results with both fire identification and suppression systems have been presented. The proposed method achieves up to 99% of accuracy in fire detection and automated suppression.

  13. The implementation of contour-based object orientation estimation algorithm in FPGA-based on-board vision system

    Science.gov (United States)

    Alpatov, Boris; Babayan, Pavel; Ershov, Maksim; Strotov, Valery

    2016-10-01

    This paper describes the implementation of the orientation estimation algorithm in FPGA-based vision system. An approach to estimate an orientation of objects lacking axial symmetry is proposed. Suggested algorithm is intended to estimate orientation of a specific known 3D object based on object 3D model. The proposed orientation estimation algorithm consists of two stages: learning and estimation. Learning stage is devoted to the exploring of studied object. Using 3D model we can gather set of training images by capturing 3D model from viewpoints evenly distributed on a sphere. Sphere points distribution is made by the geosphere principle. Gathered training image set is used for calculating descriptors, which will be used in the estimation stage of the algorithm. The estimation stage is focusing on matching process between an observed image descriptor and the training image descriptors. The experimental research was performed using a set of images of Airbus A380. The proposed orientation estimation algorithm showed good accuracy in all case studies. The real-time performance of the algorithm in FPGA-based vision system was demonstrated.

  14. Fault diagnosis based on support vector machines with parameter optimisation by artificial immunisation algorithm

    Science.gov (United States)

    Yuan, Shengfa; Chu, Fulei

    2007-04-01

    Support vector machines (SVM) is a new general machine-learning tool based on the structural risk minimisation principle that exhibits good generalisation when fault samples are few, it is especially fit for classification, forecasting and estimation in small-sample cases such as fault diagnosis, but some parameters in SVM are selected by man's experience, this has hampered its efficiency in practical application. Artificial immunisation algorithm (AIA) is used to optimise the parameters in SVM in this paper. The AIA is a new optimisation method based on the biologic immune principle of human being and other living beings. It can effectively avoid the premature convergence and guarantees the variety of solution. With the parameters optimised by AIA, the total capability of the SVM classifier is improved. The fault diagnosis of turbo pump rotor shows that the SVM optimised by AIA can give higher recognition accuracy than the normal SVM.

  15. Nondegenerate piecewise linear systems: a finite Newton algorithm and applications in machine learning.

    Science.gov (United States)

    Yuan, Xiao-Tong; Yan, Shuicheng

    2012-04-01

    We investigate Newton-type optimization methods for solving piecewise linear systems (PLSs) with nondegenerate coefficient matrix. Such systems arise, for example, from the numerical solution of linear complementarity problem, which is useful to model several learning and optimization problems. In this letter, we propose an effective damped Newton method, PLS-DN, to find the exact (up to machine precision) solution of nondegenerate PLSs. PLS-DN exhibits provable semiiterative property, that is, the algorithm converges globally to the exact solution in a finite number of iterations. The rate of convergence is shown to be at least linear before termination. We emphasize the applications of our method in modeling, from a novel perspective of PLSs, some statistical learning problems such as box-constrained least squares, elitist Lasso (Kowalski & Torreesani, 2008), and support vector machines (Cortes & Vapnik, 1995). Numerical results on synthetic and benchmark data sets are presented to demonstrate the effectiveness and efficiency of PLS-DN on these problems.

  16. Classifying spatially heterogeneous wetland communities using machine learning algorithms and spectral and textural features.

    Science.gov (United States)

    Szantoi, Zoltan; Escobedo, Francisco J; Abd-Elrahman, Amr; Pearlstine, Leonard; Dewitt, Bon; Smith, Scot

    2015-05-01

    Mapping of wetlands (marsh vs. swamp vs. upland) is a common remote sensing application.Yet, discriminating between similar freshwater communities such as graminoid/sedge fromremotely sensed imagery is more difficult. Most of this activity has been performed using medium to low resolution imagery. There are only a few studies using highspatial resolutionimagery and machine learning image classification algorithms for mapping heterogeneouswetland plantcommunities. This study addresses this void by analyzing whether machine learning classifierssuch as decisiontrees (DT) and artificial neural networks (ANN) can accurately classify graminoid/sedgecommunities usinghigh resolution aerial imagery and image texture data in the Everglades National Park, Florida.In addition tospectral bands, the normalized difference vegetation index, and first- and second-order texturefeatures derivedfrom the near-infrared band were analyzed. Classifier accuracies were assessed using confusiontablesand the calculated kappa coefficients of the resulting maps. The results indicated that an ANN(multilayerperceptron based on backpropagation) algorithm produced a statistically significantly higheraccuracy(82.04%) than the DT (QUEST) algorithm (80.48%) or the maximum likelihood (80.56%)classifier (α<0.05). Findings show that using multiple window sizes provided the best results. First-ordertexture featuresalso provided computational advantages and results that were not significantly different fromthose usingsecond-order texture features.

  17. Prediction of microRNA-regulated protein interaction pathways in Arabidopsis using machine learning algorithms.

    Science.gov (United States)

    Kurubanjerdjit, Nilubon; Huang, Chien-Hung; Lee, Yu-Liang; Tsai, Jeffrey J P; Ng, Ka-Lok

    2013-11-01

    MicroRNAs are small, endogenous RNAs found in many different species and are known to have an influence on diverse biological phenomena. They also play crucial roles in plant biological processes, such as metabolism, leaf sidedness and flower development. However, the functional roles of most microRNAs are still unknown. The identification of closely related microRNAs and target genes can be an essential first step towards the discovery of their combinatorial effects on different cellular states. A lot of research has tried to discover microRNAs and target gene interactions by implementing machine learning classifiers with target prediction algorithms. However, high rates of false positives have been reported as a result of undetermined factors which will affect recognition. Therefore, integrating diverse techniques could improve the prediction. In this paper we propose identifying microRNAs target of Arabidopsis thaliana by integrating prediction scores from PITA, miRanda and RNAHybrid algorithms used as a feature vector of microRNA-target interactions, and then implementing SVM, random forest tree and neural network machine learning algorithms to make final predictions by majority voting. Furthermore, microRNA target genes are linked with their protein-protein interaction (PPI) partners. We focus on plant resistance genes and transcription factor information to provide new insights into plant pathogen interaction networks. Downstream pathways are characterized by the Jaccard coefficient, which is implemented based on Gene Ontology. The database is freely accessible at http://ppi.bioinfo.asia.edu.tw/At_miRNA/.

  18. Transformer fault diagnosis based on chemical reaction optimization algorithm and relevance vector machine

    Directory of Open Access Journals (Sweden)

    Luo Wei

    2017-01-01

    Full Text Available Power transformer is one of the most important equipment in power system. In order to predict the potential fault of power transformer and identify the fault types correctly, we proposed a transformer fault intelligent diagnosis model based on chemical reaction optimization (CRO algorithm and relevance vector machine(RVM. RVM is a powerful machine learning method, which can solve nonlinear, high-dimensional classification problems with a limited number of samples. CRO algorithm has well global optimization and simple calculation, so it is suitable to solve parameter optimization problems. In this paper, firstly, a multi-layer RVM classification model was built by binary tree recognition strategy. Secondly, CRO algorithm was adopted to optimize the kernel function parameters which could enhance the performance of RVM classifiers. Compared with IEC three-ratio method and the RVM model, the CRO-RVM model not only overcomes the coding defect problem of IEC three-ratio method, but also has higher classification accuracy than the RVM model. Finally, the new method was applied to analyze a transformer fault case, Its predicted result accord well with the real situation. The research provides a practical method for transformer fault intelligent diagnosis and prediction.

  19. An Improved Genetic Algorithm for Single-Machine Inverse Scheduling Problem

    Directory of Open Access Journals (Sweden)

    Jianhui Mou

    2014-01-01

    Full Text Available The goal of the scheduling is to arrange operations on suitable machines with optimal sequence for corresponding objectives. In order to meet market requirements, scheduling systems must own enough flexibility against uncertain events. These events can change production status or processing parameters, even causing the original schedule to no longer be optimal or even to be infeasible. Traditional scheduling strategies, however, cannot cope with these cases. Therefore, a new idea of scheduling called inverse scheduling has been proposed. In this paper, the inverse scheduling with weighted completion time (SMISP is considered in a single-machine shop environment. In this paper, an improved genetic algorithm (IGA with a local searching strategy is proposed. To improve the performance of IGA, efficient encoding scheme, fitness evaluation mechanism, feasible initialization methods, and a local search procedure have been employed in the paper. Because of the local improving method, the proposed IGA can balance its exploration ability and exploitation ability. We adopt 27 instances to verify the effectiveness of the proposed algorithm. The experimental results illustrated that the proposed algorithm can generate satisfactory solutions. This approach also has been applied to solve the scheduling problem in the real Chinese shipyard and can bring some benefits.

  20. 机器视觉在除草机器人中的应用%Application of Machine Vision in Weeding Robot

    Institute of Scientific and Technical Information of China (English)

    李谦; 蔡晓华

    2014-01-01

    随着农业自动化技术和农业机器人技术的发展,许多国家和企业开始致力于机器视觉除草机器人的试验与研究。为此,在介绍机器视觉技术系统的基础上,结合除草机器人苗草识别的试验,讨论了机器视觉系统在除草机器人中的应用,详细分析了基于机器视觉的苗草识别系统,并优化其系统的硬件、软件结构、功能以及原理等。%With the development of agricultural automation technology and robotics ,many countries and enterprises begin to dedicated to the experimental research of machine vision weeding robot .On the basis of machine vision technology sys-tem introduced in this paper ,combine with the Identification of the Blade of grass test ,th application of machine vision system in the weeding robot was discussed .and analyzed the recognition system of weeding grass based on machine vi-sion.And optimize the structure of hardware and software of the system .Function and principle , etc.

  1. A Novel Machine Vision System for the Inspection of Micro-Spray Nozzle

    Directory of Open Access Journals (Sweden)

    Kuo-Yi Huang

    2015-06-01

    Full Text Available In this study, we present an application of neural network and image processing techniques for detecting the defects of an internal micro-spray nozzle. The defect regions were segmented by Canny edge detection, a randomized algorithm for detecting circles and a circle inspection (CI algorithm. The gray level co-occurrence matrix (GLCM was further used to evaluate the texture features of the segmented region. These texture features (contrast, entropy, energy, color features (mean and variance of gray level and geometric features (distance variance, mean diameter and diameter ratio were used in the classification procedures. A back-propagation neural network classifier was employed to detect the defects of micro-spray nozzles. The methodology presented herein effectively works for detecting micro-spray nozzle defects to an accuracy of 90.71%.

  2. Feature Subset Selection for Hot Method Prediction using Genetic Algorithm wrapped with Support Vector Machines

    Directory of Open Access Journals (Sweden)

    S. Johnson

    2011-01-01

    Full Text Available Problem statement: All compilers have simple profiling-based heuristics to identify and predict program hot methods and also to make optimization decisions. The major challenge in the profile-based optimization is addressing the problem of overhead. The aim of this work is to perform feature subset selection using Genetic Algorithms (GA to improve and refine the machine learnt static hot method predictive technique and to compare the performance of the new models against the simple heuristics. Approach: The relevant features for training the predictive models are extracted from an initial set of randomly selected ninety static program features, with the help of the GA wrapped with the predictive model using the Support Vector Machine (SVM, a Machine Learning (ML algorithm. Results: The GA-generated feature subsets containing thirty and twenty nine features respectively for the two predictive models when tested on MiBench predict Long Running Hot Methods (LRHM and frequently called hot methods (FCHM with the respective accuracies of 71% and 80% achieving an increase of 19% and 22%. Further, inlining of the predicted LRHM and FCHM improve the program performance by 3% and 5% as against 4% and 6% with Low Level Virtual Machines (LLVM default heuristics. When intra-procedural optimizations (IPO are performed on the predicted hot methods, this system offers a performance improvement of 5% and 4% as against 0% and 3% by LLVM default heuristics on LRHM and FCHM respectively. However, we observe an improvement of 36% in certain individual programs. Conclusion: Overall, the results indicate that the GA wrapped with SVM derived feature reduction improves the hot method prediction accuracy and that the technique of hot method prediction based optimization is potentially useful in selective optimization.

  3. Application of machine vision in inspecting stem and shape of fruits

    Science.gov (United States)

    Ying, Yibin; Jing, Hansong; Tao, Yang; Jin, Juanqin; Ibarra, Juan G.; Chen, Zhikuan

    2000-12-01

    The shape and the condition of stem are important features in classification of Huanghua pears. As the commonly used thinning and erosion-dilation algorithm in judging the presence of the stem is too slow, a new fast algorithm was put forward. Compared with other part of the pear, the stem is obviously thin and long, with the help of various sized templates, the judgment of whether the stem is present was easily made, meanwhile the stem head and the intersection point of stem bottom and pear were labeled. Furthermore, after the slopes of the tangential line of stem head and tangential line of stem bottom were found, the included angle of these two lines was calculated. It was found that the included angle of the broken stem was obviously different from that of the good stem. After the analysis of 53 pictures of pears, the accuracy to judge whether the stem is present is 100% and whether the stem is good reaches 93%. Also, the algorithm is of robustness and can be made invariant to translation and rotation Meanwhile, the method to describe the shape of irregular fruits was studied. Fourier transformation and inverse Fourier transformation pair were adopted to describe the shape of Huanghua pears, and the algorithm for shape identification, which was based on artificial neural network, was developed. The first sixteen harmonic components of the Fourier descriptor were enough to represent the primary shape of pear, and the identification accuracy could reach 90% by applying the Fourier descriptor in combination with artificial neural network.

  4. Automatic Recognition Method for Optical Measuring Instruments Based on Machine Vision

    Institute of Scientific and Technical Information of China (English)

    SONG Le; LIN Yuchi; HAO Liguo

    2008-01-01

    Based on a comprehensive study of various algorithms, the automatic recognition of traditional ocular optical measuring instruments is realized. Taking a universal tools microscope (UTM) lens view image as an example, a 2-layer automatic recognition model for data reading is established after adopting a series of pre-processing algorithms. This model is an optimal combination of the correlation-based template matching method and a concurrent back propagation (BP) neural network. Multiple complementary feature extraction is used in generating the eigenvectors of the concurrent network. In order to improve fault-tolerance capacity, rotation invariant features based on Zernike moments are extracted from digit characters and a 4-dimensional group of the outline features is also obtained. Moreover, the operating time and reading accuracy can be adjusted dynamically by setting the threshold value. The experimental result indicates that the newly developed algorithm has optimal recognition precision and working speed. The average reading ratio can achieve 97.23%. The recognition method can automatically obtain the results of optical measuring instruments rapidly and stably without modifying their original structure, which meets the application requirements.

  5. Simultaneous Scheduling of Jobs, AGVs and Tools Considering Tool Transfer Times in Multi Machine FMS By SOS Algorithm

    Science.gov (United States)

    Sivarami Reddy, N.; Ramamurthy, D. V., Dr.; Prahlada Rao, K., Dr.

    2017-08-01

    This article addresses simultaneous scheduling of machines, AGVs and tools where machines are allowed to share the tools considering transfer times of jobs and tools between machines, to generate best optimal sequences that minimize makespan in a multi-machine Flexible Manufacturing System (FMS). Performance of FMS is expected to improve by effective utilization of its resources, by proper integration and synchronization of their scheduling. Symbiotic Organisms Search (SOS) algorithm is a potent tool which is a better alternative for solving optimization problems like scheduling and proven itself. The proposed SOS algorithm is tested on 22 job sets with makespan as objective for scheduling of machines and tools where machines are allowed to share tools without considering transfer times of jobs and tools and the results are compared with the results of existing methods. The results show that the SOS has outperformed. The same SOS algorithm is used for simultaneous scheduling of machines, AGVs and tools where machines are allowed to share tools considering transfer times of jobs and tools to determine the best optimal sequences that minimize makespan.

