Grey Degree Research Articles

Detection of Ovarian Tumors Using TVUS Imaging and Segmentation Techniques

Ovarian most cancers remains a giant health subject due to its regularly asymptomatic development until superior stages, where treatment options are confined. Early detection is critical for enhancing affected person outcomes and survival costs. This observe proposes a comprehensive technique leveraging superior photograph processing techniques for the early detection and category of ovarian tumors the usage of trans vaginal ultrasound (TVUS) snap shots. The method starts with preprocessing steps which include median filtering to do away with noise artifacts inclusive of salt and pepper noise from ultrasound photos. Ultimately, picture enhancement techniques are applied to enhance contrast and intensity, observed through binary sample evaluation to signify texture features vital for tumor identification. Photo segmentation is performed the use of okay- manner clustering and gray degree Co-prevalence Matrix (GLCM) analysis to isolate and analyze areas of interest within the ovarian photos. A Convolutional Neural community (CNN) is then hired for correct classification of ovarian tumors as regular, benign, or malignant primarily based on the extracted functions. This approach not handiest enhances the diagnostic accuracy but also minimizes affected person publicity to ionizing radiation, making it appropriate for ordinary medical applications. The proposed methodology represents a considerable advancement within the discipline of ovarian cancer detection, presenting a non-invasive and green way to evaluate ovarian health and facilitate timely intervention. Destiny research directions include refining the set of rules’s performance with large datasets and exploring additional imaging modalities for complete ovarian fitness evaluation.

Risk assessment in lithium-ion battery circular economy in sustainable supply chain in automotive industry using gray degree of possibility in game theory and MCDM

Risk assessment in lithium-ion battery circular economy in sustainable supply chain in automotive industry using gray degree of possibility in game theory and MCDM

Carryover slag weight prediction under converter tapping process by applying attention-shifting infrared thermal mapping technique

The prediction model of actual slag weight under converter tapping process was constructed by applying attention-shifting infrared thermal mapping technique. Firstly, the linkage between programmable logic controller signal of the converter mechanical structure and the infrared thermal mapping detector image was realized to ensure the real-time monitoring of the whole tapping process. Images should be preprocessed to ensure a good distinction between the flowing liquid and background at the tapping beginning, furthermore to separated slag from liquid steel at the following process. Moreover, the use of attention-shifting block belonging to the infrared thermal detector was effect to obtain more accurate image distinction between the liquid steel and slag. Hence the gray degree threshold value was adjusted to 220–225 in the early stage, and the value was needed to be lowered to 170–176 in the end. After using the model, the slag weight was less than < 8.0kg/t when only slag-stopping awl was adopted in tapping process. The average weight of slag in converter was 7.3kg/t by using slag-stopping awl and sliding plate slag-blocking.

Anti-LSSDS pharmacological components identification of YuHuangLian based on the combination of spectrum-effect analysis and network pharmacology as well as molecular docking.

This research aimed to investigate the pharmacological components for liver stagnation and spleen deficiency syndrome (LSSDS) of Evodia rutaecarpa (also called Yu HuangLian [YHL]) by exploring the spectrum-effect relationship between fingerprints and pharmacological actions. The fingerprints of 17 batches of YHL with different preparation conditions according to Box-Behnken Design were generated and analyzed to identify the common peaks by HPLC and FT-IR. Vasoactive intestinal peptide (vip), substance P, and 5-HT levels in colon sample were measured by ELISA. Gray degree correlation and orthogonal partial least squares were employed to explore the correlation degree between components and pharmacologic activity. The presumed pharmacological components were further confirmed by network pharmacology, molecular docking, and qRT-PCR. The columbamine, jatrorrhizine, coptisine, berberine, rutecarpine, and evodiamine of the 14 common peaks in HPLC fingerprints were significantly correlated with the pharmacological indexes. Similarly, there was a strong correlation with -OH, δNC-H, and νC-O-C of the 10 common peaks in FT-IR fingerprints. PTGS2 and CHRM3 were the main targets intervening LSSDS, and the presumed pharmacological components could markedly increase the expression of CHRM3 and obviously reduce the expression of PTGS2 compared with the model group.

