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To establish a process for cleaning and efficiently utilizing solid residues and residual liquid after co-fermentation of coal and corn straw, three coal samples combined with corn straw were used. Combustion tests were carried out using the solid residue to compare the changes in calorific value, slagging characteristics and air pollutant emission before and after fermentation. Biogas production experiments were performed using acclimated residual liquid to establish the biomethane production characteristics. Results show that the total calorific value of solid residue after co-fermentation of bituminous coal D, bituminous coal C and corn straw increased by 8.16% and 8.08%, respectively, compared to original samples. The slagging index also decreased during the combustion of solid residues. The diversity of bacterial community structure was the main causes for the significant reduction in air pollutant emissions from the combustion of solid residues. Acclimation experiments improved the removal rate of hemicelluloses from corn straw and increased biomethane production by 45.86%, 10.66% and 60.47%, respectively. This work can improve the clean conversion efficiency of coal and straw, not only reducing environmental pollution, but also transforming waste into clean energy, which has a positive impact on the environment and allows the sustainable use of resources.

To find the effect of coal chemical structure on biogas production, Lignite B was collected and extracted with nitrogen methylpyrrolidone (NMP), acetone and 0.60 mol/L NaOH. Simultaneously, methanogenic bacteria were enriched, and gas production experiments involving solvent extraction from residual coal and secondary gas production experiments involving coal were performed. The characteristics of biogas production, microcrystalline structure and coal chemical structure were analyzed. The results showed that the biogas production capacity of residual coal extracted by different solvents differed. The biogas production capacity of residual coal extracted by 0.60 mol/L NaOH was severely inhibited. The biogas production capacity of residual coal extracted by NMP and acetone was enhanced compared with that of raw coal. In contrast, primary residual coal still exhibits potential for methane production, but the methane production efficiency was reduced. Changes in the microcrystalline structure and functional groups of residual coal showed that solvent extraction increases the spacing and stacking height of the aromatic lamellae of coal, reduces the hydroxyl, methyl, methylene and aromatic hydrocarbon levels in coal, loosens the macromolecular network structure of coal, and enhances the connectivity of pores and fissures, thus allowing methane-producing bacteria to enter the coal mass and create favorable conditions for gas production through interactions between the two. X-ray photoelectron spectroscopy measurements showed that the secondary gas production increased the C content on the coal surface, decreased the O content and the O/C ratio, thus promoting the consumption of oxygen-containing functional groups on the coal surface to further produce gas.

The technologies for the bioconversion of CO2 to CH4 by microorganisms are clean and safe and have attracted extensive attention in the field of energy conservation and emission reduction in recent years. A series of biological gas production experiments were carried out using bituminous coal D and indigenous bacteria from the Qianqiu coal mine, with the addition of NaHCO3 to simulate CO2 sequestration in coal seams, to explore the feasibility of biomethanation of CO2 sequestered in coal seams. Changes in the characteristics of both the gas and liquid phases during the fermentation process were studied. The results showed that the methane production follows a "low-high-low" trend with an increase in the bicarbonate concentration. During the fermentation process, the pH and chemical oxygen demand (COD) values of the fermentation solution initially increased and later decreased. In the early stage of the fermentation experiment, the microorganisms mainly degraded the ethers; the alcohols were degraded in the later stage. Identification of the microbial community structure showed that although bicarbonate inhibits the growth of some microorganisms, it increases the hydrolysis capacity of the fermentation process and also enriches the hydrogenotrophic methanogens associated with the bioconversion process of CO2 to CH4. This indicates that the composition of the archaea in the fermentation solution transforms to support the bioconversion of CO2 to CH4. The results show that the CO2 sequestered in the coal seams can be converted into CH4 by indigenous bacteria, which can reduce greenhouse gas emissions and the re-leakage risk of CO2 and also produce clean energy.