  6. Volume Measurement Algorithm for Food Product with Irregular Shape using Computer Vision based on Monte Carlo Method

    Directory of Open Access Journals (Sweden)

    Joko Siswantoro

    2014-11-01

    Full Text Available Volume is one of important issues in the production and processing of food product. Traditionally, volume measurement can be performed using water displacement method based on Archimedes’ principle. Water displacement method is inaccurate and considered as destructive method. Computer vision offers an accurate and nondestructive method in measuring volume of food product. This paper proposes algorithm for volume measurement of irregular shape food product using computer vision based on Monte Carlo method. Five images of object were acquired from five different views and then processed to obtain the silhouettes of object. From the silhouettes of object, Monte Carlo method was performed to approximate the volume of object. The simulation result shows that the algorithm produced high accuracy and precision for volume measurement.

  7. New predictive control algorithms based on Least Squares Support Vector Machines

    Institute of Scientific and Technical Information of China (English)

    LIU Bin; SU Hong-ye; CHU Jian

    2005-01-01

    Used for industrial process with different degree of nonlinearity, the two predictive control algorithms presented in this paper are based on Least Squares Support Vector Machines (LS-SVM) model. For the weakly nonlinear system, the system model is built by using LS-SVM with linear kernel function, and then the obtained linear LS-SVM model is transformed into linear input-output relation of the controlled system. However, for the strongly nonlinear system, the off-line model of the controlled system is built by using LS-SVM with Radial Basis Function (RBF) kernel. The obtained nonlinear LS-SVM model is linearized at each sampling instant of system running, after which the on-line linear input-output model of the system is built. Based on the obtained linear input-output model, the Generalized Predictive Control (GPC) algorithm is employed to implement predictive control for the controlled plant in both algorithms. The simulation results after the presented algorithms were implemented in two different industrial processes model; respectively revealed the effectiveness and merit of both algorithms.

  8. HandSight: Supporting Everyday Activities through Touch-Vision

    Science.gov (United States)

    2015-10-01

    vision algorithms to support inference and recognition, and a smartwatch for processing, power, and speech output. We have two high-level goals: first...integrated into one or more fingers, computer vision and machine learning algorithms to support fingertip-based sensing, and a smartwatch for processing...HandSight includes a smartwatch for processing and power, we wanted to explore how the wristband itself could be used to provide useful haptic feedback to

  9. On the Use of Machine Vision Techniques to Detect Human Settlements in Satellite Images

    Energy Technology Data Exchange (ETDEWEB)

    Kamath, C; Sengupta, S K; Poland, D; Futterman, J A H

    2003-01-10

    The automated production of maps of human settlement from recent satellite images is essential to studies of urbanization, population movement, and the like. The spectral and spatial resolution of such imagery is often high enough to successfully apply computer vision techniques. However, vast amounts of data have to be processed quickly. In this paper, we propose an approach that processes the data in several different stages. At each stage, using features appropriate to that stage, we identify the portion of the data likely to contain information relevant to the identification of human settlements. This data is used as input to the next stage of processing. Since the size of the data has reduced, we can now use more complex features in this next stage. These features can be more representative of human settlements, and also more time consuming to extract from the image data. Such a hierarchical approach enables us to process large amounts of data in a reasonable time, while maintaining the accuracy of human settlement identification. We illustrate our multi-stage approach using IKONOS 4-band and panchromatic images, and compare it with the straight-forward processing of the entire image.

  10. Identification of handwriting by using the genetic algorithm (GA) and support vector machine (SVM)

    Science.gov (United States)

    Zhang, Qigui; Deng, Kai

    2016-12-01

    As portable digital camera and a camera phone comes more and more popular, and equally pressing is meeting the requirements of people to shoot at any time, to identify and storage handwritten character. In this paper, genetic algorithm(GA) and support vector machine(SVM)are used for identification of handwriting. Compare with parameters-optimized method, this technique overcomes two defects: first, it's easy to trap in the local optimum; second, finding the best parameters in the larger range will affects the efficiency of classification and prediction. As the experimental results suggest, GA-SVM has a higher recognition rate.

  11. Application of support vector machine and quantum genetic algorithm in infrared target recognition

    Science.gov (United States)

    Wang, Hongliang; Huang, Yangwen; Ding, Haifei

    2010-08-01

    In this paper, a kind of classifier based on support vector machine (SVM) is designed for infrared target recognition. In allusion to the problem how to choose kernel parameter and error penalty factor, quantum genetic algorithm (QGA) is used to optimize the parameters of SVM model, it overcomes the shortcoming of determining its parameters after trial and error in the past. Classification experiments of infrared target features extracted by this method show that the convergence speed is fast and the rate of accurate recognition is high.

  12. PMSVM: An Optimized Support Vector Machine Classification Algorithm Based on PCA and Multilevel Grid Search Methods

    Directory of Open Access Journals (Sweden)

    Yukai Yao

    2015-01-01

    Full Text Available We propose an optimized Support Vector Machine classifier, named PMSVM, in which System Normalization, PCA, and Multilevel Grid Search methods are comprehensively considered for data preprocessing and parameters optimization, respectively. The main goals of this study are to improve the classification efficiency and accuracy of SVM. Sensitivity, Specificity, Precision, and ROC curve, and so forth, are adopted to appraise the performances of PMSVM. Experimental results show that PMSVM has relatively better accuracy and remarkable higher efficiency compared with traditional SVM algorithms.

  13. Execution time supports for adaptive scientific algorithms on distributed memory machines

    Science.gov (United States)

    Berryman, Harry; Saltz, Joel; Scroggs, Jeffrey

    1990-01-01

    Optimizations are considered that are required for efficient execution of code segments that consists of loops over distributed data structures. The PARTI (Parallel Automated Runtime Toolkit at ICASE) execution time primitives are designed to carry out these optimizations and can be used to implement a wide range of scientific algorithms on distributed memory machines. These primitives allow the user to control array mappings in a way that gives an appearance of shared memory. Computations can be based on a global index set. Primitives are used to carry out gather and scatter operations on distributed arrays. Communications patterns are derived at runtime, and the appropriate send and receive messages are automatically generated.

  14. Machine Learning Algorithms for $b$-Jet Tagging at the ATLAS Experiment

    CERN Document Server

    Paganini, Michela; The ATLAS collaboration

    2017-01-01

    The separation of b-quark initiated jets from those coming from lighter quark flavours (b-tagging) is a fundamental tool for the ATLAS physics program at the CERN Large Hadron Collider. The most powerful b-tagging algorithms combine information from low-level taggers exploiting reconstructed track and vertex information using a multivariate classifier. The potential of modern Machine Learning techniques such as Recurrent Neural Networks and Deep Learning is explored using simulated events, and compared to that achievable from more traditional classifiers such as boosted decision trees.

  15. Application of Cerebellar Model Articulation Controller(CMAC) with a Modified Algorithm in Monitoring Machine Performance Degradation

    Institute of Scientific and Technical Information of China (English)

    ZHANG Jing-yong; ZHANG Lei; CAO Qi-xin; Jay Lee

    2005-01-01

    On the basis of CMAC-PDM (Pattern Discrimination Model), a novel algorithm of CMAC for monitoring machine degradation is proposed in this paper. The output of CMAC with the novel algorithm represents the state of a machine and PDM is not needed. The principle was explained by analyzing the modified mapping process of CMAC. The novel CMAC is applied to a tool condition monitoring system and two methodologies (novel CMAC and CMAC-PDM) are compared. The results prove that the novel algorithm is feasible and its computational complexity is reduced significantly.

  16. Heuristic and Exact Algorithms for the Two-Machine Just in Time Job Shop Scheduling Problem

    Directory of Open Access Journals (Sweden)

    Mohammed Al-Salem

    2016-01-01

    Full Text Available The problem addressed in this paper is the two-machine job shop scheduling problem when the objective is to minimize the total earliness and tardiness from a common due date (CDD for a set of jobs when their weights equal 1 (unweighted problem. This objective became very significant after the introduction of the Just in Time manufacturing approach. A procedure to determine whether the CDD is restricted or unrestricted is developed and a semirestricted CDD is defined. Algorithms are introduced to find the optimal solution when the CDD is unrestricted and semirestricted. When the CDD is restricted, which is a much harder problem, a heuristic algorithm is proposed to find approximate solutions. Through computational experiments, the heuristic algorithms’ performance is evaluated with problems up to 500 jobs.

  17. Comparison of Multiobjective Evolutionary Algorithms for Operations Scheduling under Machine Availability Constraints

    Directory of Open Access Journals (Sweden)

    M. Frutos

    2013-01-01

    Full Text Available Many of the problems that arise in production systems can be handled with multiobjective techniques. One of those problems is that of scheduling operations subject to constraints on the availability of machines and buffer capacity. In this paper we analyze different Evolutionary multiobjective Algorithms (MOEAs for this kind of problems. We consider an experimental framework in which we schedule production operations for four real world Job-Shop contexts using three algorithms, NSGAII, SPEA2, and IBEA. Using two performance indexes, Hypervolume and R2, we found that SPEA2 and IBEA are the most efficient for the tasks at hand. On the other hand IBEA seems to be a better choice of tool since it yields more solutions in the approximate Pareto frontier.

  18. Botnet Detection Using Support Vector Machines with Artificial Fish Swarm Algorithm

    Directory of Open Access Journals (Sweden)

    Kuan-Cheng Lin

    2014-01-01

    Full Text Available Because of the advances in Internet technology, the applications of the Internet of Things have become a crucial topic. The number of mobile devices used globally substantially increases daily; therefore, information security concerns are increasingly vital. The botnet virus is a major threat to both personal computers and mobile devices; therefore, a method of botnet feature characterization is proposed in this study. The proposed method is a classified model in which an artificial fish swarm algorithm and a support vector machine are combined. A LAN environment with several computers which has infected by the botnet virus was simulated for testing this model; the packet data of network flow was also collected. The proposed method was used to identify the critical features that determine the pattern of botnet. The experimental results indicated that the method can be used for identifying the essential botnet features and that the performance of the proposed method was superior to that of genetic algorithms.

  19. Optimization of Filter by using Support Vector Regression Machine with Cuckoo Search Algorithm

    Directory of Open Access Journals (Sweden)

    M. İlarslan

    2014-09-01

    Full Text Available Herein, a new methodology using a 3D Electromagnetic (EM simulator-based Support Vector Regression Machine (SVRM models of base elements is presented for band-pass filter (BPF design. SVRM models of elements, which are as fast as analytical equations and as accurate as a 3D EM simulator, are employed in a simple and efficient Cuckoo Search Algorithm (CSA to optimize an ultra-wideband (UWB microstrip BPF. CSA performance is verified by comparing it with other Meta-Heuristics such as Genetic Algorithm (GA and Particle Swarm Optimization (PSO. As an example of the proposed design methodology, an UWB BPF that operates between the frequencies of 3.1 GHz and 10.6 GHz is designed, fabricated and measured. The simulation and measurement results indicate in conclusion the superior performance of this optimization methodology in terms of improved filter response characteristics like return loss, insertion loss, harmonic suppression and group delay.

  20. Computer vision and machine learning for robust phenotyping in genome-wide studies

    Science.gov (United States)

    Zhang, Jiaoping; Naik, Hsiang Sing; Assefa, Teshale; Sarkar, Soumik; Reddy, R. V. Chowda; Singh, Arti; Ganapathysubramanian, Baskar; Singh, Asheesh K.

    2017-01-01

    Traditional evaluation of crop biotic and abiotic stresses are time-consuming and labor-intensive limiting the ability to dissect the genetic basis of quantitative traits. A machine learning (ML)-enabled image-phenotyping pipeline for the genetic studies of abiotic stress iron deficiency chlorosis (IDC) of soybean is reported. IDC classification and severity for an association panel of 461 diverse plant-introduction accessions was evaluated using an end-to-end phenotyping workflow. The workflow consisted of a multi-stage procedure including: (1) optimized protocols for consistent image capture across plant canopies, (2) canopy identification and registration from cluttered backgrounds, (3) extraction of domain expert informed features from the processed images to accurately represent IDC expression, and (4) supervised ML-based classifiers that linked the automatically extracted features with expert-rating equivalent IDC scores. ML-generated phenotypic data were subsequently utilized for the genome-wide association study and genomic prediction. The results illustrate the reliability and advantage of ML-enabled image-phenotyping pipeline by identifying previously reported locus and a novel locus harboring a gene homolog involved in iron acquisition. This study demonstrates a promising path for integrating the phenotyping pipeline into genomic prediction, and provides a systematic framework enabling robust and quicker phenotyping through ground-based systems. PMID:28272456

  1. Computer vision and machine learning for robust phenotyping in genome-wide studies.

    Science.gov (United States)

    Zhang, Jiaoping; Naik, Hsiang Sing; Assefa, Teshale; Sarkar, Soumik; Reddy, R V Chowda; Singh, Arti; Ganapathysubramanian, Baskar; Singh, Asheesh K

    2017-03-08

    Traditional evaluation of crop biotic and abiotic stresses are time-consuming and labor-intensive limiting the ability to dissect the genetic basis of quantitative traits. A machine learning (ML)-enabled image-phenotyping pipeline for the genetic studies of abiotic stress iron deficiency chlorosis (IDC) of soybean is reported. IDC classification and severity for an association panel of 461 diverse plant-introduction accessions was evaluated using an end-to-end phenotyping workflow. The workflow consisted of a multi-stage procedure including: (1) optimized protocols for consistent image capture across plant canopies, (2) canopy identification and registration from cluttered backgrounds, (3) extraction of domain expert informed features from the processed images to accurately represent IDC expression, and (4) supervised ML-based classifiers that linked the automatically extracted features with expert-rating equivalent IDC scores. ML-generated phenotypic data were subsequently utilized for the genome-wide association study and genomic prediction. The results illustrate the reliability and advantage of ML-enabled image-phenotyping pipeline by identifying previously reported locus and a novel locus harboring a gene homolog involved in iron acquisition. This study demonstrates a promising path for integrating the phenotyping pipeline into genomic prediction, and provides a systematic framework enabling robust and quicker phenotyping through ground-based systems.

  2. Towards Sustainable Green Production: Exploring Automated Grading for Oil Palm Fresh Fruit Bunches (FFB Using Machine Vision and Spectral Analysis

    Directory of Open Access Journals (Sweden)

    Muhammad Makky

    2013-01-01

    Full Text Available Over the last decade, Indonesian palm oil industry has become a leading producer of the world, and been able to generatenotable foreign export reserves. In spite of this, problems still persist in this industry, including low productivity due to mishandling of raw material in post-harvest operations. One of the prime causes of this is manual grading/sorting of fresh fruit bunches, which is prone to error and misjudgement, as well as subjectivity. High demand of oil palm establishes its high price in world market, which drives the industry to expand its plantation area to increase production. Ultimately, it compromise forests and agricultural land, resulting stagnation or decline in several food products. Alternatively, before expanding plantation extent, oil extraction productivity of existing plantation can be improved by carefully selecting appropriate FFBs for post-harvest processing through introduction of automation. The use of machine vision and spectral analysis has shown to assist productivity of agricultural processing industry. This study employs automation technology for FFB grading in oil palm mills, resulting in improved raw material quality, thereby increasing the oil extraction productivity, and simultaneously contributing to partly release the pressure of deforestation by maintaining green agricultural areas.

