A survey of image processing techniques for agriculture

Computer technologies have been shown to improve agricultural productivity in a number of ways. One technique which is emerging as a useful tool is image processing. This paper presents a short survey on using image processing techniques to assist researchers and farmers to improve agricultural practices. Image processing has been used to assist with precision agriculture practices, weed and herbicide technologies, monitoring plant growth and plant nutrition management. This paper highlights the future potential for image processing for different agricultural industry contexts

Bidirectional optimization of the melting spinning process

This is the author's accepted manuscript (under the provisional title "Bi-directional optimization of the melting spinning process with an immune-enhanced neural network"). The final published article is available from the link below. Copyright 2014 @ IEEE.A bidirectional optimizing approach for the melting spinning process based on an immune-enhanced neural network is proposed. The proposed bidirectional model can not only reveal the internal nonlinear relationship between the process configuration and the quality indices of the fibers as final product, but also provide a tool for engineers to develop new fiber products with expected quality specifications. A neural network is taken as the basis for the bidirectional model, and an immune component is introduced to enlarge the searching scope of the solution field so that the neural network has a larger possibility to find the appropriate and reasonable solution, and the error of prediction can therefore be eliminated. The proposed intelligent model can also help to determine what kind of process configuration should be made in order to produce satisfactory fiber products. To make the proposed model practical to the manufacturing, a software platform is developed. Simulation results show that the proposed model can eliminate the approximation error raised by the neural network-based optimizing model, which is due to the extension of focusing scope by the artificial immune mechanism. Meanwhile, the proposed model with the corresponding software can conduct optimization in two directions, namely, the process optimization and category development, and the corresponding results outperform those with an ordinary neural network-based intelligent model. It is also proved that the proposed model has the potential to act as a valuable tool from which the engineers and decision makers of the spinning process could benefit.National Nature Science Foundation of China, Ministry of Education of China, the Shanghai Committee of Science and Technology), and the Fundamental Research Funds for the Central Universities

Are Picked-up Losers Really Losers?: Evidence from Scrapping Policies in the Japanese Spinning Industry, 1965-79

Scrapping policy induces or forces firms to dismantle and dispose of “excessive” machines, and was a typical policy tool for depressed or declining industries in postwar Japan. This paper examines the effects of this policy on the Japanese spinning industry from 1965-79 using firm-level data. After clarifying features in the policy scheme, I first calculated the productivity and (capital) profitability of a specified machine targeted by the scrapping policy by estimating the production function with data from the production stage. Then, several forms of (dis)investment functions were estimated to examine the direct effect on investment under policy implementation and the indirect effect on post-policy investment through the firm’s revelation of the expected long-term return on investment. The former effect crucially depended upon whether scrapping was compulsory or voluntary, as well as on the coverage of targeted machines. Compulsory scrapping distorted a firm’s investment decisions more than voluntary scrapping. Regarding the latter effect, firms picked up by voluntary scrapping in a certain period were shown to be losers still investing less or disinvesting in the post- treatment period. Firm exits also had a certain role in cutting down the unprofitable machines. Distortions by another related policy (a scrap-and-build constraint on investment) was also observed. As a result, this skillfully-designed policy scheme was, on the whole, far from a welfare-enhancing scheme

Evaluating the Validity and Reliability of Textile and Paper Fracture Characteristics in Forensic Comparative Analysis