There are a large amount of user behavior data in the recommendation system. The types of user behaviors are various, such as click, favorite, like, purchase and so on. However, most of the existing recommendation methods only consider single user behavior, such as purchase, ignoring the influence of auxiliary behavior data on target behavior. In order to make full use of multi-behavior data and further improve the recommendation performance, multi-behavior recommendation based on simplified graph convolutional networks(SGCNMB) is proposed in this paper. The SGCNMB model firstly initializes the embedding vectors for users and items in the shared embedding layer; then, in the embedding propagation layer, the simplified graph convolutional network is exploited to learn the user representations and item representations on a single behavior, the users' preferences on different single behavior are calculated respectively; finally, through the fusion prediction layer, the users' preferences on various behaviors are fused to get the top-n recommendation results of users on the target behavior. Extensive experiments were conducted on two real-world multi-behavior datasets. The experimental results demonstrate that SGCNMB can effectively take advantage of multi-behavior interaction data to further improve the recommendation performance.

In order to increase the utilization of coal slime, realize efficient utilization of resources and protect the environment, the feasibility of anaerobic fermentation technology employing coal slime was explored. The biodegradation of coal slimes and its influence on the utilization characteristics were analyzed using biogas production simulations, drying dehydration and thermogravimetric (TG) analysis. The results showed that the organic matter in various coal slimes could be converted to biomethane. In addition, the main methanogenic pathway was the reduction of CO2. Moreover, lower the metamorphic degree of coal slimes and higher the ash content, more conducive were they to the dehydration of coal slimes. After biodegradation, the temperatures of four coal slimes during the stages of release of moisture, volatile combustion, residual coke combustion and burnout advanced to varying degrees. Moreover, the combustion performance improved. The research results provided a novel idea for the efficient utilization of coal slime.

An outbreak of COVID-19 in Wuhan, China, at the end of 19 was followed by a national and global epidemic. On March 11, 2020, the World Health Organization declared COVID-19 a global pandemic. The COVID-19 virus is transmitted from person to person by droplets or direct contact, and there is no possibility of symptomatic transmission. It is a great threat to human life and health and affects the development of the global economy. It is at the background, a system is needed to predict COVID-19, so a clinical decision-making system based on deep learning for COVID-19 was proposed.

In recent years, deep learning algorithms have been rapidly developed and applied more and more widely in the field of biomedical engineering. Among them, the use of deep learning algorithms to decode physiological, psychological, or pathological states from electroencephalogram (EEG) signals is also attracting more and more attention. This paper reviews the applications of deep learning algorithms in motor imagery (MI) EEG in recent years and introduces the common algorithms, application status, and existing problems. Firstly, the research status of CNN and DBN on EEG signal classification in MI is described. Then, the classification of EEG signals by CNN and DBN in MI is discussed. Finally, some key problems of deep learning in MI-EEG signal decoding are summarized, such as high model complexity and lack of training data.

Reliable detection of obstacles around the vehicle is crucial for autonomous cars. We present a novel and robust ground shape invariant feature method for general obstacle detection with a car-mounted monocular fisheye camera. Both stationary and moving obstacles can be detected by our approach without recovering the camera motion. Firstly, In order to compute the ground shape invariant feature, the image is mapped into the top view image. And then feature points are extracted and matched between adjacent frames. Secondly, the points are grouped according to image patch partition. Finally, the ground shape invariant feature is computed for each group of points to detect obstacle points. Extensive experiments have been carried out with prerecorded video sequences including various obstacle types, various scenes and various illumination conditions. The experimental results show promising detection performance of the proposed method.

Ultrasound-guided renal biopsy is one of the most widely applicable diagnostic methods. The procedure involved ultrasound scanning of the lesion area and sampling using a puncture needle. The success rate of sampling depends largely on the accuracy of needle placement and requires a high degree of skill on the part of the operator. In this paper, we describe a visual puncture guidance system designed to increase the accuracy of needle puncture. Our solution is based on the Vuforia engine, which tracks the location of the puncture needle and ultrasonic probe in space. Our system can reduce the difficulty of puncture by providing an intuitive way to display the visual track. The operator’s field of view is enhanced on the screen, thus exercising 3D vision and eliminating the need to consult external monitors. Based on this prototype, our goal is to develop a complete system, reduce the training cost, and improve the overall precision of puncture, to improve the success rate of sampling.