  3. DEVELOPMENT OF A PROXIMAL MACHINE VISION SYSTEM FOR OFF-SEASON WEED MAPPING IN BROADACRE NO-TILLAGE FALLOWS

    Directory of Open Access Journals (Sweden)

    H. Liu

    2013-01-01

    Full Text Available Weeds are among the most significant and costly environmental threats in Australian agriculture. Weeds compete with crop plants for moisture, nutrients and sunlight and can have a detrimental impact on crop yields and quality if uncontrolled. The distribution, size, density and species of the weeds are often heterogeneous in the cropping land. Instead of uniformly spray the same type of herbicide to the whole farm land, selective spray can reduce the herbicide usage therefore can reduce the serious problems of herbicide resistance, soil damage and food safety. This study describes a weed mapping method which could be used for broadacre no-tillage fallow weed management. The weed maps have the potential to be used as powerful herbicide prescription maps for spot spray. The weed mapping is realized by the machine vision technologies which including image acquisition, image stitching and photomosaic processing. The sampling points are continuous and the interpolation methods are used at the minimum levels. The experiment result shows that this weed mapping method can map weed under limited conditions.

  4. Synthetic Molecular Machines for Active Self-Assembly: Prototype Algorithms, Designs, and Experimental Study

    Science.gov (United States)

    Dabby, Nadine L.

    Computer science and electrical engineering have been the great success story of the twentieth century. The neat modularity and mapping of a language onto circuits has led to robots on Mars, desktop computers and smartphones. But these devices are not yet able to do some of the things that life takes for granted: repair a scratch, reproduce, regenerate, or grow exponentially fast--all while remaining functional. This thesis explores and develops algorithms, molecular implementations, and theoretical proofs in the context of "active self-assembly" of molecular systems. The long-term vision of active self-assembly is the theoretical and physical implementation of materials that are composed of reconfigurable units with the programmability and adaptability of biology's numerous molecular machines. En route to this goal, we must first find a way to overcome the memory limitations of molecular systems, and to discover the limits of complexity that can be achieved with individual molecules. One of the main thrusts in molecular programming is to use computer science as a tool for figuring out what can be achieved. While molecular systems that are Turing-complete have been demonstrated [Winfree, 1996], these systems still cannot achieve some of the feats biology has achieved. One might think that because a system is Turing-complete, capable of computing "anything," that it can do any arbitrary task. But while it can simulate any digital computational problem, there are many behaviors that are not "computations" in a classical sense, and cannot be directly implemented. Examples include exponential growth and molecular motion relative to a surface. Passive self-assembly systems cannot implement these behaviors because (a) molecular motion relative to a surface requires a source of fuel that is external to the system, and (b) passive systems are too slow to assemble exponentially-fast-growing structures. We call these behaviors "energetically incomplete" programmable

  5. 机器视觉电动缝纫机关键技术研究%A Study on Key Technologies of Electric Sewing Machine Based on Machine Vision

    Institute of Scientific and Technical Information of China (English)

    崔群; 白顺科

    2014-01-01

    This article introduces the machine vision electric sewing machine,which is an intelligent electric sewing machine based on machine vision and numerical control tool. This machine can recognize and localize all of the sewing pieces of cloth on working table through the image sensors,can grab the sewing pieces of cloth by folding arm manipulator and can complete the accurate and reliable feeding work. So,the form clamp would be removed,and the manual work could be reduced.%研究了一种基于机器视觉和数控技术的工业智能缝纫设备的关键技术。该设备通过图像传感器对工作台上摆放的缝片进行识别与定位,通过送料机械手实现缝片的抓取并实现精确、快速、可靠地缝片的送料,以抛弃目前电动缝纫机的模板夹具,并实现减少操作工干预。

  6. Michelson interferometer experiment based on machine vision%基于 LabVIEW机器视觉的迈克耳孙干涉仪实验

    Institute of Scientific and Technical Information of China (English)

    王建中; 黄林; 唐一文

    2014-01-01

    基于机器视觉设计了迈克耳孙干涉仪实验,采用机器视觉技术替代人眼对目标(干涉圆环)进行分析与测量,可有效避免因视觉疲劳而造成的测量错误。%Based on the machine vision ,the Michelson interferometer was put forward to consider-ably reducing the measurement errors due to visual fatigue in experimental analysis and measure-ments .The interference ring in Michelson interferometer was measured via the machine vision tech-nology ,without observing by naked eyes .

  7. A novel virtual machine deployment algorithm with energy efficiency in cloud computing

    Institute of Scientific and Technical Information of China (English)

    周舟; 胡志刚; 宋铁; 于俊洋

    2015-01-01

    In order to improve the energy efficiency of large-scale data centers, a virtual machine (VM) deployment algorithm called three-threshold energy saving algorithm (TESA), which is based on the linear relation between the energy consumption and (processor) resource utilization, is proposed. In TESA, according to load, hosts in data centers are divided into four classes, that is, host with light load, host with proper load, host with middle load and host with heavy load. By defining TESA, VMs on lightly loaded host or VMs on heavily loaded host are migrated to another host with proper load;VMs on properly loaded host or VMs on middling loaded host are kept constant. Then, based on the TESA, five kinds of VM selection policies (minimization of migrations policy based on TESA (MIMT), maximization of migrations policy based on TESA (MAMT), highest potential growth policy based on TESA (HPGT), lowest potential growth policy based on TESA (LPGT) and random choice policy based on TESA (RCT)) are presented, and MIMT is chosen as the representative policy through experimental comparison. Finally, five research directions are put forward on future energy management. The results of simulation indicate that, as compared with single threshold (ST) algorithm and minimization of migrations (MM) algorithm, MIMT significantly improves the energy efficiency in data centers.

  8. Classification of frontal cortex haemodynamic responses during cognitive tasks using wavelet transforms and machine learning algorithms.

    Science.gov (United States)

    Abibullaev, Berdakh; An, Jinung

    2012-12-01

    Recent advances in neuroimaging demonstrate the potential of functional near-infrared spectroscopy (fNIRS) for use in brain-computer interfaces (BCIs). fNIRS uses light in the near-infrared range to measure brain surface haemoglobin concentrations and thus determine human neural activity. Our primary goal in this study is to analyse brain haemodynamic responses for application in a BCI. Specifically, we develop an efficient signal processing algorithm to extract important mental-task-relevant neural features and obtain the best possible classification performance. We recorded brain haemodynamic responses due to frontal cortex brain activity from nine subjects using a 19-channel fNIRS system. Our algorithm is based on continuous wavelet transforms (CWTs) for multi-scale decomposition and a soft thresholding algorithm for de-noising. We adopted three machine learning algorithms and compared their performance. Good performance can be achieved by using the de-noised wavelet coefficients as input features for the classifier. Moreover, the classifier performance varied depending on the type of mother wavelet used for wavelet decomposition. Our quantitative results showed that CWTs can be used efficiently to extract important brain haemodynamic features at multiple frequencies if an appropriate mother wavelet function is chosen. The best classification results were obtained by a specific combination of input feature type and classifier.

  9. A distributed parallel genetic algorithm of placement strategy for virtual machines deployment on cloud platform.

    Science.gov (United States)

    Dong, Yu-Shuang; Xu, Gao-Chao; Fu, Xiao-Dong

    2014-01-01

    The cloud platform provides various services to users. More and more cloud centers provide infrastructure as the main way of operating. To improve the utilization rate of the cloud center and to decrease the operating cost, the cloud center provides services according to requirements of users by sharding the resources with virtualization. Considering both QoS for users and cost saving for cloud computing providers, we try to maximize performance and minimize energy cost as well. In this paper, we propose a distributed parallel genetic algorithm (DPGA) of placement strategy for virtual machines deployment on cloud platform. It executes the genetic algorithm parallelly and distributedly on several selected physical hosts in the first stage. Then it continues to execute the genetic algorithm of the second stage with solutions obtained from the first stage as the initial population. The solution calculated by the genetic algorithm of the second stage is the optimal one of the proposed approach. The experimental results show that the proposed placement strategy of VM deployment can ensure QoS for users and it is more effective and more energy efficient than other placement strategies on the cloud platform.

  10. PROSODIC FEATURE BASED TEXT DEPENDENT SPEAKER RECOGNITION USING MACHINE LEARNING ALGORITHMS

    Directory of Open Access Journals (Sweden)

    Sunil Agrawal

    2010-10-01

    Full Text Available Most of us are aware of the fact that voices of different individuals do not sound alike. The ability of recognizing a person solely from his voice is known as speaker recognition. Speaker recognition can not only assist in building better access control systems and security apparatus, it can be a useful tool in many other areas such as forensic speech analysis. The choice of features plays an important role in the performance of ML algorithm. Here we propose prosodic features based text dependent speaker recognition where the prosodic features can be extracted through linear predictive coding. Formants are efficient parameters to characterize a speaker’s voice. Formants are combined with their corresponding amplitudes, fundamental frequency, duration of speech utterance and energy ofthe windowed section. This feature vector is input to machine learning (ML algorithms for recognition. We investigate the performance of four ML algorithms namely MLP, RBFN, C4.5 decision tree, and BayesNet. Out of these ML algorithms, C4.5 decision tree performance is consistent. MLP performs better for gender recognition and experimental results show that RBFN gives better performance for increased population size.

  11. Land Use Classification using Support Vector Machine and Maximum Likelihood Algorithms by Landsat 5 TM Images

    Directory of Open Access Journals (Sweden)

    Abbas TAATI

    2015-08-01

    Full Text Available Nowadays, remote sensing images have been identified and exploited as the latest information to study land cover and land uses. These digital images are of significant importance, since they can present timely information, and capable of providing land use maps. The aim of this study is to create land use classification using a support vector machine (SVM and maximum likelihood classifier (MLC in Qazvin, Iran, by TM images of the Landsat 5 satellite. In the pre-processing stage, the necessary corrections were applied to the images. In order to evaluate the accuracy of the 2 algorithms, the overall accuracy and kappa coefficient were used. The evaluation results verified that the SVM algorithm with an overall accuracy of 86.67 % and a kappa coefficient of 0.82 has a higher accuracy than the MLC algorithm in land use mapping. Therefore, this algorithm has been suggested to be applied as an optimal classifier for extraction of land use maps due to its higher accuracy and better consistency within the study area.

  12. Likelihood Gradient Ascent (LGA): a closed-loop decoder adaptation algorithm for brain-machine interfaces.

    Science.gov (United States)

    Dangi, Siddharth; Gowda, Suraj; Carmena, Jose M

    2013-01-01

    Closed-loop decoder adaptation (CLDA) is an emerging paradigm for improving or maintaining the online performance of brain-machine interfaces (BMIs). Here, we present Likelihood Gradient Ascent (LGA), a novel CLDA algorithm for a Kalman filter (KF) decoder that uses stochastic, gradient-based corrections to update KF parameters during closed-loop BMI operation. LGA's gradient-based paradigm presents a variety of potential advantages over other "batch" CLDA methods, including the ability to update decoder parameters on any time-scale, even on every decoder iteration. Using a closed-loop BMI simulator, we compare the LGA algorithm to the Adaptive Kalman Filter (AKF), a partially gradient-based CLDA algorithm that has been previously tested in non-human primate experiments. In contrast to the AKF's separate mean-squared error objective functions, LGA's update rules are derived directly from a single log likelihood objective, making it one step towards a potentially optimal continuously adaptive CLDA algorithm for BMIs.

  13. OPTIMIZATION OF MACHINING PARAMETERS IN TURNING PROCESS USING GENETIC ALGORITHM AND PARTICLE SWARM OPTIMIZATION WITH EXPERIMENTAL VERIFICATION

    Directory of Open Access Journals (Sweden)

    K.RAMESH KUMAR

    2011-02-01

    Full Text Available Optimization of cutting parameters is one of the most important elements in any process planning of metal parts. Economy of machining operation plays a key role in competitiveness in the market. All CNCmachines produce finished components from cylindrical bar. Finished profiles consist of straight turning, facing, taper and circular machining. Finished profile from a cylindrical bar is done in two stages, rough machining and finish machining. Numbers of passes are required for rough machining and single pass is required for the finished pass. The machining parameters in multipass turning are depth of cut, cutting speed and feed. The machining performance is measured by the minimum production time. In this paper the optimal machining parameters for continuous profile machining are determinedwith respect to the minimum production time, subject to a set of practical constraints, cutting force, power and dimensional accuracy and surface finish. Due to complexity of this machining optimizationproblem, a genetic algorithm (GA and Particle Swarm Optimization (PSO are applied to resolve the problem and the results obtained from GA and PSO are compared.

  14. Automated Quality Assessment of Structural Magnetic Resonance Brain Images Based on a Supervised Machine Learning Algorithm

    Directory of Open Access Journals (Sweden)

    Ricardo Andres Pizarro

    2016-12-01

    Full Text Available High-resolution three-dimensional magnetic resonance imaging (3D-MRI is being increasingly used to delineate morphological changes underlying neuropsychiatric disorders. Unfortunately, artifacts frequently compromise the utility of 3D-MRI yielding irreproducible results, from both type I and type II errors. It is therefore critical to screen 3D-MRIs for artifacts before use. Currently, quality assessment involves slice-wise visual inspection of 3D-MRI volumes, a procedure that is both subjective and time consuming. Automating the quality rating of 3D-MRI could improve the efficiency and reproducibility of the procedure. The present study is one of the first efforts to apply a support vector machine (SVM algorithm in the quality assessment of structural brain images, using global and region of interest (ROI automated image quality features developed in-house. SVM is a supervised machine-learning algorithm that can predict the category of test datasets based on the knowledge acquired from a learning dataset. The performance (accuracy of the automated SVM approach was assessed, by comparing the SVM-predicted quality labels to investigator-determined quality labels. The accuracy for classifying 1457 3D-MRI volumes from our database using the SVM approach is around 80%. These results are promising and illustrate the possibility of using SVM as an automated quality assessment tool for 3D-MRI.

  15. Machine learning algorithms for predicting roadside fine particulate matter concentration level in Hong Kong Central

    Directory of Open Access Journals (Sweden)

    Yin Zhao

    2013-09-01

    Full Text Available Data mining is an approach to discover knowledge from large data. Pollutant forecasting is an important problem in the environmental sciences. This paper tries to use data mining methods to forecast fine particles (PM2.5 concentration level in Hong Kong Central, which is a famous business centre in Asia. There are several classification algorithms available in data mining, such as Artificial Neural Network (ANN and Support Vector Machine (SVM. ANN and SVM are both machine learning algorithm used in variant area. This paper builds PM2.5 concentration level predictive models based on ANN and SVM by using R packages. The data set includes 2008-2011 period meteorological data and PM2.5 data. The PM2.5 concentration is divided into 2 levels: low and high. The critical point is 40ug/cubic meter (24 hours mean, which is based on the standard of US Environmental Protection Agency (EPA. The parameters of both models are selected by multiple cross validation. According to 100 times 10-fold cross validation, the testing accuracy of SVM is around 0.803-0.820, which is much better than ANN whose accuracy is around 0.746-0.793.

