Threshold Determination Method Research Articles

EPFL-DAC: Enhancing Privacy in Federated Learning with Dynamic Aggregation and Clipping

Federated learning achieves data privacy protection by uploading only users’ local updates. However, recent research has shown that privacy data can still be inferred from local updates. Privacy-preserving federated learning also faces challenges related to the confidentiality and privacy of local updates, the robustness of dynamic membership schemes, and the determination of appropriate clipping threshold in differentially private federated learning. To address these issues, this paper presents the EPFL-DAC scheme: Enhancing Privacy in Federated Learning with Dynamic Aggregation and Clipping. Firstly, Paillier homomorphic encryption is employed to securely aggregate local updates, ensuring that the server can only access the aggregated information. Secondly, a dynamic robustness algorithm is designed to ensure the robustness of the aggregation scheme when user states change. Lastly, a dynamic threshold determination method is introduced to introduce more accurate noise, while also providing resistance against collusive attacks in differentially private settings. Experimental results demonstrate the effectiveness, low computational overhead, and preservation of accuracy in the dynamic aggregation scheme. Additionally, the proposed dynamic threshold determination method exhibits good performance, especially at higher privacy protection levels and larger initial clipping threshold values.

Local ischaemia morphology in diabetics differs from healthy controls

Aims/Purpose: To evaluate local macular ischaemia morphology additional to global metrics in diabetic macular ischaemia (DMI) patients and healthy controls.Methods: The study was registered: DRKS‐ID DRKS00024399. Informed written consent was obtained from all participants. Seven DMI patients and seven patients after cataract surgery were included as healthy controls. 10 × 10° Heidelberg Spectralis OCT Angiography was recorded and divided to superficial/deep vascular complexes (SVC/DVC) following the inbuilt segmentation. Further processing was done in MATLAB R2022a. After binarization following Otsu's method for threshold determination, minimum distances to blood vessels were calculated for each pixel applying a sliding window. More than 30 μm distance to the next vessel was considered ischaemic. Morphology analysis of the resulting ischaemic lesions was conducted for SVC and DVC separately. The total ischaemic area; mean ischaemic area per lesion; area and circularity of the second largest ischaemic lesion—avoiding the foveal avascular zone—was extracted. Two‐sample Student t‐tests were performed testing differences of healthy controls and DMI patients in SVC and DVC for each of the four named metrics. Statistical significance was defined when p < 0.05.Results: Analysing total ischaemic area, statistically significant differences were found for SVC and DVC data. Mean ischaemic area per lesion was significantly larger in DMI patients in SVC but not in DVC. The area of the largest ischaemic lesion avoiding FAZ was significantly larger in DMI patients in both SVC and DVC. Lesion circularity did not reveal significant differences.Conclusions: Supplementing reported global nonperfusion differences, local nonperfusion morphology differs significantly between DMI patients and healthy controls. Findings need to be replicated in a larger trial in the future. Research on structure function correlation in areas of nonperfusion will shed light on clinical implications.

Research on the Threshold Determination Method of the Duffing Chaotic System Based on Improved Permutation Entropy and Poincaré Mapping.

The transition from a chaotic to a periodic state in the Duffing chaotic oscillator detection system is crucial in detecting weak signals. However, accurately determining the critical threshold for this transition remains a challenging problem. Traditional methods such as Melnikov theory, the Poincaré section quantitative discrimination method, and experimental analyses based on phase diagram segmentation have limitations in accuracy and efficiency. In addition, they require large computational data and complex algorithms while having slow convergence. Improved permutation entropy incorporates signal amplitude information on the basis of permutation entropy and has better noise resistance. According to the characteristics of improved permutation entropy, a threshold determination method for the Duffing chaotic oscillator detection system based on improved permutation entropy (IPE) and Poincaré mapping (PM) is proposed. This new metric is called Poincaré mapping improved permutation entropy (PMIPE). The simulation results and the verification results of real underwater acoustic signals indicate that our proposed method outperforms traditional methods in terms of accuracy, simplicity, and stability.

Rapid evaluation of the high-speed railway bridge vertical deformation threshold and train operation safety performance based on the virtual track inspection method

This study proposes a method for analyzing the impact of bridge deformation on the train-track-bridge coupled dynamics from the perspective of dynamic track geometry. First, with the static mapping geometry and the superposition of random track irregularities are used as excitations, a mapping analytical model between bridge foundation deformation and dynamic track geometry is established by combining the vehicle-track-bridge dynamics model and the virtual track inspection model. Second, after analyzing the effect of bridge foundation deformation on the ratio of dynamic and static extremums of the second derivative, the mapping relationship among the amplitude of the bridge deformation, the second derivative of the dynamic track geometry and the vehicle dynamic response is obtained. Finally, based on the driving safety indicators, a threshold determination method applicable to different bridge deformation patterns is proposed. This method, when combined with modern track detection technology, allows for rapid evaluation and is adaptable to complex bridge deformation scenarios.