Moving target detection is widely used in the field of intelligent monitoring. This paper proposes an Improved Frame Difference Method Fusion Horn-Schunck Optical Flow Method (IFHS) algorithm to detect moving targets in video surveillance. First, a three-frame difference method is performed on the image to obtain a difference image. Second, the differential image is tracked according to the HS optical flow method to obtain an optical flow vector. Finally, the moving target is obtained by fusing the optical flow vector information of the two difference results. The problem of incomplete contours of moving targets detected in video surveillance by the separate optical flow method and the three-frame difference method is solved. The experimental simulation and experimental applications in different scenarios prove the robustness and accuracy of the method.

Multi-Domain Neural Machine Translation (NMT) aims at building a single system that performs well on a range of target domains. However, along with the extreme diversity of cross-domain wording and phrasing style, the imperfections of training data distribution and the inherent defects of the current sequential learning process all contribute to making the task of multi-domain NMT very challenging. To mitigate these problems, we propose the Factorized Transformer, which consists of an in-depth factorization of the parameters of an NMT model, namely Transformer in this paper, into two categories: domain-shared ones that encode common cross-domain knowledge and domain-specific ones that are private for each constituent domain. We experiment with various designs of our model and conduct extensive validations on English to French open multi-domain dataset. Our approach achieves state-of-the-art performance and opens up new perspectives for multi-domain and open-domain applications.

Waste reuse energy generation is encouraged in many countries. A low H/C ratio in coal is a limiting factor that affects biomethanation. However, corn straw has a better H/C ratio, which is beneficial to methane production. A series of biological gas production experiments was carried out using lignite B, bituminous coal D, bituminous coal C, and corn straw to study the effect of synergistic biodegradation of coal combined with corn straw into biomethane. Methane production was used as an index to analyze the gas production characteristics of single coal, single corn straw, and coal and corn straw. The results show that the optimum proportion of the conversion of lignite B and corn straw to biomethane is 2:1, and the optimum proportion of the conversion of bituminous coal D, bituminous coal C, and corn straw into biomethane is 3:1, and corn straw has a significant enhancement effect on the biological gas production in this experiment. When lignite B, bituminous coal D, and bituminous coal C are, respectiv...

The anaerobic degradation of coal combined with straw biomass can promote the methane production. The biogas production potential and metabolic pathway were explored via the co-digestion simulation experiment of coal and corn straw. The results showed that 2 g of corn straw combined respectively with 4 g of bituminous coal A, 6 g of bituminous coal B and 4 g of bituminous coal C resulted in highest methane yields. The structure of lignocellulose in corn straw was partially degraded into guaiacyl and syringyl units. Meanwhile, the content of biodegradable tyrosine like protein and soluble microbial by-products in liquid phase significantly decreased. Significantly, the structure of archaea altered from aceticlastic to hydrogenotrophic methanogens when the fermentation substrate changed from high to low rank coal. The proportion of hydrogenotrophic methanogens was significantly higher than that of aceticlastic and methylotrophic methanogens, and the hydrogenotrophic pathway was dominant than the aceticlastic pathway.

Sesamin, a lignan from sesame seed, has been reported to attenuate chronic mild stress-induced depressive-like behaviors. Gut microbiota play pivotal roles in mediating psychological behaviors by regulating gut barrier integrity and systemic inflammatory responses. Here, we found that oral sesamin administration (50 mg/kg·bodyweight/day) significantly attenuated depressive, aversive, repetitive, and anxiety-like behaviors in a long-term multiple nonsocial stress-treated mice model. Sesamin inhibited stress-induced gut barrier integrity damage, reduced circulating lipopolysaccharide (LPS) levels, and suppressed neuroinflammatory responses. Moreover, sesamin treatment also restructured the gut microbiome by enhancing the relative abundances of Bacteroidales and S24-7. The correlation analysis indicated that the microbiota composition changes were strongly correlated with behavioral disorders, serotonin, norepinephrine, and LPS levels. In conclusion, sesamin has preventive effects on stress-induced behavioral and psychological disorders, which might be highly related to the reshaped microbiota composition. This study provides a clue for understanding the systemic mechanism of anti-depression effects of sesamin.