Research into the Structure and Adhesion of WCCoCr Coatings Plasma-Sprayed onto Castings of AlSi Alloy Plates

The article presents structural investigations and mechanical properties of hard coatings deposited by spraying WCCoCr powder in an argon-hydrogen plasma jet onto the surfaces of AlSi10Mg alloy casting plates. Two variants (A and B) of processing parameters of the powder spraying process onto the surface of silumin plates were applied, resulting in different coating thickness. The coating applied according to variant A was done with 12 passes, and its thickness was approximately 150 μm. The coating applied according to variant B was done with 20 passes, and its thickness was about 320 μm. The microstructures of these coatings are similar, consisting of wavy, alternately deposited phases of solid solutions with varying concentrations of elements, and fine spherical phases, irregularly dispersed carbides. A qualitative analysis of the distribution of microstructure components was performed based on surface mapping. Precipitates differing in their degree of grayness and shape were identified based on microanalysis of their chemical composition. The porosity assessment of coatings performed in five randomly selected areas amounts to an average of 9%. The applied coatings exhibit good adhesion to the substrate, as evidenced by the absence of delamination during scratching tests using a diamond Rockwell indenter loaded with a force of 10 N. The coating hardness averaged 1180HV0.2. The test results indicate the high quality of the WCCoCr coatings, regardless of their thickness.

Detection of Throat Disorders Based on Thermal Image Using Digital Image Processing Methods

Throat disorders are often considered trivial for some people, but if they are not treated immediately they can result in more severe conditions and require a longer time to cure this disorder. Objective, safe and comfortable detection of throat disorders is important because throat disorders are an indication of inflammation which, if not treated immediately, can have negative consequences. This research aims to detect throat disorders based on thermal images using digital image processing methods. Image capture was carried out with the same color pallete range on the camera, namely 33°C-38°C. The image obtained is then cropped in the ROI, then the image is threshold with a gray degree of 190. Pixels that have a gray degree above 190 are converted to white, while those below the threshold are converted to black. Next, the percentage of each white and black area is calculated compared to the total ROI area. If the percentage of white area is greater than 38% compared to the area of ​​the throat then it is identified as having a throat disorder, whereas if the percentage of white is less than 38% then it is identified as not having a throat disorder. The detection program created provides an accuracy of 87.5% on sample data of 8 test data.

Analysis of Histogram and Grayscale on Chest X-Ray in Lung Cancer Using Image-J

Posteranterior (PA) chest radiographic examination is a support in screening for the diagnosis of lung cancer. Computed radiography (CR) modality can produce chest images quickly, optimally and can be processed as needed. However, so far radiologists interpret images only by visual assessment, so the results are very subjective. Therefore digital medical image processing can be done by looking at the histogram and gray scale values to increase the accuracy of enforcing patient diagnoses. This study aims to analyze the comparison of histograms and gray degree values on CR chest images between normal patients and lung cancer patients. The study was conducted using 30 chest images consisting of normal and lung cancer patient groups with 15 images each. All images are calculated grayscale and display histogram graphics with the Image-J application and statistically analyzed using the Independent T-Test. The results show that there is a difference in grayscale values between normal chest images and lung cancer (p<0.001). The grayscale and histogram values on lung cancer chest images ( 103.2908 + 6.119 ) are higher and tend to the right compared to the grayscale and histogram values on normal chest images ( 64.5848 + 3.28) . Histogram and grayscale values add objective image interpretation in diagnosing lung cancer.