In a comparative forensic analysis, an examiner can report that a physical fit exists between two torn or separated items when they realign in a manner unlikely to be replicated. Due to the common belief that it is unlikely that two unrelated fractured objects would match with distinctive characteristics, a physical fit represents the highest degree of association between two items. Nonetheless, despite the probative value that this evidence could have to a trier of fact, few studies have demonstrated such assumptions\u27 scientific validity and reliability. Moreover, there is a lack of consensus-based standard protocols for physical fit comparisons, making it difficult to demonstrate the basis for the features that constitute a “fit.” Since these analyses rely entirely on human judgment, they are highly subjective, which could be problematic in the absence of harmonized examination and interpretation criteria protocols. As a result, organizations like the National Institute of Justice and NIST-OSAC have identified the need for developing standardized methods and assessing potential error sources in this field. This research aims to address these gaps as applied to physical fits of textiles and paper. Here, standard criteria and prominent features for each material are defined to conduct physical fit examinations in a more reproducible manner. Additionally, a quantitative metric is used to quantify what constitutes a physical fit when conducting comparative analyses of textiles and paper, further increasing the validity and reliability of this methodology and providing a manner of assessing the weight of this evidence when presented in the courtroom. The first aim of this research involved the development of an objective and systematic method of quantifying the similarity between fractured textile samples. This was done by identifying relevant macroscopic and microscopic characteristics in the comparative analysis of a fractured textile dataset. Additionally, factors that affect the suitability of certain types of textiles for physical fit analysis were evaluated. Finally, the systematic score metric was implemented to quantify and document the quality of a physical fit and estimate error rates. The second objective of this study consisted of establishing the scientific foundations of individuality concerning the orientation of microfibers in fractured paper edges. In comparative analysis of paper, it is assumed that the microfibers deposited across the surface of paper are randomly oriented, a key feature for addressing the individuality of paper physical fits. However, this hypothesis has not been tested. This research evaluated the rarity and occurrence of microfiber alignments on fractured documents. It also quantified the comparative features of scissor-cut and hand-torn paper and the respective performance rates. Finally, the comparative analysis of textile and paper physical fits was validated through ground truth datasets and inter-examiner and intra-examiner variability studies. A ground truth blind dataset of known fits and known non-fits was created for 700 textile samples with various fiber types, weave patterns, and separation methods. Also, a set of 260 paper items, including 100 stamps and 160 office paper samples, were examined. The paper specimens contained handwritten or printed entries on two paper types and were separated by scissor-cut or hand-torn methods. This proposed research provides the criminal justice system with a valuable body of knowledge and a more objective and methodical assessment of the evidential value of physical fits of textiles, paper, and postage stamps

Research on the application status of image recognition technology in textile and clothing field

With the development of the times, computer, information technology gradually penetrated into all walks of life. Textile and clothing design, production, consumption integration of machine vision, graphic image recognition technology development have become a trend. At present, the intelligence degree of the textile and clothing industry has reached an unprecedented new height. In the production and identification of fabrics, the machine vision gradually replaces the manual work, and realizes the automatic and accurate production line. In the design, manufacturing, consumption and other aspects of clothing, image recognition technology has assumed the historical responsibility. Combined with the Internet of things, cloud computing and other technologies, it has improved the production efficiency and achieved intelligent design and production

A study of word association aids in information retrieval

Issued as Final project reports [nos. 1-2], Project no. G-36-65

Development and Properties of Kernel-based Methods for the Interpretation and Presentation of Forensic Evidence

The inference of the source of forensic evidence is related to model selection. Many forms of evidence can only be represented by complex, high-dimensional random vectors and cannot be assigned a likelihood structure. A common approach to circumvent this is to measure the similarity between pairs of objects composing the evidence. Such methods are ad-hoc and unstable approaches to the judicial inference process. While these methods address the dimensionality issue they also engender dependencies between scores when 2 scores have 1 object in common that are not taken into account in these models. The model developed in this research captures the dependencies between pairwise scores from a hierarchical sample and models them in the kernel space using a linear model. Our model is flexible to accommodate any kernel satisfying basic conditions and as a result is applicable to any type of complex high-dimensional data. An important result of this work is the asymptotic multivariate normality of the scores as the data dimension increases. As a result, we can: 1) model very high-dimensional data when other methods fail; 2) determine the source of multiple samples from a single trace in one calculation. Our model can be used to address high-dimension model selection problems in different situations and we show how to use it to assign Bayes factors to forensic evidence. We will provide examples of real-life problems using data from very small particles and dust analyzed by SEM/EDX, and colors of fibers quantified by microspectrophotometry