The mechanisms associated with the biomethane metabolism through the synergistic biodegradation of both coal and corn straw were explored to improve the utilization rate of corn straw. This applies to the filling of the goaf with corn straw and the production of biomethane using indigenous bacteria in the mine water with coal. The results showed that new macromolecular substances (e.g., Tetracosane and Pentacosane) were produced on the third day. A lower coal rank leads to a lower biodegradation rate of low-molecular-weight substances (e.g., butyric acid and valeric acid). Under the addition of coal samples, the biodegradation rate of cellulose, hemicellulose and lignin in corn straw could reached up to 29.82%, 35.79% and 6.16%, respectively. The addition of corn straw promoted the complementary advantages of archaeal genera (such as Methanosarina and Methanospirillum) and decreased the adverse bacterial genera (such as Desulfovibrio and Pseudomonas) in the fermentation system of single coal.

The algorithm based on kernel correlation filtering shows excellent performance. Aiming at the problem that traditional correlation filtering algorithms are sensitive to deformation and cannot solve the tracking failure caused by scale changes, this paper proposes a scale-adaptive correlation filtering algorithm combining statistical color feature. Firstly, predict and train the HOG features of the correlation filter and the color histogram features to obtain the respective response values, namely the template response and the histogram response. Then adopt the block method, and perform scale prediction based on the position of each response point. Finally, the final fusion response is calculated. The experimental results demonstrate that compared with traditional KCF, when the target scale has large scale change and deformation, the improved algorithm in this paper performs better, and can still track stably.

Moving target tracking is one of the core issues in the fields of automobile assisted driving and autonomous driving. This algorithm proposes a target tracking algorithm suitable for fisheye cameras in vehicle environment. A background point removal method based on median ordering is proposed to optimize the optical flow method to solve the problem of target scale change in fisheye camera videos. Multi-scale scaling processing for frames, tracking algorithm based on cyclic matrix structure (CSK), target tracking for multiple images after zooming, using fitting to determine the maximum corresponding value, reducing the error caused by target deformation on tracking results. The tracking test results of multiple fisheye camera videos in the vehicle environment show that the algorithm has high real-time tracking accuracy.

The aspect-level sentiment classification task is to determine the polarity of a sentence with respect to an aspect, which is typically a noun phrase. Example applications include product recommendation, where the aspect is the name of the product. The task is challenging since a model needs to learn to focus on only the part of the sentence that is related to the aspect. In this paper, we propose an informed neural network architecture, which takes advantage of a general property of comments that contains expressions of opposite sentiment polarities: the positive expressions often precede the negative ones, or vice versa. Specifically, we apply a "coarse-grained" attention to the left and right side contexts of the aspect, respectively. We evaluate our methods on two datasets: SemEval-2014 and a manual annotation dataset from Twitter. Our model achieves the state-of-the-art performance on the laptop dataset of SemEval-2014 and the Twitter dataset.

This study presents a hierarchical background modelling and subtraction approach for real-time detection of moving objects. At the first level, a novel pixel-wise background modelling method is proposed for coarse detection. The method can dynamically assign the optimal number of components for each pixel with the borrow–lend strategy. And a flexible learning rate which is variable and different for each component is presented to adapt to scene changes. Additionally, a new mechanism using a framework of finite state machine is introduced to maintain and update the background models. At the second level, in order to deal with sudden illumination changes, a block-wise foreground validation approach is adopted for refined detection. The authors compare the proposed approach with state-of-the-art methods and experimental results under various scenes demonstrate the robustness and effectiveness of the proposed approach.

Intrinsic and Sb-doped CdSe nanobelts (NBs) were synthesized via a thermal evaporation technique. The electrical transport properties of field effect transistors (FETs) fabricated using the NBs were investigated. The results indicate that the Sb-doped NBs behave as n-type semiconductors with improved electrical conductivity (10 −1 to 100 S/cm) compared with the intrinsic CdSe. Photodetectors made of single NB were also fabricated and investigated. The results show that Sb-doped NB photodetectors exhibit much higher responsivity (1.5 × 10 4 A/W) and external quantum efficiency (1.2 × 10 5 ) but lower on/off current ratio (∼253) and longer response time (≤40 ms). Furthermore, both electrical transport and optoelectrical properties of the as-synthesized CdSe NBs can be tuned by changing the doping concentration. The results indicate that the as-synthesized NBs are excellent building blocks for constructing electronic and optoelectronic devices.