A Novel Color Image Encryption Algorithm Using Coupled Map Lattice with Polymorphic Mapping

Some typical security algorithms such as SHA, MD4, MD5, etc. have been cracked in recent years. However, these algorithms have some shortcomings. Therefore, the traditional one-dimensional-mapping coupled lattice is improved by using the idea of polymorphism in this paper, and a polymorphic mapping–coupled map lattice with information entropy is developed for encrypting color images. Firstly, we extend a diffusion matrix with the original 4 × 4 matrix into an n × n matrix. Then, the Huffman idea is employed to propose a new pixel-level substitution method, which is applied to replace the grey degree value. We employ the idea of polymorphism and select f(x) in the spatiotemporal chaotic system. The pseudo-random sequence is more diversified and the sequence is homogenized. Finally, three plaintext color images of 256×256×3, “Lena”, “Peppers” and “Mandrill”, are selected in order to prove the effectiveness of the proposed algorithm. The experimental results show that the proposed algorithm has a large key space, better sensitivity to keys and plaintext images, and a better encryption effect.

Interval-valued grey (hyper)group

In this research, we apply the notations of the kernel and relative measure of an interval-valued grey to introduce grey groups (groups are based on interval-valued grey) and grey hypergroups (hypergroups are based on interval-valued grey). The primary method used in this research is based on linear inequalities related to elements of grey (hyper)groups and (hyper)groups. It found a relation between grey hypergroups and grey groups via the fundamental relation and proves that the identity element of any given group plays a main role in the grey groups and show that its measure is greater than or equal to its degree of greyness and less than or equal to its kernel, respectively. We show that any given grey group is a generalization of a group and analyze that interval-valued grey groups are different from the interval-valued fuzzy group.

The Application of Image Processing Technology in Camera Picture.

The application scene of camera pictures in our life is very huge, and the effect of information conveyed by images is more intense than words and language, so it is very important for us to make good use of pictures obtained by photography or even pictures obtained by other means. Aiming at the processing problem of camera pictures, we have adopted image scaling method, camera picture color change method, gray degree processing method, image color space change method, image brightness processing method, and other methods to solve the processing problems of camera pictures. After reasonable processing, the presentation effect of camera pictures will be greatly improved, and the utilization efficiency will be greatly improved.

A Grey Incidence Based Group Decision-Making Approach and Its Application

We define the group measure matrix of the alternative scheme and the ideal scheme based on the relevant factor sequence and the system characteristic behavior sequence. Furthermore, the information distances of decision makers and decision criteria are defined, respectively. According to the information distance, we obtain each scheme’s grey matrix incidence degree for the scheme ranking. Finally, we use an example to verify the rationality of the model and compare it with other classic methods, such as TOPSIS, VIKOR, MULTI-MOORA. Compared with previous grey incidence analysis model, the proposed model can make full use of information of the decision-maker dimension and the criteria dimension. The proposed model can avoid high-dimensional information loss. The results show that the proposed method has superiority in measuring decision-maker information and decision-making standard information.

A grey Bayesian inference framework for structural damage assessment

Real-life civil structures often stay in a grey situation due to lack of knowledge and measurements. In a poor information condition, classic Bayesian inference is practically difficult to perform owing to intractable likelihood functions. Under such circumstance, a structure should be defined as a grey system. Thereby, a grey Bayesian inference framework has been proposed by incorporating the grey theory with approximate Bayesian computation for damage assessment purposes. Structural parameters are represented by interval grey variables, whose grey kernels and degrees of greyness are used to define probability distributions for sampling. A grey population Monte Carlo sampler has also been developed by introducing interval grey numbers and an evolutionary particle pool into the particle filtering process. A novel particle interchange mechanism is simultaneously proposed for effective particle interflow. Meanwhile, stochastic response surfaces are used to correlate structural parameters with responses for fast response computation. Also, likelihood calculation is replaced with a distance measure between simulated and measured samples. Lastly, the proposed inference strategy has been verified against both the numerical and experimental beams having the different damage scenarios. The damage locations and severities were successfully identified by the changes in the grey intervals and kernels.