  16. Concept of automatic programming of NC machine for metal plate cutting by genetic algorithm method

    Directory of Open Access Journals (Sweden)

    B. Vaupotic

    2005-12-01

    Full Text Available Purpose: In this paper the concept of automatic programs of the NC machine for metal plate cutting by genetic algorithm method has been presented.Design/methodology/approach: The paper was limited to automatic creation of NC programs for two-dimensional cutting of material by means of adaptive heuristic search algorithms.Findings: Automatic creation of NC programs in laser cutting of materials combines the CAD concepts, the recognition of features and creation and optimization of NC programs. The proposed intelligent system is capable to recognize automatically the nesting of products in the layout, to determine the incisions and sequences of cuts forming the laid out products. Position of incisions is determined at the relevant places on the cut. The system is capable to find the shortest path between individual cuts and to record the NC program.Research limitations/implications: It would be appropriate to orient future researches towards conceiving an improved system for three-dimensional cutting with optional determination of positions of incisions, with the capability to sense collisions and with optimization of the speed and acceleration during cutting.Practical implications: The proposed system assures automatic preparation of NC program without NC programer.Originality/value: The proposed concept shows a high degree of universality, efficiency and reliability and it can be simply adapted to other NC-machines.

  17. Application of supervised machine learning algorithms for the classification of regulatory RNA riboswitches.

    Science.gov (United States)

    Singh, Swadha; Singh, Raghvendra

    2016-04-03

    Riboswitches, the small structured RNA elements, were discovered about a decade ago. It has been the subject of intense interest to identify riboswitches, understand their mechanisms of action and use them in genetic engineering. The accumulation of genome and transcriptome sequence data and comparative genomics provide unprecedented opportunities to identify riboswitches in the genome. In the present study, we have evaluated the following six machine learning algorithms for their efficiency to classify riboswitches: J48, BayesNet, Naïve Bayes, Multilayer Perceptron, sequential minimal optimization, hidden Markov model (HMM). For determining effective classifier, the algorithms were compared on the statistical measures of specificity, sensitivity, accuracy, F-measure and receiver operating characteristic (ROC) plot analysis. The classifier Multilayer Perceptron achieved the best performance, with the highest specificity, sensitivity, F-score and accuracy, and with the largest area under the ROC curve, whereas HMM was the poorest performer. At present, the available tools for the prediction and classification of riboswitches are based on covariance model, support vector machine and HMM. The present study determines Multilayer Perceptron as a better classifier for the genome-wide riboswitch searches.

  18. Scheduling and sequencing in four machines robotic cell: Application of genetic algorithm and enumeration techniques

    Directory of Open Access Journals (Sweden)

    M.M.S. Abdulkader

    2013-09-01

    Full Text Available The introduction of robotic cells to manufacturing systems improved the efficiency, productivity and reliability of the system. The main objective of the scheduling problem of multi-item multi-machine robotic cells is the identification of the optimum robot cycle/s and jobs sequencing for certain processing conditions which yield the higher possible production rate. The objective of this work is to solve the scheduling problem in four-machine blocking robotic cells producing identical and different part types while minimizing the cycle time. A genetic algorithm is developed to find the parts sequence that minimizes the robot-moves cycle time for each robot cycle. The results showed that the developed genetic algorithm yields competitive results compared to the results of the full enumeration of all possible parts sequences. The results show also that the ratio between the average processing time of all parts and the robot travel time determines the cycle having the optimal robot-moves.

  19. Machine Learning Model of the Swift/BAT Trigger Algorithm for Long GRB Population Studies

    CERN Document Server

    Graff, Philip B; Baker, John G; Sakamoto, Takanori

    2015-01-01

    To draw inferences about gamma-ray burst (GRB) source populations based on Swift observations, it is essential to understand the detection efficiency of the Swift burst alert telescope (BAT). This study considers the problem of modeling the Swift/BAT triggering algorithm for long GRBs, a computationally expensive procedure, and models it using machine learning algorithms. A large sample of simulated GRBs from Lien 2014 is used to train various models: random forests, boosted decision trees (with AdaBoost), support vector machines, and artificial neural networks. The best models have accuracies of $\\gtrsim97\\%$ ($\\lesssim 3\\%$ error), which is a significant improvement on a cut in GRB flux which has an accuracy of $89.6\\%$ ($10.4\\%$ error). These models are then used to measure the detection efficiency of Swift as a function of redshift $z$, which is used to perform Bayesian parameter estimation on the GRB rate distribution. We find a local GRB rate density of $n_0 \\sim 0.48^{+0.41}_{-0.23} \\ {\\rm Gpc}^{-3} {\\...

  20. Study on Immune Relevant Vector Machine Based Intelligent Fault Detection and Diagnosis Algorithm

    Directory of Open Access Journals (Sweden)

    Zhong-hua Miao

    2013-01-01

    Full Text Available An immune relevant vector machine (IRVM based intelligent classification method is proposed by combining the random real-valued negative selection (RRNS algorithm and the relevant vector machine (RVM algorithm. The method proposed is aimed to handle the training problem of missing or incomplete fault sampling data and is inspired by the “self/nonself” recognition principle in the artificial immune systems. The detectors, generated by the RRNS, are treated as the “nonself” training samples and used to train the RVM model together with the “self” training samples. After the training succeeds, the “nonself” detection model, which requires only the “self” training samples, is obtained for the fault detection and diagnosis. It provides a general way solving the problems of this type and can be applied for both fault detection and fault diagnosis. The standard Fisher's Iris flower dataset is used to experimentally testify the proposed method, and the results are compared with those from the support vector data description (SVDD method. Experimental results have shown the validity and practicability of the proposed method.

  1. Machine learning algorithms for mode-of-action classification in toxicity assessment.

    Science.gov (United States)

    Zhang, Yile; Wong, Yau Shu; Deng, Jian; Anton, Cristina; Gabos, Stephan; Zhang, Weiping; Huang, Dorothy Yu; Jin, Can

    2016-01-01

    Real Time Cell Analysis (RTCA) technology is used to monitor cellular changes continuously over the entire exposure period. Combining with different testing concentrations, the profiles have potential in probing the mode of action (MOA) of the testing substances. In this paper, we present machine learning approaches for MOA assessment. Computational tools based on artificial neural network (ANN) and support vector machine (SVM) are developed to analyze the time-concentration response curves (TCRCs) of human cell lines responding to tested chemicals. The techniques are capable of learning data from given TCRCs with known MOA information and then making MOA classification for the unknown toxicity. A novel data processing step based on wavelet transform is introduced to extract important features from the original TCRC data. From the dose response curves, time interval leading to higher classification success rate can be selected as input to enhance the performance of the machine learning algorithm. This is particularly helpful when handling cases with limited and imbalanced data. The validation of the proposed method is demonstrated by the supervised learning algorithm applied to the exposure data of HepG2 cell line to 63 chemicals with 11 concentrations in each test case. Classification success rate in the range of 85 to 95 % are obtained using SVM for MOA classification with two clusters to cases up to four clusters. Wavelet transform is capable of capturing important features of TCRCs for MOA classification. The proposed SVM scheme incorporated with wavelet transform has a great potential for large scale MOA classification and high-through output chemical screening.

  2. Automatically designed machine vision system for the localization of CCA transverse section in ultrasound images.

    Science.gov (United States)

    Benes, Radek; Karasek, Jan; Burget, Radim; Riha, Kamil

    2013-01-01

    The common carotid artery (CCA) is a source of important information that doctors can use to evaluate the patients' health. The most often measured parameters are arterial stiffness, lumen diameter, wall thickness, and other parameters where variation with time is usually measured. Unfortunately, the manual measurement of dynamic parameters of the CCA is time consuming, and therefore, for practical reasons, the only alternative is automatic approach. The initial localization of artery is important and must precede the main measurement. This article describes a novel method for the localization of CCA in the transverse section of a B-mode ultrasound image. The novel method was designed automatically by using the grammar-guided genetic programming (GGGP). The GGGP searches for the best possible combination of simple image processing tasks (independent building blocks). The best possible solution is represented with the highest detection precision. The method is tested on a validation database of CCA images that was specially created for this purpose and released for use by other scientists. The resulting success of the proposed solution was 82.7%, which exceeded the current state of the art by 4% while the computation time requirements were acceptable. The paper also describes an automatic method that was used in designing the proposed solution. This automatic method provides a universal approach to designing complex solutions with the support of evolutionary algorithms.

  3. Egg volume prediction using machine vision technique based on pappus theorem and artificial neural network.

    Science.gov (United States)

    Soltani, Mahmoud; Omid, Mahmoud; Alimardani, Reza

    2015-05-01

    Egg size is one of the important properties of egg that is judged by customers. Accordingly, in egg sorting and grading, the size of eggs must be considered. In this research, a new method of egg volume prediction was proposed without need to measure weight of egg. An accurate and efficient image processing algorithm was designed and implemented for computing major and minor diameters of eggs. Two methods of egg size modeling were developed. In the first method, a mathematical model was proposed based on Pappus theorem. In second method, Artificial Neural Network (ANN) technique was used to estimate egg volume. The determined egg volume by these methods was compared statistically with actual values. For mathematical modeling, the R(2), Mean absolute error and maximum absolute error values were obtained as 0.99, 0.59 cm(3) and 1.69 cm(3), respectively. To determine the best ANN, R(2) test and RMSEtest were used as selection criteria. The best ANN topology was 2-28-1 which had the R(2) test and RMSEtest of 0.992 and 0.66, respectively. After system calibration, the proposed models were evaluated. The results which indicated the mathematical modeling yielded more satisfying results. So this technique was selected for egg size determination.

  4. Crop species identification using machine vision of computer extracted individual leaves

    Science.gov (United States)

    Camargo Neto, João; Meyer, George E.

    2005-11-01

    An unsupervised method for plant species identification was developed which uses computer extracted individual whole leaves from color images of crop canopies. Green canopies were isolated from soil/residue backgrounds using a modified Excess Green and Excess Red separation method. Connected components of isolated green regions of interest were changed into pixel fragments using the Gustafson-Kessel fuzzy clustering method. The fragments were reassembled as individual leaves using a genetic optimization algorithm and a fitness method. Pixels of whole leaves were then analyzed using the elliptic Fourier shape and Haralick's classical textural feature analyses. A binary template was constructed to represent each selected leaf region of interest. Elliptic Fourier descriptors were generated from a chain encoding of the leaf boundary. Leaf template orientation was corrected by rotating each extracted leaf to a standard horizontal position. This was done using information provided from the first harmonic set of coefficients. Textural features were computed from the grayscale co-occurrence matrix of the leaf pixel set. Standardized leaf orientation significantly improved the leaf textural venation results. Principle component analysis from SAS (R) was used to select the best Fourier descriptors and textural indices. Indices of local homogeneity, and entropy were found to contribute to improved classification rates. A SAS classification model was developed and correctly classified 83% of redroot pigweed, 100% of sunflower 83% of soybean, and 73% of velvetleaf species. An overall plant species correct classification rate of 86% was attained.

  5. Machine-vision-based bar code scanning for long-range applications

    Science.gov (United States)

    Banta, Larry E.; Pertl, Franz A.; Rosenecker, Charles; Rosenberry-Friend, Kimberly A.

    1998-10-01

    Bar code labeling of products has become almost universal in most industries. However, in the steel industry, problems with high temperatures, harsh physical environments and the large sizes of the products and material handling equipment have slowed implementation of bar code based systems in the hot end of the mill. Typical laser-based bar code scanners have maximum scan distances of only 15 feet or so. Longer distance models have been developed which require the use of retro reflective paper labels, but the labels must be very large, are expensive, and cannot stand the heat and physical abuse of the steel mill environment. Furthermore, it is often difficult to accurately point a hand held scanner at targets in bright sunlight or at long distances. An automated product tag reading system based on CCD cameras and computer image processing has been developed by West Virginia University, and demonstrated at the Weirton Steel Corporation. The system performs both the pointing and reading functions. A video camera is mounted on a pan/tilt head, and connected to a personal computer through a frame grabber board. The computer analyzes the images, and can identify product ID tags in a wide-angle scene. It controls the camera to point at each tag and zoom for a closeup picture. The closeups are analyzed and the program need both a barcode and the corresponding alphanumeric code on the tag. This paper describes the camera pointing and bar-code reading functions of the algorithm. A companion paper describes the OCR functions.

  6. Parameters selection in gene selection using Gaussian kernel support vector machines by genetic algorithm

    Institute of Scientific and Technical Information of China (English)

    2005-01-01

    In microarray-based cancer classification, gene selection is an important issue owing to the large number of variables and small number of samples as well as its non-linearity. It is difficult to get satisfying results by using conventional linear statistical methods. Recursive feature elimination based on support vector machine (SVM RFE) is an effective algorithm for gene selection and cancer classification, which are integrated into a consistent framework. In this paper, we propose a new method to select parameters of the aforementioned algorithm implemented with Gaussian kernel SVMs as better alternatives to the common practice of selecting the apparently best parameters by using a genetic algorithm to search for a couple of optimal parameter. Fast implementation issues for this method are also discussed for pragmatic reasons. The proposed method was tested on two representative hereditary breast cancer and acute leukaemia datasets. The experimental results indicate that the proposed method performs well in selecting genes and achieves high classification accuracies with these genes.

  7. A fast and precise indoor localization algorithm based on an online sequential extreme learning machine.

    Science.gov (United States)

    Zou, Han; Lu, Xiaoxuan; Jiang, Hao; Xie, Lihua

    2015-01-15

    Nowadays, developing indoor positioning systems (IPSs) has become an attractive research topic due to the increasing demands on location-based service (LBS) in indoor environments. WiFi technology has been studied and explored to provide indoor positioning service for years in view of the wide deployment and availability of existing WiFi infrastructures in indoor environments. A large body of WiFi-based IPSs adopt fingerprinting approaches for localization. However, these IPSs suffer from two major problems: the intensive costs of manpower and time for offline site survey and the inflexibility to environmental dynamics. In this paper, we propose an indoor localization algorithm based on an online sequential extreme learning machine (OS-ELM) to address the above problems accordingly. The fast learning speed of OS-ELM can reduce the time and manpower costs for the offline site survey. Meanwhile, its online sequential learning ability enables the proposed localization algorithm to adapt in a timely manner to environmental dynamics. Experiments under specific environmental changes, such as variations of occupancy distribution and events of opening or closing of doors, are conducted to evaluate the performance of OS-ELM. The simulation and experimental results show that the proposed localization algorithm can provide higher localization accuracy than traditional approaches, due to its fast adaptation to various environmental dynamics.