Single-crystalline chlorine-doped cadmium selenide (CdSe) nanobelts (NBs) with a wurtzite structure were synthesised by using CdSe and InCl3 powder as sources via a co-evaporation approach. The investigation of the performance of the field-effect transistors fabricated from Cl-doped NBs shows n-type conduction behaviour and enhanced conductivity (1–70 S/cm). Furthermore, it is found that the photoconductivity (illuminated by a white light with a power density of 1.3 mW/cm2) increases with the enhancement of the conductivity. Photoconductive analysis reveals that the Cl-doped NBs show excellent photoresponse properties, with responsivity of 8.87 × 105 AW−1 and a corresponding external quantum efficiency of 1.74 × 106 when illuminated under a 650 nm light and biased 1 V. In addition, the electrical properties of the Cl-doped NBs can be influenced by changing the ambiance, which is caused by their surface states.

The controllable manipulation of a single nanowire (NW) using dual-nanomanipulators attached to a scanning electron microscope (e.g. selectively removing a single NW from the NW-cluster to a desired substrate) is significant for device fabrication. This can only be achieved when the contact between the NW and tungsten tip equipped in the nanomanipulator is mechanically strong. Here, a Joule heat welding technique is developed to create strong contacts between the NWs (including metal and semiconductor NWs) and the tungsten tips, which can greatly improve the manipulation technology that presents great potential in device fabrication.

We report on the growth of single crystalline wurtzite CuGaS 2 nanoribbons by solution-based method. By investigating the evolutions of structure and composition during growth, it is confirmed that Cu 1.75 S nanocrystals play the catalytic roles for the formation and growth of the CuGaS 2 nanoribbon. Derived from thermal decomposition of the copper precursor, the Cu 1.75 S nanocrystals formed firstly and were consumed gradually. When they exhausted, the growth of the nanoribbon stopped. Photoresponses of the CuGaS 2 nanoribbons were evaluated by I–V curves based on CuGaS 2 film fabricated by drop-casting method. The photocurrents of CuGaS 2 film showed almost 17-fold increase in the dark than under illumination, indicating that the as-synthesized CuGaS 2 nanoribbons are excellent photoactive materials for solar cell.

In this paper we present a pupil location method based on the OTSU method and Hough transform. Firstly, it finds a pint in the facula by gray summing operator for ensuring the probable area to find the center of the pupil, then it chooses one channel from R,G and B these three channels by distribution of histograms of three channels respectively for reducing the computing complexity. Thirdly it ensures an automatic threshold value by OTSU method and gets binary image. Finally in the binary image it uses Hough transform in certain area to fit the pupil edge. 384 eye images comprising an eye image database are located, our algorithm reaches 100% at accuracy. What is more, it ensures the robustness of location.

When a laser beam transmits through a multi-mode fiber, speckles show in the output beam. In this work, we study the laser speckle under static and dynamic scrambling by the histogram and line profile of far-field pattern. The results show that static scrambling has little effect on the intensity distribution. The dynamic scrambling reduces the speckles without changing the line profile. Two possible explanations are proposed.

The digital camera has become a requisite for people’s life, also essential in imaging applications, and it is important to get more accurate colors with digital camera. The colorimetric characterization of digital camera is the basis of image copy and color management process. One of the traditional methods for deriving a colorimetric mapping between camera RGB signals and the tristimulus values CIEXYZ is to use polynomial modeling with 3×11 polynomial transfer matrices. In this paper, an improved polynomial modeling is presented, in which the normalized luminance replaces the camera inherent RGB values in the traditional polynomial modeling. The improved modeling can be described by a two stage model. The first stage, relationship between the camera RGB values and normalized luminance with six gray patches in the X-rite ColorChecker 24-color card was described as "Gamma", camera RGB values were converted into normalized luminance using Gamma. The second stage, the traditional polynomial modeling was improved to the colorimetric mapping between normalized luminance and the CIEXYZ. Meanwhile, this method was used under daylight lighting environment, the users can not measure the CIEXYZ of the color target char using professional instruments, but they can accomplish the task of the colorimetric characterization of digital camera. The experimental results show that: (1) the proposed method for the colorimetric characterization of digital camera performs better than traditional polynomial modeling; (2) it’s a feasible approach to handle the color characteristics using this method under daylight environment without professional instruments, the result can satisfy for request of simple application.