Salt frost resistance and micro characteristics of polynary blended concrete using in frost areas

To explore the deterioration process and improvement mechanism concrete blended with supplementary cementitious materials (fly ash, slag, and silica fume) in frozen areas, the SCM content, w/b ratio, curing age, freeze-thaw (F-T) cycle, and salt-frost (S-F) cycle were varied to systematically evaluate the chloride permeability resistance and S-F resistance. Pore structures, hydration degree, phase composition and interfacial transition zone (ITZ) between aggregate and matrix of investigated mixtures were characterized by mercury intrusion porosimetry (MIP), thermogravimetric analysis (TGA), X-ray diffraction (XRD) and SEM-EDS, respectively. The S-F resistance of blended concrete is greater than that of control mixture without SCMs. The crystallization pressure is dominated by larger saturation with higher salt concentration at the early stage of S-F cycles, and greatly affected by the actual volume expansion of salt solutions at its later stage. SCMs can compensates the structure deterioration under S-F and F-T conditions, where quaternary blends shows an optimal refinement for the pore structures. It is found that the total pore volume, average pore size, and porosity are highly correlated with the chloride permeability resistance of concrete by grey degree analysis. The SCMs blends can improve cement hydration degree by pozzolanic and hydraulic reactions at the later curing age. With the S-F cycles, the internal stresses could cause pore nucleation and crack propagation, while SCMs can effectively control the ITZ width and mitigate the structural deterioration of concrete.

Grey variable dual precision rough set model and its application

PurposeThe purpose of this paper is to advance a novel grey variable dual precision rough set model for grey concept.Design/methodology/approachTo obtain the approximation of a grey object, the authors first define the concepts of grey rough membership degree and grey degree of approximation on the basic thinking logic of variable precision rough set. Based on grey rough membership degree and grey degree of approximation, the authors proposed a grey variable dual precision rough set model. It uses a clear knowledge concept to approximate a grey concept, and the output result is also a clear concept.FindingsThe result demonstrates that the proposed model may be closer to the actual decision-making situation, can effectively improve the rationality and scientificity of the approximation and reduce the risk of decision-making. It can effectively achieve the whitenization of grey objects. The model can be degenerated to traditional variable precision rough fuzzy set model, variable precision rough set model and classic Pawlak rough set, when some specific conditions are met.Practical implicationsThe method exposed in the paper can be used to solve multi-criteria decision problems with grey decision objects and provide a decision rule. It can also help us better realize knowledge discovery and attribute reduction. It can effectively achieve the whitenization of grey object.Originality/valueThis method proposed in this paper implements a rough approximation of grey decision object and obtains low-risk probabilistic decision rule. It can effectively achieve a certain degree of whitenization of some grey objects.

Implementasi Algoritma Haar Cascade pada Aplikasi Pengenalan Wajah

The large number of citizens in an organization makes the development of an attendance system or citizen detection in a place important in the running of work activities in the organization. Utilization of an IP Camera which is only used for regular monitoring without further detection of the needs of citizens in the organization made the development of personnel detection developed for monitoring the presence of personnel. With the development of a face detection system, it is hoped that the facial algorithm development system will be developed using an IP Camera. Face detection has been developed which has many and special features which aim to determine whether or not a face has been detected in an image. With image management that is developed in face detection, detection will be faster and more accurate because the color is processed into gray degrees so that there are fewer color pixels than those with colors. By using the Python programming language and an image detection library called OpenCV, less code will be designed. This study uses the Viola Jones method, which is a fast and accurate face detection method developed by Paul Viola and Michael Jones. In this study, the Viola Jones method uses the Haar Cascade algorithm which functions as a detection feature in the system and is combined with the internal image process and the AdaBoost Learning and Cascade Classifier so that the detected face object will easily classify whether the object is a face or not. In this case the Cascade Classfier used in this study is the face and eyes. The development of this algorithm is carried out for face detection and recognition. The detection is done by taking pictures with the process taken using a webcam. The system will take several pictures and then the image data will be stored in a folder called dataSet. After that, all data is trained so that it can be recognized by the system. With retrieval, detection and recognition limitations that can only be taken from a distance of less than three meters, face detection on the IP Camera can still read objects other than faces. With recognition and accuracy on the webcam camera, about 80,5% this system can be developed with the Haar Cascade algorithm and the Haar Cascade algorithm precisely to be applied to the development of faced detection and face recognition. By developing the Haar Cascade algorithm for face detection, problems and utilization of an organization's data can be more easily detected and used by IP cameras that can support the performance process of face detection and recognition