  8. A Novel Classification Algorithm Based on Incremental Semi-Supervised Support Vector Machine

    Science.gov (United States)

    Gao, Fei; Mei, Jingyuan; Sun, Jinping; Wang, Jun; Yang, Erfu; Hussain, Amir

    2015-01-01

    For current computational intelligence techniques, a major challenge is how to learn new concepts in changing environment. Traditional learning schemes could not adequately address this problem due to a lack of dynamic data selection mechanism. In this paper, inspired by human learning process, a novel classification algorithm based on incremental semi-supervised support vector machine (SVM) is proposed. Through the analysis of prediction confidence of samples and data distribution in a changing environment, a “soft-start” approach, a data selection mechanism and a data cleaning mechanism are designed, which complete the construction of our incremental semi-supervised learning system. Noticeably, with the ingenious design procedure of our proposed algorithm, the computation complexity is reduced effectively. In addition, for the possible appearance of some new labeled samples in the learning process, a detailed analysis is also carried out. The results show that our algorithm does not rely on the model of sample distribution, has an extremely low rate of introducing wrong semi-labeled samples and can effectively make use of the unlabeled samples to enrich the knowledge system of classifier and improve the accuracy rate. Moreover, our method also has outstanding generalization performance and the ability to overcome the concept drift in a changing environment. PMID:26275294

  9. A Novel Classification Algorithm Based on Incremental Semi-Supervised Support Vector Machine.

    Directory of Open Access Journals (Sweden)

    Fei Gao

    Full Text Available For current computational intelligence techniques, a major challenge is how to learn new concepts in changing environment. Traditional learning schemes could not adequately address this problem due to a lack of dynamic data selection mechanism. In this paper, inspired by human learning process, a novel classification algorithm based on incremental semi-supervised support vector machine (SVM is proposed. Through the analysis of prediction confidence of samples and data distribution in a changing environment, a "soft-start" approach, a data selection mechanism and a data cleaning mechanism are designed, which complete the construction of our incremental semi-supervised learning system. Noticeably, with the ingenious design procedure of our proposed algorithm, the computation complexity is reduced effectively. In addition, for the possible appearance of some new labeled samples in the learning process, a detailed analysis is also carried out. The results show that our algorithm does not rely on the model of sample distribution, has an extremely low rate of introducing wrong semi-labeled samples and can effectively make use of the unlabeled samples to enrich the knowledge system of classifier and improve the accuracy rate. Moreover, our method also has outstanding generalization performance and the ability to overcome the concept drift in a changing environment.

  10. A Fast and Precise Indoor Localization Algorithm Based on an Online Sequential Extreme Learning Machine

    Directory of Open Access Journals (Sweden)

    Han Zou

    2015-01-01

    Full Text Available Nowadays, developing indoor positioning systems (IPSs has become an attractive research topic due to the increasing demands on location-based service (LBS in indoor environments. WiFi technology has been studied and explored to provide indoor positioning service for years in view of the wide deployment and availability of existing WiFi infrastructures in indoor environments. A large body of WiFi-based IPSs adopt fingerprinting approaches for localization. However, these IPSs suffer from two major problems: the intensive costs of manpower and time for offline site survey and the inflexibility to environmental dynamics. In this paper, we propose an indoor localization algorithm based on an online sequential extreme learning machine (OS-ELM to address the above problems accordingly. The fast learning speed of OS-ELM can reduce the time and manpower costs for the offline site survey. Meanwhile, its online sequential learning ability enables the proposed localization algorithm to adapt in a timely manner to environmental dynamics. Experiments under specific environmental changes, such as variations of occupancy distribution and events of opening or closing of doors, are conducted to evaluate the performance of OS-ELM. The simulation and experimental results show that the proposed localization algorithm can provide higher localization accuracy than traditional approaches, due to its fast adaptation to various environmental dynamics.

  11. A Support Vector Machine Hydrometeor Classification Algorithm for Dual-Polarization Radar

    Directory of Open Access Journals (Sweden)

    Nicoletta Roberto

    2017-07-01

    Full Text Available An algorithm based on a support vector machine (SVM is proposed for hydrometeor classification. The training phase is driven by the output of a fuzzy logic hydrometeor classification algorithm, i.e., the most popular approach for hydrometer classification algorithms used for ground-based weather radar. The performance of SVM is evaluated by resorting to a weather scenario, generated by a weather model; the corresponding radar measurements are obtained by simulation and by comparing results of SVM classification with those obtained by a fuzzy logic classifier. Results based on the weather model and simulations show a higher accuracy of the SVM classification. Objective comparison of the two classifiers applied to real radar data shows that SVM classification maps are spatially more homogenous (textural indices, energy, and homogeneity increases by 21% and 12% respectively and do not present non-classified data. The improvements found by SVM classifier, even though it is applied pixel-by-pixel, can be attributed to its ability to learn from the entire hyperspace of radar measurements and to the accurate training. The reliability of results and higher computing performance make SVM attractive for some challenging tasks such as its implementation in Decision Support Systems for helping pilots to make optimal decisions about changes inthe flight route caused by unexpected adverse weather.

  12. An Efficient Multiobjective Backtracking Search Algorithm for Single Machine Scheduling with Controllable Processing Times

    Directory of Open Access Journals (Sweden)

    Chao Lu

    2017-01-01

    Full Text Available The scheduling problem with controllable processing times (CPT is one of the most important research topics in the scheduling field due to its widespread application. Because of the complexity of this problem, a majority of research mainly addressed single-objective small scale problems. However, most practical problems are multiobjective and large scale issues. Multiobjective metaheuristics are very efficient in solving such problems. This paper studies a single machine scheduling problem with CPT for minimizing total tardiness and compression cost simultaneously. We aim to develop a new multiobjective discrete backtracking search algorithm (MODBSA to solve this problem. To accommodate the characteristic of the problem, a solution representation is constructed by a permutation vector and an amount vector of compression processing times. Furthermore, two major improvement strategies named adaptive selection scheme and total cost reduction strategy are developed. The adaptive selection scheme is used to select a suitable population to enhance the search efficiency of MODBSA, and the total cost reduction strategy is developed to further improve the quality of solutions. For the assessment of MODBSA, MODBSA is compared with other algorithms including NSGA-II, SPEA2, and PAES. Experimental results demonstrate that the proposed MODBSA is a promising algorithm for such scheduling problem.

  13. Robust signal recognition algorithm based on machine learning in heterogeneous networks

    Institute of Scientific and Technical Information of China (English)

    Xiaokai Liu; Rong Li; Chenglin Zhao; Pengbiao Wang

    2016-01-01

    There are various heterogeneous networks for terminals to deliver a better quality of service. Signal system recognition and classification contribute a lot to the process. However, in low signal to noise ratio (SNR) circumstances or under time-varying multipath channels, the majority of the existing algorithms for signal recognition are already facing limitations. In this series, we present a robust signal recogni-tion method based upon the original and latest updated ver-sion of the extreme learning machine (ELM) to help users to switch between networks. The ELM utilizes signal characte- ristics to distinguish systems. The superiority of this algorithm lies in the random choices of hidden nodes and in the fact that it determines the output weights analyticaly, which result in lower complexity. Theoreticaly, the algorithm tends to ofer a good generalization performance at an extremely fast speed of learning. Moreover, we implement the GSM/WCDMA/LTE mod-els in the Matlab environment by using the Simulink tools. The simulations reveal that the signals can be recognized suc-cessfuly to achieve a 95% accuracy in a low SNR (0 dB) environment in the time-varying multipath Rayleigh fading channel.

  14. Novel pose measurement for agricultural vehicle guided by machine vision%视觉导航农用车辆相对位姿测量新方法

    Institute of Scientific and Technical Information of China (English)

    周俊; 姬长英; 刘成良

    2006-01-01

    农田环境中农作物大多呈近似直线的行垄分布特点,农用车辆自主视觉导航时通常利用这些景物特征作为跟踪目标.提出了一种计算车辆相对于跟踪目标位姿的新型方法,首先分析了传统算法中存在的计算量大、忽视图像平面中各像素权重不同等缺陷,而后依据跟踪路径局部线性模型假设,详细地推导了算法过程.基于视觉导航原型车辆的试验结果表明,与人工测量值相比,横向距离和航向角的误差均值都等于零,标准差分别为3cm 和0.62deg.%Some agricultural tasks consist of applying chemical fertilizer to crops, but the products are often applied throughout the field in most cases, which cause pollution of water and possible chemical residues. In order to apply the products selectively and reduce the quantity of application, an autonomous vehicle can be used. Generally, this kind of vehicle follows the crop rows autonomously in the field where plants are arranged in rows, so its pose relative to crop row is important for tracking algorithm to work. With the machine vision, a novel method to calculate this pose was demonstrated, which could adapt to the complex characteristics of field environment excellently. First, some shortcomings involved in the conventional measuring method were analyzed carefully, such as processing time being long, pixel weight in the digital image being ignored and so on. With the local linear model of the tracked crop row then the algorithm was deduced at full length. Finally, based on the prototype of autonomous agricultural vehicle, the experiment was carried out, and it was shown that compared with the manual measurement the standard deviation of offset was 3 cm and of heading angle 0.62 deg while without any fixed displacement.

  15. 采棉机视觉导航路线图像检测方法%Detection for navigation route for cotton harvester based on machine vision

    Institute of Scientific and Technical Information of China (English)

    李景彬; 陈兵旗; 刘阳; 查涛

    2013-01-01

    Auto-navigation has a great significance in increasing the operating quality and production efficiency of agriculture machinery, such as improving the working environment and security situation for workers, reducing the labor intensity, etc. The vision navigation has many technical advantages that it can adapt to the complicated field of the operating environment, has wide detection range and has rich and complete information. It is the research focus in the field of agriculture machinery auto-navigation. How to extract routes fast, accurately, and effectively in the natural environment is the key in vision navigation. The vision navigation routes’ detect of a Cotton-picker is the main premise to achieve its auto-navigation. There are many difficulties in detecting the operation routes of a cotton-picker in the field. For example, during harvest, there are many kinds of target features like stalks, cotton, bolls, leaves, weeds in the cotton field and its outside region. In addition, a little cotton may be left on the stalks in the harvested region when we use machine to pick. This paper puts forward the detection algorithms of the operation routes of a cotton-picker, the edge and the end of the cotton field by analyzing the different color features of the harvested region, the un-harvested region, the outside region, and the end of the field. First, we used the difference of color components, such as 3B-R-G, |R-G|, |R-B| and |G-B| to extract the target features of the inner and outside of the cotton field respectively, and smooth the image using the moving average method by the set length. Then by finding the rose critical point of the crest that is based on the lowest trough point to the un-harvested region, and connecting with the line detect result of the previous frame, we determine the alternate point group of a straight line transform. At last, we extracted the navigation routes based on Passing a Known Point Hough Transform (PKPHT). The test proves that

  16. Application of machine learning algorithms to the study of noise artifacts in gravitational-wave data

    CERN Document Server

    Biswas, Rahul; Cao, Junwei; Essick, Reed; Hodge, Kari Alison; Katsavounidis, Erotokritos; Kim, Kyungmin; Kim, Young-Min; Bigot, Eric-Olivier Le; Lee, Chang-Hwan; Oh, John J; Oh, Sang Hoon; Son, Edwin J; Vaulin, Ruslan; Wang, Xiaoge; Ye, Tao

    2013-01-01

    The sensitivity of searches for astrophysical transients in data from the LIGO is generally limited by the presence of transient, non-Gaussian noise artifacts, which occur at a high-enough rate such that accidental coincidence across multiple detectors is non-negligible. Furthermore, non-Gaussian noise artifacts typically dominate over the background contributed from stationary noise. These "glitches" can easily be confused for transient gravitational-wave signals, and their robust identification and removal will help any search for astrophysical gravitational-waves. We apply Machine Learning Algorithms (MLAs) to the problem, using data from auxiliary channels within the LIGO detectors that monitor degrees of freedom unaffected by astrophysical signals. The number of auxiliary-channel parameters describing these disturbances may also be extremely large; an area where MLAs are particularly well-suited. We demonstrate the feasibility and applicability of three very different MLAs: Artificial Neural Networks, Su...

  17. Screw Remaining Life Prediction Based on Quantum Genetic Algorithm and Support Vector Machine

    Directory of Open Access Journals (Sweden)

    Xiaochen Zhang

    2017-01-01

    Full Text Available To predict the remaining life of ball screw, a screw remaining life prediction method based on quantum genetic algorithm (QGA and support vector machine (SVM is proposed. A screw accelerated test bench is introduced. Accelerometers are installed to monitor the performance degradation of ball screw. Combined with wavelet packet decomposition and isometric mapping (Isomap, the sensitive feature vectors are obtained and stored in database. Meanwhile, the sensitive feature vectors are randomly chosen from the database and constitute training samples and testing samples. Then the optimal kernel function parameter and penalty factor of SVM are searched with the method of QGA. Finally, the training samples are used to train optimized SVM while testing samples are adopted to test the prediction accuracy of the trained SVM so the screw remaining life prediction model can be got. The experiment results show that the screw remaining life prediction model could effectively predict screw remaining life.

  18. 机器视觉在农业生产中的应用研究%Summary of Research on Machine Vision Application in Agricultural Production

    Institute of Scientific and Technical Information of China (English)

    刁智华; 王会丹; 魏伟

    2014-01-01

    Because of the non-destructive , high accuracy , speed and other advantages , Machine vision technology was widely used in modern agricultural production .Based on the previous research results and analysis of literature , the pro-gress in agricultural product quality grading and testing ,controlling injurious insects and weeds in farmland , agricultural automatic picking system and crop growth process detection and agricultural machine navigation based on machine vision technology were reviewed in this paper .And then they were analyzed and summarized .In the end ,open problems and fur-ther research of machine vision technology in agricultural production application were discussed .%机器视觉技术因其非破坏性、精度高、速度快等特点,在现代农业生产中得到广泛应用。基于前人研究成果和文献分析,综述了近年来机器视觉技术在农产品质量分级与检测、农田病虫草害控制、农业自动采摘系统、农作物生长过程检测以及农业机械导航等方面的国内外研究进展,并对机器视觉技术在各领域的研究情况进行分析和总结,提出了机器视觉技术在农业生产应用中存在的问题和未来的研究方向。

  19. Application of machine learning algorithms to the study of noise artifacts in gravitational-wave data

    Science.gov (United States)

    Biswas, Rahul; Blackburn, Lindy; Cao, Junwei; Essick, Reed; Hodge, Kari Alison; Katsavounidis, Erotokritos; Kim, Kyungmin; Kim, Young-Min; Le Bigot, Eric-Olivier; Lee, Chang-Hwan; Oh, John J.; Oh, Sang Hoon; Son, Edwin J.; Tao, Ye; Vaulin, Ruslan; Wang, Xiaoge

    2013-09-01

    The sensitivity of searches for astrophysical transients in data from the Laser Interferometer Gravitational-wave Observatory (LIGO) is generally limited by the presence of transient, non-Gaussian noise artifacts, which occur at a high enough rate such that accidental coincidence across multiple detectors is non-negligible. These “glitches” can easily be mistaken for transient gravitational-wave signals, and their robust identification and removal will help any search for astrophysical gravitational waves. We apply machine-learning algorithms (MLAs) to the problem, using data from auxiliary channels within the LIGO detectors that monitor degrees of freedom unaffected by astrophysical signals. Noise sources may produce artifacts in these auxiliary channels as well as the gravitational-wave channel. The number of auxiliary-channel parameters describing these disturbances may also be extremely large; high dimensionality is an area where MLAs are particularly well suited. We demonstrate the feasibility and applicability of three different MLAs: artificial neural networks, support vector machines, and random forests. These classifiers identify and remove a substantial fraction of the glitches present in two different data sets: four weeks of LIGO’s fourth science run and one week of LIGO’s sixth science run. We observe that all three algorithms agree on which events are glitches to within 10% for the sixth-science-run data, and support this by showing that the different optimization criteria used by each classifier generate the same decision surface, based on a likelihood-ratio statistic. Furthermore, we find that all classifiers obtain similar performance to the benchmark algorithm, the ordered veto list, which is optimized to detect pairwise correlations between transients in LIGO auxiliary channels and glitches in the gravitational-wave data. This suggests that most of the useful information currently extracted from the auxiliary channels is already described

  20. How to measure metallicity from five-band photometry with supervised machine learning algorithms

    CERN Document Server

    Acquaviva, Viviana

    2015-01-01

    We demonstrate that it is possible to measure metallicity from the SDSS five-band photometry to better than 0.1 dex using supervised machine learning algorithms. Using spectroscopic estimates of metallicity as ground truth, we build, optimize and train several estimators to predict metallicity. We use the observed photometry, as well as derived quantities such as stellar mass and photometric redshift, as features, and we build two sample data sets at median redshifts of 0.103 and 0.218 and median r-band magnitude of 17.5 and 18.3 respectively. We find that ensemble methods, such as Random Forests of Trees and Extremely Randomized Trees, and Support Vector Machines all perform comparably well and can measure metallicity with a Root Mean Square Error (RMSE) of 0.081 and 0.090 for the two data sets when all objects are included. The fraction of outliers (objects for which the difference between true and predicted metallicity is larger than 0.2 dex) is only 2.2 and 3.9% respectively, and the RMSE decreases to 0.0...