This paper presents an obstacle detection method by using a monocular camera on a moving platform. The method can detect various static and moving obstacles based on multi-cues fusion. Firstly, an improved motion compensation cue is applied to detect the obstacles which violate the road plane assumption; Secondly, a novel image segmentation cue is introduced to increase the obstacle detection rate and decrease false detection rate; Furthermore, these cues are combined in a Bayesian framework to generate a probability map of obstacle; Finally, an efficient probability update model is presented to update the probability map and the obstacle regions can be generated. Experiment results show that the proposed obstacle detection method is robust to obstacle types, varying illumination conditions, and various scenes. Moreover, using the combined cues outperforms any individual cue.

Automatic analysis of medical cell images has been gaining more importance in pharmacology research. Segmentation and classification of cell images are the crucial steps in their analysis since results of segmentation and classification directly affect the following quantitative analysis steps. In this paper, we present a cell image segmentation and classification method based on OTSU method and connected region labeling. By K-L transform we choose one channel and ensure an automatic threshold value by OTSU method to make segmentation, then we adopt the method of connected region labeling designed by ourselves to make classification. The method has been applied to the segmentation and classification of several kinds of cell images. The experimental results show that the method is practical and effective; it has good robustness and ensures the accuracy.

Lane recognition plays an important role in driver assistance systems. Many Lane recognition methods have been proposed until now. But most methods are used to recognize the traditional painted lines and can't work on the road where there are only raised pavement lane markers such as reflective markers and bots dots. Lane recognition on this kind of roads is a difficult problem especially in the daytime for the raised pavement markers are poorly visible and not reflected. In this paper, a novel solution for lane recognition in this case is presented. Firstly, the dot filters and the OLS templates are proposed for location of raised pavement markers. Then a line fitting method based on Hough transform and clustering is used on the accumulation image of raised pavement markers to detect the implicit lane. The experimental results under various scenes show that the proposed method is effective and can be implemented in real-time.

Traffic sign, especially speed limit sign recognition is important in a driver assistance system. In this paper, a robust approach for real-time detection and recognition of speed limit sign is presented. It consists of two major steps: sign detection and sign recognition. In detection stage, Fast Radial Symmetry Transform is utilized to detect possible sign locations. Then the new method proposed, that is named “Largest Containing Circle” is used to segment characters region. In recognition stage, one fuzzy template matching method is applied to coarse recognize the sign number character, Furthermore, we conduct a similar character recognizer based on the local feature vector for fine recognition of the character. Experimental results in different conditions, including sunny, cloudy, foggy and rainy weather demonstrates that most speed limit signs can be correctly detected and recognized with a high accuracy and the average processing time is 15ms per frame on a standard PC.

This paper proposes a robust rear-view camera based moving object detection algorithm for backup aid and parking assist applications. A single fish-eye camera is used in order to get much larger FOV (field of view) for object detection. To detect various moving objects such as vehicles and pedestrians, the ego-motion of host vehicle is firstly estimated by A robust NGPP (near ground point projection) method. Then a novel point based moving object detection method is proposed which can detect fast motion as well as slight motion in the fish-eye image. Finally, a region based motion compensation method is used in order to filter out the false detection results caused by the error matching points. Experimental results under various conditions show that most of moving objects can be detected correctly by our algorithm.

Moving objects detection by an also moving camera plays an important role in driver assistance systems and robot navigations. Many motion detection methods have been proposed until now. But most of them are based on normal cameras with a limited view and not suitable for large FOV (field of view) cameras. For motion detection using large FOV cameras, there are two main challenges. One comes from difficulties to tell moving objects from moving background due to camera motion. The other comes from the image distortion brought by large FOV cameras. These two problems are solved in our approach by a novel motion detector which can be considered as a special motion constraint based on virtual planes. The experimental results under various scenes illustrate the effectiveness of this work.