G-GERT network model of online public opinion reversal based on kernel and grey degree

PurposeIn recent years, online public opinion reversal incidents have been occurring frequently, which has increased the complexity of the evolution of online public opinion, and they have become a difficult issue for public opinion management and control. It is of great significance to explore the regularity of online public opinion reversal.Design/methodology/approachCombined with the grey characteristics of online public opinion information, a grey graphical evaluation review technique (G-GERT) network model is constructed based on kernel and grey degree, and the frequency, probability and time of online public opinion reversal nodes are calculated using C-marking method and Z-marking method.FindingsThroughout the online public opinion reversal events, there are all repeated outbreak nodes occurring, so the authors regard the repeated occurrence of outbreak nodes as reversal. According to the average frequency, probability and time of repeated outbreak nodes in the G-GERT network model, the authors predict the corresponding key information of reversal. It can simulate the evolution process of public opinion events accurately.Originality/valueThe G-GERT network model based on kernel and grey degree reveals the regulation of public opinion reversal, predicts the frequency, probability and time of reversal nodes, which are the most concerned and difficult issues for decision-makers. The model provides the decision basis and reference for government decision-making departments.

Designing Closed-Loop Supply Chain Network for High-Salvage Perishable Products Under Uncertain Environment

High-salvage perishable products have the characteristics of a short life cycle and high residual value. To extract the maximum value of this kind of product, a closed-loop supply chain network model based on the product life cycle is constructed under various uncertainties. This model incorporates various assumptions such as multiple products, multiple periods, timeliness of recycling, and four-echelon supply chain including manufacturers, wholesalers, recycling centers, and consumer markets. Where the recovery cost and the reuse rate are denoted by interval numbers, the demand and the capacity are denoted by stochastic and fuzzy numbers respectively. A Two-step interactive method based on fuzzy flexible programming is used to handle the uncertainties. The optimum solutions in the harsh environment and favorable environment are obtained by a numerical example, and the results show the proposed method is more accurate in grey degree and more robust in site selection than the interval chance-constraint mixed-integer nonlinear programming method. Besides, we demonstrate the relationships between the sensitivity coefficients and the profits. i.e., the higher the probability of stockout in the secondary market, the harsher the external environment is, the stockout will get increasing, and the corresponding expected profit will get lower; inversely, the higher the degree of satisfaction in the e-commerce market, the more favorable the external environment is, the stockout will get decreasing, and the corresponding expected profit will get higher. Furthermore, the numerical simulation also shows that the price of recycled products fluctuates with the increase of the probability of no stockout, where the price fluctuation in the secondary market and the e-commerce market are fundamentally the same, while the price of parts fluctuates least. Finally, according to the results of the sensitivity analysis, some managerial insights are given in the conclusion.

Forecasting smog in Beijing using a novel time-lag GM(1,N) model based on interval grey number sequences