  1. Semi-supervised prediction of gene regulatory networks using machine learning algorithms

    Indian Academy of Sciences (India)

    Nihir Patel; T L Wang

    2015-10-01

    Use of computational methods to predict gene regulatory networks (GRNs) from gene expression data is a challenging task. Many studies have been conducted using unsupervised methods to fulfill the task; however, such methods usually yield low prediction accuracies due to the lack of training data. In this article, we propose semi-supervised methods for GRN prediction by utilizing two machine learning algorithms, namely, support vector machines (SVM) and random forests (RF). The semi-supervised methods make use of unlabelled data for training. We investigated inductive and transductive learning approaches, both of which adopt an iterative procedure to obtain reliable negative training data from the unlabelled data. We then applied our semi-supervised methods to gene expression data of Escherichia coli and Saccharomyces cerevisiae, and evaluated the performance of our methods using the expression data. Our analysis indicated that the transductive learning approach outperformed the inductive learning approach for both organisms. However, there was no conclusive difference identified in the performance of SVM and RF. Experimental results also showed that the proposed semi-supervised methods performed better than existing supervised methods for both organisms.

  2. The applications of machine learning algorithms in the modeling of estrogen-like chemicals.

    Science.gov (United States)

    Liu, Huanxiang; Yao, Xiaojun; Gramatica, Paola

    2009-06-01

    Increasing concern is being shown by the scientific community, government regulators, and the public about endocrine-disrupting chemicals that, in the environment, are adversely affecting human and wildlife health through a variety of mechanisms, mainly estrogen receptor-mediated mechanisms of toxicity. Because of the large number of such chemicals in the environment, there is a great need for an effective means of rapidly assessing endocrine-disrupting activity in the toxicology assessment process. When faced with the challenging task of screening large libraries of molecules for biological activity, the benefits of computational predictive models based on quantitative structure-activity relationships to identify possible estrogens become immediately obvious. Recently, in order to improve the accuracy of prediction, some machine learning techniques were introduced to build more effective predictive models. In this review we will focus our attention on some recent advances in the use of these methods in modeling estrogen-like chemicals. The advantages and disadvantages of the machine learning algorithms used in solving this problem, the importance of the validation and performance assessment of the built models as well as their applicability domains will be discussed.

  3. Synthetic tests of passive microwave brightness temperature assimilation over snow covered land using machine learning algorithms

    Science.gov (United States)

    Forman, B. A.

    2015-12-01

    A novel data assimilation framework is evaluated that assimilates passive microwave (PMW) brightness temperature (Tb) observations into an advanced land surface model for the purpose of improving snow depth and snow water equivalent (SWE) estimates across regional- and continental-scales. The multifrequency, multipolarization framework employs machine learning algorithms to predict PMW Tb as a function of land surface model state information and subsequently merges the predicted PMW Tb with observed PMW Tb from the Advanced Microwave Scanning Radiometer (AMSR-E). The merging procedure is predicated on conditional probabilities computed within a Bayesian statistical framework using either an Ensemble Kalman Filter (EnKF) or an Ensemble Kalman Smoother (EnKS). The data assimilation routine produces a conditioned (updated) estimate of modeled SWE that is more accurate and contains less uncertainty than the model without assimilation. A synthetic case study is presented for select locations in North America that compares model results with and without assimilation against synthetic observations of snow depth and SWE. It is shown that the data assimilation framework improves modeled estimates of snow depth and SWE during both the accumulation and ablation phases of the snow season. Further, it is demonstrated that the EnKS outperforms the EnKF implementation due to its ability to better modulate high frequency noise into the conditioned estimates. The overarching findings from this study demonstrate the feasibility of machine learning algorithms for use as an observation model operator within a data assimilation framework in order to improve model estimates of snow depth and SWE across regional- and continental-scales.

  4. Application of Machine Learning Algorithms to the Study of Noise Artifacts in Gravitational-Wave Data

    Science.gov (United States)

    Biswas, Rahul; Blackburn, Lindy L.; Cao, Junwei; Essick, Reed; Hodge, Kari Alison; Katsavounidis, Erotokritos; Kim, Kyungmin; Young-Min, Kim; Le Bigot, Eric-Olivier; Lee, Chang-Hwan; hide

    2014-01-01

    The sensitivity of searches for astrophysical transients in data from the Laser Interferometer Gravitationalwave Observatory (LIGO) is generally limited by the presence of transient, non-Gaussian noise artifacts, which occur at a high-enough rate such that accidental coincidence across multiple detectors is non-negligible. Furthermore, non-Gaussian noise artifacts typically dominate over the background contributed from stationary noise. These "glitches" can easily be confused for transient gravitational-wave signals, and their robust identification and removal will help any search for astrophysical gravitational-waves. We apply Machine Learning Algorithms (MLAs) to the problem, using data from auxiliary channels within the LIGO detectors that monitor degrees of freedom unaffected by astrophysical signals. Terrestrial noise sources may manifest characteristic disturbances in these auxiliary channels, inducing non-trivial correlations with glitches in the gravitational-wave data. The number of auxiliary-channel parameters describing these disturbances may also be extremely large; high dimensionality is an area where MLAs are particularly well-suited. We demonstrate the feasibility and applicability of three very different MLAs: Artificial Neural Networks, Support Vector Machines, and Random Forests. These classifiers identify and remove a substantial fraction of the glitches present in two very different data sets: four weeks of LIGO's fourth science run and one week of LIGO's sixth science run. We observe that all three algorithms agree on which events are glitches to within 10% for the sixth science run data, and support this by showing that the different optimization criteria used by each classifier generate the same decision surface, based on a likelihood-ratio statistic. Furthermore, we find that all classifiers obtain similar limiting performance, suggesting that most of the useful information currently contained in the auxiliary channel parameters we extract

  5. Automatic ultrasonic breast lesions detection using support vector machine based algorithm

    Science.gov (United States)

    Yeh, Chih-Kuang; Miao, Shan-Jung; Fan, Wei-Che; Chen, Yung-Sheng

    2007-03-01

    It is difficult to automatically detect tumors and extract lesion boundaries in ultrasound images due to the variance in shape, the interference from speckle noise, and the low contrast between objects and background. The enhancement of ultrasonic image becomes a significant task before performing lesion classification, which was usually done with manual delineation of the tumor boundaries in the previous works. In this study, a linear support vector machine (SVM) based algorithm is proposed for ultrasound breast image training and classification. Then a disk expansion algorithm is applied for automatically detecting lesions boundary. A set of sub-images including smooth and irregular boundaries in tumor objects and those in speckle-noised background are trained by the SVM algorithm to produce an optimal classification function. Based on this classification model, each pixel within an ultrasound image is classified into either object or background oriented pixel. This enhanced binary image can highlight the object and suppress the speckle noise; and it can be regarded as degraded paint character (DPC) image containing closure noise, which is well known in perceptual organization of psychology. An effective scheme of removing closure noise using iterative disk expansion method has been successfully demonstrated in our previous works. The boundary detection of ultrasonic breast lesions can be further equivalent to the removal of speckle noise. By applying the disk expansion method to the binary image, we can obtain a significant radius-based image where the radius for each pixel represents the corresponding disk covering the specific object information. Finally, a signal transmission process is used for searching the complete breast lesion region and thus the desired lesion boundary can be effectively and automatically determined. Our algorithm can be performed iteratively until all desired objects are detected. Simulations and clinical images were introduced to

  6. The Research of Multi Target Tracking Based on Machine Vision%基于机器视觉的多目标跟踪技术的研究

    Institute of Scientific and Technical Information of China (English)

    徐仲勋; 黄科程

    2015-01-01

    Machine vision can help the robot object recognition and operations on objects. In this paper,based on the industrial robot, a machine vision system is added. The system is composed of the robot,camera,image acquisition card,computer and system software. With the robot vision system,features and objects recognition can capture the target object,then the object position,the control of industri⁃al robots to perform tasks.%机器视觉可以帮助机器识别物体并对物体进行作业。本文在工业机器人的设计基础上,增加一个机器视觉系统。系统由机器人、摄像机、图像采集卡、计算机及系统软件所构成。机器人凭着这个视觉系统,可以捕获目标物体的特征并且识别目标物体,然后对物体进行定位,最后控制工业机器人完成作业任务。

  7. Design of Clinical Support Systems Using Integrated Genetic Algorithm and Support Vector Machine

    Science.gov (United States)

    Chen, Yung-Fu; Huang, Yung-Fa; Jiang, Xiaoyi; Hsu, Yuan-Nian; Lin, Hsuan-Hung

    Clinical decision support system (CDSS) provides knowledge and specific information for clinicians to enhance diagnostic efficiency and improving healthcare quality. An appropriate CDSS can highly elevate patient safety, improve healthcare quality, and increase cost-effectiveness. Support vector machine (SVM) is believed to be superior to traditional statistical and neural network classifiers. However, it is critical to determine suitable combination of SVM parameters regarding classification performance. Genetic algorithm (GA) can find optimal solution within an acceptable time, and is faster than greedy algorithm with exhaustive searching strategy. By taking the advantage of GA in quickly selecting the salient features and adjusting SVM parameters, a method using integrated GA and SVM (IGS), which is different from the traditional method with GA used for feature selection and SVM for classification, was used to design CDSSs for prediction of successful ventilation weaning, diagnosis of patients with severe obstructive sleep apnea, and discrimination of different cell types form Pap smear. The results show that IGS is better than methods using SVM alone or linear discriminator.

  8. Water quality of Danube Delta systems: ecological status and prediction using machine-learning algorithms.

    Science.gov (United States)

    Stoica, C; Camejo, J; Banciu, A; Nita-Lazar, M; Paun, I; Cristofor, S; Pacheco, O R; Guevara, M

    2016-01-01

    Environmental issues have a worldwide impact on water bodies, including the Danube Delta, the largest European wetland. The Water Framework Directive (2000/60/EC) implementation operates toward solving environmental issues from European and national level. As a consequence, the water quality and the biocenosis structure was altered, especially the composition of the macro invertebrate community which is closely related to habitat and substrate heterogeneity. This study aims to assess the ecological status of Southern Branch of the Danube Delta, Saint Gheorghe, using benthic fauna and a computational method as an alternative for monitoring the water quality in real time. The analysis of spatial and temporal variability of unicriterial and multicriterial indices were used to assess the current status of aquatic systems. In addition, chemical status was characterized. Coliform bacteria and several chemical parameters were used to feed machine-learning (ML) algorithms to simulate a real-time classification method. Overall, the assessment of the water bodies indicated a moderate ecological status based on the biological quality elements or a good ecological status based on chemical and ML algorithms criteria.

  9. Forest classification trees and forest support vector machines algorithms: Demonstration using microarray data.

    Science.gov (United States)

    Zintzaras, Elias; Kowald, Axel

    2010-05-01

    Classification into multiple classes when the measured variables are outnumbered is a major methodological challenge in -omics studies. Two algorithms that overcome the dimensionality problem are presented: the forest classification tree (FCT) and the forest support vector machines (FSVM). In FCT, a set of variables is randomly chosen and a classification tree (CT) is grown using a forward classification algorithm. The process is repeated and a forest of CTs is derived. Finally, the most frequent variables from the trees with the smallest apparent misclassification rate (AMR) are used to construct a productive tree. In FSVM, the CTs are replaced by SVMs. The methods are demonstrated using prostate gene expression data for classifying tissue samples into four tumor types. For threshold split value 0.001 and utilizing 100 markers the productive CT consisted of 29 terminal nodes and achieved perfect classification (AMR=0). When the threshold value was set to 0.01, a tree with 17 terminal nodes was constructed based on 15 markers (AMR=7%). In FSVM, reducing the fraction of the forest that was used to construct the best classifier from the top 80% to the top 20% reduced the misclassification to 25% (when using 200 markers). The proposed methodologies may be used for identifying important variables in high dimensional data. Furthermore, the FCT allows exploring the data structure and provides a decision rule.

  10. A suction detection system for rotary blood pumps based on the Lagrangian support vector machine algorithm.

    Science.gov (United States)

    Wang, Yu; Simaan, Marwan A

    2013-05-01

    The Left Ventricular Assist Device (LVAD) is a rotary mechanical pump that is implanted in patients with congestive heart failure to help the left ventricle in pumping blood in the circulatory system. However, using such a device may result in a very dangerous event, called ventricular suction that can cause ventricular collapse due to overpumping of blood from the left ventricle when the rotational speed of the pump is high. Therefore, a reliable technique for detecting ventricular suction is crucial. This paper presents a new suction detection system that can precisely classify pump flow patterns, based on a Lagrangian Support Vector Machine (LSVM) model that combines six suction indices extracted from the pump flow signal to make a decision about whether the pump is in suction, approaching suction, or not in suction. The proposed method has been tested using in vivo experimental data based on two different pumps. The simulation results show that the system can produce superior performance in terms of classification accuracy, stability, learning speed, and good robustness compared to three other existing suction detection methods and the original SVM-based algorithm. The ability of the proposed algorithm to detect suction provides a reliable platform for the development of a feedback control system to control the speed of the pump while at the same time ensuring that suction is avoided.

  11. The use of machine learning algorithms to design a generalized simplified denitrification model

    Science.gov (United States)

    Oehler, F.; Rutherford, J. C.; Coco, G.

    2010-10-01

    We propose to use machine learning (ML) algorithms to design a simplified denitrification model. Boosted regression trees (BRT) and artificial neural networks (ANN) were used to analyse the relationships and the relative influences of different input variables towards total denitrification, and an ANN was designed as a simplified model to simulate total nitrogen emissions from the denitrification process. To calibrate the BRT and ANN models and test this method, we used a database obtained collating datasets from the literature. We used bootstrapping to compute confidence intervals for the calibration and validation process. Both ML algorithms clearly outperformed a commonly used simplified model of nitrogen emissions, NEMIS, which is based on denitrification potential, temperature, soil water content and nitrate concentration. The ML models used soil organic matter % in place of a denitrification potential and pH as a fifth input variable. The BRT analysis reaffirms the importance of temperature, soil water content and nitrate concentration. Generalization, although limited to the data space of the database used to build the ML models, could be improved if pH is used to differentiate between soil types. Further improvements in model performance and generalization could be achieved by adding more data.