In this paper, authors propose a new learning based method for medical image retrieval which is based on fusing different features by linearly combining different similarities. Considering the abundant classes of medical images, this paper avoid to train a classifier for each class by using large amount training data. Instead, by using optimization method to combine different features’ similarity, new method can get good performance while has no much training computation. Experimental results show that the algorithm has potential practical values for clinical routine application.

The camera output RGB signals do not directly corresponded to the tristimulus values based on the CIE standard colorimetric observer, i.e., it is a device-independent color space. For achieving accurate color information, we need to do color characterization, which can be used to derive a transformation between camera RGB values and CIE XYZ values. In this paper we set up a Back-Propagation (BP) artificial neutral network to realize the mapping from camera RGB to CIE XYZ. We used the Munsell Book of Color with total number 1267 as color samples. Each patch of the Munsell Book of Color was recorded by camera, and the RGB values could be obtained. The Munsell Book of Color were taken in a light booth and the surround was kept dark. The viewing/illuminating geometry was 0/45 using D 65 illuminate. The lighting illuminating the reference target needs to be as uniform as possible. The BP network was a 5-layer one and (3-10-10-10-3), which was selected through our experiments. 1000 training samples were selected randomly from the 1267 samples, and the rest 267 samples were as the testing samples. Experimental results show that the mean color difference between the reproduced colors and target colors is 0.5 CIELAB color-difference unit, which was smaller than the biggest acceptable color difference 2 CIELAB color-difference unit. The results satisfy some applications for the more accurate color measurements, such as medical diagnostics, cosmetics production, the color reappearance of different media, etc.

Purpose : In this paper the chromatic contrast was defined as color difference CIEDE2000, the sensitivity was defined as reciprocal of threshold of the color difference, and the CSFs called color difference sensitivity functions were measured. Methods : The CSFs of 4 subjects were measured for nine spatial frequencies(0.28, 0.56, 1.00, 1.97, 2.95, 4.72, 6.74, 11.80 and 15.74cpd) gratings of mean luminance of 40cd/m 2 on a CRT display. Measurements were made for gratings whose average color was a chromatically neutral point(a*=0 and b*=0) and also for modulations around four chromatic points along the color directions a* and b* in the CIELAB color space. Results. The thresholds of color difference are from 0.74 to 6.67 in the range of experimental frequencies. The color difference sensitivity functions are similar with known results that CSF curves for the two chromatic directions are consistently low-pass irrespective of the average color of the stimulus. The sensitivity to gratings for b* direction is identical as for a* direction below spatial frequency 4.72cpd, however, the sensitivity for b* direction is smaller than the one for a* direction above spatial frequency 4.72cpd, which indicates that the CIEDE2000 threshold for grating with lower frequencies(i.e., small color differences) is not related to the chromatic direction and chromatic point of modulation, however, the threshold is related to the chromatic direction and chromatic point of modulation for large color differences.

As new method of characterizing CRT monitors is proposed. The features of this method are, it can take account of some color appearance factors, such as the appearance difference between self-luminous and surface color, but without the complexity of using any color appearance model, and it may improve the performances of an interpolation operation when an arbitrary assigned color is to be displayed on a CRT screen. The method is introduced more detailedly in Section 2, and preliminary experimental results are given in Section 3.

It is necessary for a modern spectrophotometry system to possess such properties as high measuring accuracy, fast working speed, uniform spectral response in the whole wavelength band, low consumption of power, little heat accruing while working, small size and low weight, etc. To meet all these needs, a spectrophotometry system today usually employs the so-called dual-beam channel configuration in which one sampling channel with another compensatory channel and a cold light source such as a xenon flash lamp, which usually causes an increase of the complexity in the system configurations. To further simplify the instrument configuration, we put forward a new dual-channel compensatory model called the integral compensatory model. Our experimental results show that using this new model, a spectrophotometry system cannot only be greatly simplified in its hardware but can also achieve highly accurate measurements.