PurposeSmog seriously affects the ecological environment and poses a threat to public health. Therefore, smog control has become a key task in China, which requires reliable prediction.Design/methodology/approachThis paper establishes a novel time-lag GM(1,N) model based on interval grey number sequences. Firstly, calculating kernel and degree of greyness of the interval grey number sequence respectively. Then, establishing the time-lag GM(1,N) model of kernel and degree of greyness sequences respectively to obtain their values after determining the time-lag parameters of two models. Finally, the upper and lower bounds of interval grey number sequences are obtained by restoring the values of kernel and degree of greyness.FindingsIn order to verify the validity and practicability of the model, the monthly concentrations of PM2.5, SO2 and NO2 in Beijing during August 2017 to September 2018 are selected to establish the time-lag GM(1,3) model for kernel and degree of greyness sequences respectively. Compared with three existing models, the proposed model in this paper has better simulation accuracy. Therefore, the novel model is applied to forecast monthly PM2.5 concentration for October to December 2018 in Beijing and provides a reference basis for the government to formulate smog control policies.Practical implicationsThe proposed model can simulate and forecast system characteristic data with the time-lag effect more accurately, which shows that the time-lag GM(1,N) model proposed in this paper is practical and effective.Originality/valueBased on interval grey number sequences, the traditional GM(1,N) model neglects the time-lag effect of driving terms, hence this paper introduces the time-lag parameters into driving terms of the traditional GM(1,N) model and proposes a novel time-lag GM(1,N) model.

Analysis and Prediction of Influencing Parameters on the Coal Classification Performance of a Novel Three Products Hydrocyclone Screen (TPHS) Based on Grey System Theory

A novel hydrocyclone including a cylindrical screen embedded in a conventional hydrocyclone (CH), named three products hydrocyclone screen (TPHS), has been successfully designed. In TPHS, the combination of centrifugal classification and screening was employed to separate particles. In this paper, Grey theory, as an effective means to the laws of both complex and uncertainty system’s behavior with small samples, was used to investigate the operational (feed concentration and feed pressure) and structural (aperture size, spigot diameter, and vortex finder diameter) parameters on performance evaluation Hancock classification efficiency (HE), imperfection (I), and cut size (d50c). The experiments of coal sample (0–1 mm) show that TPHS with coarser particles in underflow exhibited the absent “fish-hook”. The closeness calculated using the Grey System algorithm indicates that the performance of TPHS was closely related to the operation and structure parameters. Further, the order of grey incidence degree between different parameters and HE (or I or d50c) is the spigot diameter and aperture size with the highest value, the feed pressure and vortex finder diameter with the middle value, and the feed concentration with the lowest value. The prediction using the GM(1, N) algorithm implies that the dynamic prediction model for the performance evaluation can be created depending on the operation, structure and previous performance value. The mean relative errors between the predicted and actual HE, I, and d50 were 2.84%, 5.83%, and 3.57%, respectively, which exhibit the accurate prediction.

A new approach to the degree of greyness

PurposeThe degree of greyness may be regarded as a measure of cognitive uncertainty. Therefore, it is a part of the epistemological core of the grey systems theory. The theoretical importance of the degree of greyness concept is also due to its application in a range of uncertainty modelling methods: predictive, relational and decision-making methods. Greyness, being a result of cognitive uncertainty, was recently subjected to axiomatization in the form of grey space with the use of the classical sets theory. The purpose of this article is to develop a new approach to the degree of greyness, being consistent with the grey space concept.Design/methodology/approachIn order to realise the article’s goals, the research is divided into three stages described in particular sections. The first section of the article presents a theoretical framework of the degree of greyness and the grey space. The second part includes the assumptions of the new degree of greyness concept, along with the mathematical models for the first, the second and the third degree of greyness. The third section contains numerical examples for each degree of greyness.FindingsAs a result of the research, a concept of a degree of greyness was created and it was linked with a concept of grey space. This new approach to the issue of the degree of greyness has allowed the analysing of this category in three dimensions dependent on an accepted reference base. As a result, a concept of concrete and abstractive grey numbers was introduced and relationships between these categories of numbers and the degree of greyness were determined.Originality/valueThe proposed approach to the issue of the degree of greyness is a theoretical unification of the previous considerations in this area. The proposed three dimensions of greyness degree will be derived from the grey space, so they will also be a function of quantity. Thus, the degree of greyness was linked with a classical set theory. An original input in this article is also a differentiation of concrete and abstractive grey numbers, which give a basis for deliberations connected with interpretation of grey numbers in the context of real applications.