  12. Sentiment Analysis on Movie Reviews: A Comparative Study of Machine Learning Algorithms and Open Source Technologies

    Directory of Open Access Journals (Sweden)

    B. Narendra

    2016-08-01

    Full Text Available Social Networks such as Facebook, Twitter, Linked In etc… are rich in opinion data and thus Sentiment Analysis has gained a great attention due to the abundance of this ever growing opinion data. In this research paper our target set is movie reviews. There are diverge range of mechanisms to express their data which may be either subjective, objective or a mixture of both. Besides the data collected from World Wide Web consists of lot of noisy data. It is very much true that we are going to apply some pre-processing techniques and compare the accuracy using Machine Learning algorithm Naïve Bayes Classifier. With ever growing demand to mine the Big Data the open source software technologies such as Hadoop using map reducing paradigm has gained a lot of pragmatic importance. This paper illustrates a comparitive study of sentiment analysis of movie reviews using Naïve Bayes Classifier and Apache Hadoop in order to calculate the performance of the algorithms and show that Map Reduce paradigm of Apache Hadoop performed better than Naïve Bayes Classifier.

  13. Time series online prediction algorithm based on least squares support vector machine

    Institute of Scientific and Technical Information of China (English)

    WU Qiong; LIU Wen-ying; YANG Yi-han

    2007-01-01

    Deficiencies of applying the traditional least squares support vector machine (LS-SVM) to time series online prediction were specified. According to the kernel function matrix's property and using the recursive calculation of block matrix, a new time series online prediction algorithm based on improved LS-SVM was proposed. The historical training results were fully utilized and the computing speed of LS-SVM was enhanced. Then, the improved algorithm was applied to time series online prediction. Based on the operational data provided by the Northwest Power Grid of China, the method was used in the transient stability prediction of electric power system. The results show that, compared with the calculation time of the traditional LS-SVM(75-1 600 ms), that of the proposed method in different time windows is 40-60 ms, and the prediction accuracy(normalized root mean squared error) of the proposed method is above 0.8. So the improved method is better than the traditional LS-SVM and more suitable for time series online prediction.

  14. Modelling soil water retention using support vector machines with genetic algorithm optimisation.

    Science.gov (United States)

    Lamorski, Krzysztof; Sławiński, Cezary; Moreno, Felix; Barna, Gyöngyi; Skierucha, Wojciech; Arrue, José L

    2014-01-01

    This work presents point pedotransfer function (PTF) models of the soil water retention curve. The developed models allowed for estimation of the soil water content for the specified soil water potentials: -0.98, -3.10, -9.81, -31.02, -491.66, and -1554.78 kPa, based on the following soil characteristics: soil granulometric composition, total porosity, and bulk density. Support Vector Machines (SVM) methodology was used for model development. A new methodology for elaboration of retention function models is proposed. Alternative to previous attempts known from literature, the ν-SVM method was used for model development and the results were compared with the formerly used the C-SVM method. For the purpose of models' parameters search, genetic algorithms were used as an optimisation framework. A new form of the aim function used for models parameters search is proposed which allowed for development of models with better prediction capabilities. This new aim function avoids overestimation of models which is typically encountered when root mean squared error is used as an aim function. Elaborated models showed good agreement with measured soil water retention data. Achieved coefficients of determination values were in the range 0.67-0.92. Studies demonstrated usability of ν-SVM methodology together with genetic algorithm optimisation for retention modelling which gave better performing models than other tested approaches.

  15. Modelling Soil Water Retention Using Support Vector Machines with Genetic Algorithm Optimisation

    Directory of Open Access Journals (Sweden)

    Krzysztof Lamorski

    2014-01-01

    Full Text Available This work presents point pedotransfer function (PTF models of the soil water retention curve. The developed models allowed for estimation of the soil water content for the specified soil water potentials: –0.98, –3.10, –9.81, –31.02, –491.66, and –1554.78 kPa, based on the following soil characteristics: soil granulometric composition, total porosity, and bulk density. Support Vector Machines (SVM methodology was used for model development. A new methodology for elaboration of retention function models is proposed. Alternative to previous attempts known from literature, the ν-SVM method was used for model development and the results were compared with the formerly used the C-SVM method. For the purpose of models’ parameters search, genetic algorithms were used as an optimisation framework. A new form of the aim function used for models parameters search is proposed which allowed for development of models with better prediction capabilities. This new aim function avoids overestimation of models which is typically encountered when root mean squared error is used as an aim function. Elaborated models showed good agreement with measured soil water retention data. Achieved coefficients of determination values were in the range 0.67–0.92. Studies demonstrated usability of ν-SVM methodology together with genetic algorithm optimisation for retention modelling which gave better performing models than other tested approaches.

  16. The use of machine learning algorithms to design a generalized simplified denitrification model

    Directory of Open Access Journals (Sweden)

    F. Oehler

    2010-04-01

    Full Text Available We designed generalized simplified models using machine learning algorithms (ML to assess denitrification at the catchment scale. In particular, we designed an artificial neural network (ANN to simulate total nitrogen emissions from the denitrification process. Boosted regression trees (BRT, another ML was also used to analyse the relationships and the relative influences of different input variables towards total denitrification. To calibrate the ANN and BRT models, we used a large database obtained by collating datasets from the literature. We developed a simple methodology to give confidence intervals for the calibration and validation process. Both ML algorithms clearly outperformed a commonly used simplified model of nitrogen emissions, NEMIS. NEMIS is based on denitrification potential, temperature, soil water content and nitrate concentration. The ML models used soil organic matter % in place of a denitrification potential and pH as a fifth input variable. The BRT analysis reaffirms the importance of temperature, soil water content and nitrate concentration. Generality of the ANN model may also be improved if pH is used to differentiate between soil types. Further improvements in model performance can be achieved by lessening dataset effects.

  17. Design and Implementation of Gesture Recognition System Based on Machine Vision%基于机器视觉的手势识别系统设计与实现

    Institute of Scientific and Technical Information of China (English)

    刘德强

    2015-01-01

    机器视觉的手势识别系统,采用FPGA驱动摄像头OV7650采集视频图像,通过控制FT245RL实现与PC机之间的高速数据传输,在PC机上有运用OpenCV视觉库简单快速的识别手势,避免了高难度算法编程.通过大量实验验证,该系统开发周期短,识别率高.%This article describes the gesture recognition system based on machine vision.The system uses FPGA driven OV7650 camera to capture video images,through controlling FT245RL to achieve high-speed data transfer between PC and FT245RL.Using the simple and rapid identification gestures of OpenCV vision library on PC can avoid the difficult algorithmic programming.A large number of experiments show that the system development cycle is short and recognition rate is high.

  18. Exploiting machine learning algorithms for tree species classification in a semiarid woodland using RapidEye image

    CSIR Research Space (South Africa)

    Adelabu, S

    2013-11-01

    Full Text Available in semiarid environments. In this study, we examined the suitability of 5-band RapidEye satellite data for the classification of five tree species in mopane woodland of Botswana using machine leaning algorithms with limited training samples. We performed...

  19. A branch-and-bound algorithm for single-machine earliness-tardiness scheduling with idle time

    NARCIS (Netherlands)

    J.A. Hoogeveen (Han); S.L. van de Velde (Steef)

    1996-01-01

    textabstractPresents a branch-and-bound algorithm which is based upon many dominance rules and various lower bound approaches, including relaxation of the machine capacity, data manipulation and Lagrangian relaxation. Insertion of the idle time for a given sequence; Properties of the proposed lower

  20. Cyborg systems as platforms for computer-vision algorithm-development for astrobiology

    Science.gov (United States)

    McGuire, Patrick Charles; Rodríguez Manfredi, José Antonio; Martínez, Eduardo Sebastián; Gómez Elvira, Javier; Díaz Martínez, Enrique; Ormö, Jens; Neuffer, Kai; Giaquinta, Antonino; Camps Martínez, Fernando; Lepinette Malvitte, Alain; Pérez Mercader, Juan; Ritter, Helge; Oesker, Markus; Ontrup, Jörg; Walter, Jörg

    2004-03-01

    Employing the allegorical imagery from the film "The Matrix", we motivate and discuss our "Cyborg Astrobiologist" research program. In this research program, we are using a wearable computer and video camcorder in order to test and train a computer-vision system to be a field-geologist and field-astrobiologist.

  1. From geospatial observations of ocean currents to causal predictors of spatio-economic activity using computer vision and machine learning

    Science.gov (United States)

    Popescu, Florin; Ayache, Stephane; Escalera, Sergio; Baró Solé, Xavier; Capponi, Cecile; Panciatici, Patrick; Guyon, Isabelle

    2016-04-01

    The big data transformation currently revolutionizing science and industry forges novel possibilities in multi-modal analysis scarcely imaginable only a decade ago. One of the important economic and industrial problems that stand to benefit from the recent expansion of data availability and computational prowess is the prediction of electricity demand and renewable energy generation. Both are correlates of human activity: spatiotemporal energy consumption patterns in society are a factor of both demand (weather dependent) and supply, which determine cost - a relation expected to strengthen along with increasing renewable energy dependence. One of the main drivers of European weather patterns is the activity of the Atlantic Ocean and in particular its dominant Northern Hemisphere current: the Gulf Stream. We choose this particular current as a test case in part due to larger amount of relevant data and scientific literature available for refinement of analysis techniques. This data richness is due not only to its economic importance but also to its size being clearly visible in radar and infrared satellite imagery, which makes it easier to detect using Computer Vision (CV). The power of CV techniques makes basic analysis thus developed scalable to other smaller and less known, but still influential, currents, which are not just curves on a map, but complex, evolving, moving branching trees in 3D projected onto a 2D image. We investigate means of extracting, from several image modalities (including recently available Copernicus radar and earlier Infrared satellites), a parameterized representation of the state of the Gulf Stream and its environment that is useful as feature space representation in a machine learning context, in this case with the EC's H2020-sponsored 'See.4C' project, in the context of which data scientists may find novel predictors of spatiotemporal energy flow. Although automated extractors of Gulf Stream position exist, they differ in methodology

  2. About one algorithm of the broken line approximation and a modeling of tool path for CNC plate cutting machines

    Science.gov (United States)

    Kurennov, D. V.; Petunin, A. A.; Repnitskii, V. B.; Shipacheva, E. N.

    2016-12-01

    The problem of approximating two-dimensional broken line with composite curve consisting of arc and line segments is considered. The resulting curve nodes have to coincide with source broken line nodes. This problem arises in the development of control programs for CNC (computer numerical control) cutting machines, permitting circular interpolation. An original algorithm is proposed minimizing the number of nodes for resulting composite curve. The algorithm is implemented in the environment of the Russian CAD system T-Flex CAD using its API (Application Program Interface). The algorithm optimality is investigated. The result of test calculation along with its geometrical visualization is given.

  3. A Gradient-Simulated Annealing Algorithm of Pre-location-Based Best Fitting of Blank to Complex Surfaces Machining

    Institute of Scientific and Technical Information of China (English)

    MALi-ming; JIANGHong; WANGXiao-chun

    2004-01-01

    The algorithm is divided into two steps. The first step pre-locates the blank by aligning its centre of gravity and approximate normal vector with those of destination surfaces, with largest overlap of projections of two objects on a plane perpendicular to the normal vector. The second step is optimizing an objective function by means of gradient-simulated annealing algorithm to get the best matching of a set of distributed points on the blank and destination surfaces. An example for machining hydroelectric turbine blades is given to verify the effectiveness of algorithm.

  4. 基于机器视觉技术的无精蛋鉴别研究%Identification of Wind Egg Based on Machine Vision Technology

    Institute of Scientific and Technical Information of China (English)

    李天华; 李海亮

    2011-01-01

    [Objectives] To identify the wind egg by machine vision technology so as to improve the accuracy and rapidness of removing the wind eggs during the incubation process. [ Method] The images of hatching eggs were firstly acquired by CCD camera, and then imported into the computer to analyze it gray scale and detect the hatching eggs, the computer transmitted the signals to the core controller, which controlled the sense station to screen out the wind eggs. [ Result ] The wind eggs could be rapidly and accurately removed after 6-day hatching by using the machine vision technology. [Conclusion] The identification of wind egg by machine vision technology had high theoretical and practical significance.%[目的]采用机器视觉技术鉴别无精蛋,提高孵化生产过程中剔除无精蛋的准确率和速度.[方法]首先用CCD照相机获取入孵蛋图像,再将入孵蛋图像传输到计算机主机,主机测控软件对图像进行灰度特征分析、比较等,对入孵蛋进行检测,主机输出信号给核心控制器,由核心控制器控制检测台对无精蛋进行分选.[结果]采用机器视觉技术可以在入孵后第6天快速准确地剔除无精蛋.[结论]利用机器视觉检测技术鉴别无精蛋,具有较高的理论价值和实际生产意义.

  5. Socket Quality Detection Based on Machine Vision%基于机器视觉的插座品质检测方法

    Institute of Scientific and Technical Information of China (English)

    徐德明; 汪成龙

    2016-01-01

    针对目前人工成本上升,人眼易出现视觉疲劳导致检测效率低下等问题,本文提出了一种基于机器视觉的插座品质检测方法.主要研究内容如下:1)研究了基于模板匹配的插座机器视觉定位方法.2)研究了基于灰度均值的螺丝缺陷机器视觉检测方法.3)研究了基于灰度标准偏差的插孔缺陷机器视觉检测方法.根据以上检测方法,编写了检测软件,在生产线上进行测试,对插座螺丝和插孔缺陷的识别率达到100%,平均每帧图像处理仅耗时100ms,表明该方法具有准确率高、速度快的优点.%For the current rising labor costs and low detection efficiency of eye fatigue, this paper presents a method for detecting socket quality based on machine vision. The main research contents are as follows: 1) Socket positioning method based on template matching with machine vision. 2) The machine vision detection method for screws defects based on the mean of gray value. 3) The detection method of jack defect based on grayscale standard deviation. In the production line testing, screws and jack defect recognition rate have reached 100%. And the average per-frame image processing only takes 100ms, which shows that this method has high accuracy, fast speed advantages.

  6. Parallel machine scheduling with step-deteriorating jobs and setup times by a hybrid discrete cuckoo search algorithm

    Science.gov (United States)

    Guo, Peng; Cheng, Wenming; Wang, Yi

    2015-11-01

    This article considers the parallel machine scheduling problem with step-deteriorating jobs and sequence-dependent setup times. The objective is to minimize the total tardiness by determining the allocation and sequence of jobs on identical parallel machines. In this problem, the processing time of each job is a step function dependent upon its starting time. An individual extended time is penalized when the starting time of a job is later than a specific deterioration date. The possibility of deterioration of a job makes the parallel machine scheduling problem more challenging than ordinary ones. A mixed integer programming model for the optimal solution is derived. Due to its NP-hard nature, a hybrid discrete cuckoo search algorithm is proposed to solve this problem. In order to generate a good initial swarm, a modified Biskup-Hermann-Gupta (BHG) heuristic called MBHG is incorporated into the population initialization. Several discrete operators are proposed in the random walk of Lévy flights and the crossover search. Moreover, a local search procedure based on variable neighbourhood descent is integrated into the algorithm as a hybrid strategy in order to improve the quality of elite solutions. Computational experiments are executed on two sets of randomly generated test instances. The results show that the proposed hybrid algorithm can yield better solutions in comparison with the commercial solver CPLEX® with a one hour time limit, the discrete cuckoo search algorithm and the existing variable neighbourhood search algorithm.

  7. An imperialist competitive algorithm for a bi-objective parallel machine scheduling problem with load balancing consideration

    Directory of Open Access Journals (Sweden)

    Mansooreh Madani-Isfahani

    2013-04-01

    Full Text Available In this paper, we present a new Imperialist Competitive Algorithm (ICA to solve a bi-objective scheduling of parallel-unrelated machines where setup times are sequence dependent. The objectives include mean completion tasks and mean squares of deviations from machines workload from their averages. The performance of the proposed ICA (PICA method is examined using some randomly generated data and they are compared with three alternative methods including particle swarm optimization (PSO, original version of imperialist competitive algorithm (OICA and genetic algorithm (GA in terms of the objective function values. The preliminary results indicate that the proposed study outperforms other alternative methods. In addition, while OICA performs the worst as alternative solution strategy, PSO and GA seem to perform better.

  8. Application of OpenCV in machine vision detection system of cigarette case%OpenCV在条烟视觉检测系统中的应用

    Institute of Scientific and Technical Information of China (English)

    周传宏; 陈郭宝; 王怀虎; 康少博

    2011-01-01

    The cigarette case detection system is a special device, which is installed on the production line in front of packing machine to detect the appearance defect.The system can detect cigarette case in real-time and remove the defective products in time with characteristics of high-speed, efficient and accurate, which is an auto-detection system covering mechanic, optical, electric, computer and communications as well as other technologies.The application of the system greatly improves the automation of the tobacco company;In addition, it also can reduce the labor intensity,improve the working environment and ensure the product quality.The algorithm for common detection items of cigarette case was designed and the open source computer vision library OpenCV of Intel Company was used to improve the detection speed and accuracy in it.%条烟视觉检测系统是安装在条包生产线上,在条包装箱之前,对其外观缺陷进行检测的一种专用设备.系统可以对生产线上的条包进行实时检测,并及时剔除外观有缺陷的产品,具有高速、高效、准确的优点.该检测系统是集机械、光电、计算机、通信等技术为一体自动化检测系统,系统具有高速、高效、准确的优点,它的使用不仅可以提高烟草生产企业的自动化程度,还可以降低工人的劳动程度,改善工作环境,保证产品的质量.对常见条烟检测项进行了算法设计,并利用某公司的开源计算机视觉库OpenCV进行算法实现,提高了检测速度和精度.

  9. Machine Learning Algorithms For Predicting the Instability Timescales of Compact Planetary Systems

    Science.gov (United States)

    Tamayo, Daniel; Ali-Dib, Mohamad; Cloutier, Ryan; Huang, Chelsea; Van Laerhoven, Christa L.; Leblanc, Rejean; Menou, Kristen; Murray, Norman; Obertas, Alysa; Paradise, Adiv; Petrovich, Cristobal; Rachkov, Aleksandar; Rein, Hanno; Silburt, Ari; Tacik, Nick; Valencia, Diana

    2016-10-01

    The Kepler mission has uncovered hundreds of compact multi-planet systems. The dynamical pathways to instability in these compact systems and their associated timescales are not well understood theoretically. However, long-term stability is often used as a constraint to narrow down the space of orbital solutions from the transit data. This requires a large suite of N-body integrations that can each take several weeks to complete. This computational bottleneck is therefore an important limitation in our ability to characterize compact multi-planet systems.From suites of numerical simulations, previous studies have fit simple scaling relations between the instability timescale and various system parameters. However, the numerically simulated systems can deviate strongly from these empirical fits.We present a new approach to the problem using machine learning algorithms that have enjoyed success across a broad range of high-dimensional industry applications. In particular, we have generated large training sets of direct N-body integrations of synthetic compact planetary systems to train several regression models (support vector machine, gradient boost) that predict the instability timescale. We find that ensembling these models predicts the instability timescale of planetary systems better than previous approaches using the simple scaling relations mentioned above.Finally, we will discuss how these models provide a powerful tool for not only understanding the current Kepler multi-planet sample, but also for characterizing and shaping the radial-velocity follow-up strategies of multi-planet systems from the upcoming Transiting Exoplanet Survey Satellite (TESS) mission, given its shorter observation baselines.

  10. Towards a Vision Algorithm Compiler for Recognition of Partially Occluded 3-D Objects

    Science.gov (United States)

    1992-11-20

    0 2000 4000 (b) Actual Arem of (b) Mocle Surface Figure 5: Example distributions of a given feature value (area) over a model face. The distributions...and Paradigms, pages 564-584. Morgan Kaufmann, 1987. [GH91] W. Eric L. Grimson and Daniel P. Huttenlocher. On the verification of hy- pothesized...of Computer Vision and Pattern Recognition, pages 541-548, 1989. 51 [Hut88] Daniel P. Huttenlocher. Three-Dimensional Recognition of Solid Objects from

  11. Automated analysis of retinal imaging using machine learning techniques for computer vision [version 2; referees: 2 approved

    Directory of Open Access Journals (Sweden)

    Jeffrey De Fauw

    2017-06-01

    Full Text Available There are almost two million people in the United Kingdom living with sight loss, including around 360,000 people who are registered as blind or partially sighted. Sight threatening diseases, such as diabetic retinopathy and age related macular degeneration have contributed to the 40% increase in outpatient attendances in the last decade but are amenable to early detection and monitoring. With early and appropriate intervention, blindness may be prevented in many cases. Ophthalmic imaging provides a way to diagnose and objectively assess the progression of a number of pathologies including neovascular (“wet” age-related macular degeneration (wet AMD and diabetic retinopathy. Two methods of imaging are commonly used: digital photographs of the fundus (the ‘back’ of the eye and Optical Coherence Tomography (OCT, a modality that uses light waves in a similar way to how ultrasound uses sound waves. Changes in population demographics and expectations and the changing pattern of chronic diseases creates a rising demand for such imaging. Meanwhile, interrogation of such images is time consuming, costly, and prone to human error. The application of novel analysis methods may provide a solution to these challenges. This research will focus on applying novel machine learning algorithms to automatic analysis of both digital fundus photographs and OCT in Moorfields Eye Hospital NHS Foundation Trust patients. Through analysis of the images used in ophthalmology, along with relevant clinical and demographic information, DeepMind Health will investigate the feasibility of automated grading of digital fundus photographs and OCT and provide novel quantitative measures for specific disease features and for monitoring the therapeutic success.

  12. Automated analysis of retinal imaging using machine learning techniques for computer vision [version 1; referees: 2 approved

    Directory of Open Access Journals (Sweden)

    Jeffrey De Fauw

    2016-07-01

    Full Text Available There are almost two million people in the United Kingdom living with sight loss, including around 360,000 people who are registered as blind or partially sighted. Sight threatening diseases, such as diabetic retinopathy and age related macular degeneration have contributed to the 40% increase in outpatient attendances in the last decade but are amenable to early detection and monitoring. With early and appropriate intervention, blindness may be prevented in many cases.   Ophthalmic imaging provides a way to diagnose and objectively assess the progression of a number of pathologies including neovascular (“wet” age-related macular degeneration (wet AMD and diabetic retinopathy. Two methods of imaging are commonly used: digital photographs of the fundus (the ‘back’ of the eye and Optical Coherence Tomography (OCT, a modality that uses light waves in a similar way to how ultrasound uses sound waves. Changes in population demographics and expectations and the changing pattern of chronic diseases creates a rising demand for such imaging. Meanwhile, interrogation of such images is time consuming, costly, and prone to human error. The application of novel analysis methods may provide a solution to these challenges.   This research will focus on applying novel machine learning algorithms to automatic analysis of both digital fundus photographs and OCT in Moorfields Eye Hospital NHS Foundation Trust patients.   Through analysis of the images used in ophthalmology, along with relevant clinical and demographic information, Google DeepMind Health will investigate the feasibility of automated grading of digital fundus photographs and OCT and provide novel quantitative measures for specific disease features and for monitoring the therapeutic success.

  13. A bidirectional brain-machine interface algorithm that approximates arbitrary force-fields.

    Directory of Open Access Journals (Sweden)

    Alessandro Vato

    Full Text Available We examine bidirectional brain-machine interfaces that control external devices in a closed loop by decoding motor cortical activity to command the device and by encoding the state of the device by delivering electrical stimuli to sensory areas. Although it is possible to design this artificial sensory-motor interaction while maintaining two independent channels of communication, here we propose a rule that closes the loop between flows of sensory and motor information in a way that approximates a desired dynamical policy expressed as a field of forces acting upon the controlled external device. We previously developed a first implementation of this approach based on linear decoding of neural activity recorded from the motor cortex into a set of forces (a force field applied to a point mass, and on encoding of position of the point mass into patterns of electrical stimuli delivered to somatosensory areas. However, this previous algorithm had the limitation that it only worked in situations when the position-to-force map to be implemented is invertible. Here we overcome this limitation by developing a new non-linear form of the bidirectional interface that can approximate a virtually unlimited family of continuous fields. The new algorithm bases both the encoding of position information and the decoding of motor cortical activity on an explicit map between spike trains and the state space of the device computed with Multi-Dimensional-Scaling. We present a detailed computational analysis of the performance of the interface and a validation of its robustness by using synthetic neural responses in a simulated sensory-motor loop.

  14. Testing of machinability of mould steel 40CrMnMo7 using genetic algorithm

    OpenAIRE

    Stoić, Antun; Cukor, Goran; Kopač, Janez

    2015-01-01

    This paper deals with testing of hard materials machinability by high speed turning process and influence of cutting parameters on machinability rates. Todetermine machinability rates, surface roughness, tool wear and cutting force components were measured. In accordance with expected influence of certain parameter on machinability, experiments were designed and performed todetermine mathematical models of the measured values over full response range. Obtained mathematical models were used fo...

  15. Robot Vision Library

    Science.gov (United States)

    Howard, Andrew B.; Ansar, Adnan I.; Litwin, Todd E.; Goldberg, Steven B.

    2009-01-01

    The JPL Robot Vision Library (JPLV) provides real-time robot vision algorithms for developers who are not vision specialists. The package includes algorithms for stereo ranging, visual odometry and unsurveyed camera calibration, and has unique support for very wideangle lenses

  16. Detecting anomalies in astronomical signals using machine learning algorithms embedded in an FPGA

    Science.gov (United States)

    Saez, Alejandro F.; Herrera, Daniel E.

    2016-07-01

    Taking a large interferometer for radio astronomy, such as the ALMA1 telescope, where the amount of stations (50 in the case of ALMA's main array, which can extend to 64 antennas) produces an enormous amount of data in a short period of time - visibilities can be produced every 16msec or total power information every 1msec (this means up to 2016 baselines). With the aforementioned into account it is becoming more difficult to detect problems in the signal produced by each antenna in a timely manner (one antenna produces 4 x 2GHz spectral windows x 2 polarizations, which means a 16 GHz bandwidth signal which is later digitized using 3-bits samplers). This work will present an approach based on machine learning algorithms for detecting problems in the already digitized signal produced by the active antennas (the set of antennas which is being used in an observation). The aim of this work is to detect unsuitable, or totally corrupted, signals. In addition, this development also provides an almost real time warning which finally helps stop and investigate the problem in order to avoid collecting useless information.

  17. Malware Detection Module using Machine Learning Algorithms to Assist in Centralized Security in Enterprise Networks

    Directory of Open Access Journals (Sweden)

    Priyank Singhal

    2012-02-01

    Full Text Available Malicious software is abundant in a world of innumerable computer users, who are constantly faced withthese threats from various sources like the internet, local networks and portable drives. Malware is potentially low to high risk and can cause systems to function incorrectly, steal data and even crash.Malware may be executable or system library files in the form of viruses, worms, Trojans, all aimed atbreaching the security of the system and compromising user privacy. Typically, anti-virus software is based on a signature definition system which keeps updating from the internet and thus keeping track of known viruses. While this may be sufficient for home-users, a security risk from a new virus could threaten an entire enterprise network. This paper proposes a new and more sophisticated antivirus engine that can not only scan files, but also build knowledge and detect files as potential viruses. This is done by extracting system API calls made by various normal and harmful executable, and using machine learning algorithms to classify and hence, rank files on a scale of security risk. While such a system is processor heavy, it is very effective when used centrally to protect an enterprise network which maybe more prone to such threats.

  18. Cognitive Development Optimization Algorithm Based Support Vector Machines for Determining Diabetes

    Directory of Open Access Journals (Sweden)

    Utku Kose

    2016-03-01

    Full Text Available The definition, diagnosis and classification of Diabetes Mellitus and its complications are very important. First of all, the World Health Organization (WHO and other societies, as well as scientists have done lots of studies regarding this subject. One of the most important research interests of this subject is the computer supported decision systems for diagnosing diabetes. In such systems, Artificial Intelligence techniques are often used for several disease diagnostics to streamline the diagnostic process in daily routine and avoid misdiagnosis. In this study, a diabetes diagnosis system, which is formed via both Support Vector Machines (SVM and Cognitive Development Optimization Algorithm (CoDOA has been proposed. Along the training of SVM, CoDOA was used for determining the sigma parameter of the Gauss (RBF kernel function, and eventually, a classification process was made over the diabetes data set, which is related to Pima Indians. The proposed approach offers an alternative solution to the field of Artificial Intelligence-based diabetes diagnosis, and contributes to the related literature on diagnosis processes.

  19. Machine learning algorithms for modeling groundwater level changes in agricultural regions of the U.S.

    Science.gov (United States)

    Sahoo, S.; Russo, T. A.; Elliott, J.; Foster, I.

    2017-05-01

    Climate, groundwater extraction, and surface water flows have complex nonlinear relationships with groundwater level in agricultural regions. To better understand the relative importance of each driver and predict groundwater level change, we develop a new ensemble modeling framework based on spectral analysis, machine learning, and uncertainty analysis, as an alternative to complex and computationally expensive physical models. We apply and evaluate this new approach in the context of two aquifer systems supporting agricultural production in the United States: the High Plains aquifer (HPA) and the Mississippi River Valley alluvial aquifer (MRVA). We select input data sets by using a combination of mutual information, genetic algorithms, and lag analysis, and then use the selected data sets in a Multilayer Perceptron network architecture to simulate seasonal groundwater level change. As expected, model results suggest that irrigation demand has the highest influence on groundwater level change for a majority of the wells. The subset of groundwater observations not used in model training or cross-validation correlates strongly (R > 0.8) with model results for 88 and 83% of the wells in the HPA and MRVA, respectively. In both aquifer systems, the error in the modeled cumulative groundwater level change during testing (2003-2012) was less than 2 m over a majority of the area. We conclude that our modeling framework can serve as an alternative approach to simulating groundwater level change and water availability, especially in regions where subsurface properties are unknown.

  20. Automated EEG artifact elimination by applying machine learning algorithms to ICA-based features

    Science.gov (United States)

    Radüntz, Thea; Scouten, Jon; Hochmuth, Olaf; Meffert, Beate

    2017-08-01

    Objective. Biological and non-biological artifacts cause severe problems when dealing with electroencephalogram (EEG) recordings. Independent component analysis (ICA) is a widely used method for eliminating various artifacts from recordings. However, evaluating and classifying the calculated independent components (IC) as artifact or EEG is not fully automated at present. Approach. In this study, we propose a new approach for automated artifact elimination, which applies machine learning algorithms to ICA-based features. Main results. We compared the performance of our classifiers with the visual classification results given by experts. The best result with an accuracy rate of 95% was achieved using features obtained by range filtering of the topoplots and IC power spectra combined with an artificial neural network. Significance. Compared with the existing automated solutions, our proposed method is not limited to specific types of artifacts, electrode configurations, or number of EEG channels. The main advantages of the proposed method is that it provides an automatic, reliable, real-time capable, and practical tool, which avoids the need for the time-consuming manual selection of ICs during artifact removal.