Filtering Green Vegetation Out from Colored Point Clouds of Rocky Terrains Based on Various Vegetation Indices: Comparison of Simple Statistical Methods, Support Vector Machine, and Neural Network

Filtering out vegetation from a point cloud based on color is only rarely used, largely due to the lack of knowledge of the suitability of input information (color, vegetation indices) and the thresholding methods. We have evaluated multiple vegetation indices (ExG, ExR, ExB, ExGr, GRVI, MGRVI, RGBVI, IKAW, VARI, CIVE, GLI, and VEG) and combined them with 10 methods of threshold determination based on training set selection (including machine learning methods) and the renowned Otsu’s method. All these combinations were applied to four clouds representing vegetated rocky terrain, and the results were compared. The ExG and GLI indices were generally the most suitable for this purpose, with the best F-scores of 97.7 and 95.4, respectively, and the best-balanced accuracies for the same combination of the method/vegetation index of 98.9 and 98.3%, respectively. Surprisingly, these best results were achieved using the simplest method of threshold determination, considering only a single class (vegetation) with a normal distribution. This algorithm outperformed all other methods, including those based on a support vector machine and a deep neural network. Thanks to its simplicity and ease of use (only several patches representing vegetation must be manually selected as a training set), this method can be recommended for vegetation removal from rocky and anthropogenic surfaces.

Power Supply Coal Consumption Prediction Model Based on Long and Short-term Memory Neural Network

As a measure of the overall economic performance of thermal power plants and an important component of the variable cost of power generation, the prediction of coal consumption of power supply is of great significance for the bidding decision of power generation companies. In this paper, we propose an improved long and short-term memory (LSTM) model for calculating the coal consumption of thermal power units, using a large amount of data stored in the plant monitoring information system (SIS). Firstly, the data are pre-processed using the threshold determination method to filter out the data of stable operating conditions. Secondly, the maximum correlation minimum redundancy (mRMR) algorithm is used to determine the optimal set of special features. The results show that the prediction effect of the improved LSTM-based power supply coal consumption calculation model proposed in this paper is better than other models, and the calculation accuracy is higher, which is suitable for power supply coal consumption calculation.

Threshold Determination Method for Detecting the Gazing Areas on a PC Screen While Silent Reading Japanese Texts

This study aims to develop a method that can detect the areas of Japanese texts that are difficult to read on a PC screen by analyzing eye movements. An effective method for detecting the areas of text that are difficult to read is to create a heatmap and consider the areas where the eye stays for longer than a fixation duration threshold (threshold). Usually, the threshold is determined through a 50 ms step search. However, since the eye stay time (fixation duration) for difficult‐to‐read text is longer than the fixation duration when reading smoothly, it is expected that steps that last longer than 50 ms are more effective at detecting the threshold. Furthermore, there are individual differences in the speed at which we comprehend text. In this study, we proposed a method that considers both the efficient search and individual differences among each subject. The experimental results showed that the threshold determined by the proposed method is more useful than the thresholds of 1000 and 800 ms that were used in a previous study, as well as the usual fixation duration of 250 ms. © 2023 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

Three-dimensional reconstruction for highly reflective diffuse object based on online measurement

The three-dimensional (3D) reconstruction for highly reflective diffuse object has always been challenged in fringe projection profilometry. So a 3D reconstruction method for highly reflective diffuse object based on online measurement is proposed. Only a fixed grating needs to be projected onto the highly reflective diffuse object. While this object is moving online across the projection area, partial highly reflective area (PHRA) may be changed in accordance. As long as PHRAs in the two online captured deformed patterns do not overlap, the profiles without PHRAs of the object at these two positions can be well reconstructed respectively by single-shot computer-generated moiré profilometry. Then a grayscale histogram threshold determination method is proposed to automatically and accurately locate these PHRAs, and the 3D profiles in these PHRAs can be made up by using feature match and effective point cloud non-rigid registration and fusion. So the complete profile of the object can be successfully reconstructed. Experimental results show the feasibility and effectiveness of the proposed method. Compared with the traditional 3D measuring methods for highly reflective diffuse object, the proposed method has simpler structural light control and higher accuracy.

Determination of Monitoring Control Value for Concrete Gravity Dam Spatial Deformation Based on POT Model

Deformation can directly reflect the working behavior of the dam, so determining the deformation monitoring control value can effectively monitor the safety of dam operation. The traditional dam deformation monitoring control value only considers the single measuring point. In order to overcome the limitation, this paper presents a new method to determine the monitoring control value for concrete gravity dam based on the deformations of multi-measuring points. A dam’s comprehensive deformation displacement is determined by the measured values at different measuring points on the positive inverted vertical line and the corresponding weight of each measuring point. The projection pursuit method (PPM) combined with the grey wolf optimization (GWO) algorithm is used to determine the weight of each measuring point according to the spatial correlation distribution characteristics of dam deformation. The peaks over threshold (POT) model based on the extreme value theory is adopted to determine the monitoring control value with the obtained dam comprehensive deformation displacement. In addition, the POT model is improved with the automatic threshold determination method based on the criterion in probability theory and the GWO algorithm, which can avoid subjectivity and randomness in determining the threshold. The results of the engineering application show the feasibility and applicability of the proposed method.

A matched-filter technique with an objective threshold

We propose an objective threshold determination method for detecting outliers from the empirical distribution of cross-correlation coefficients among seismic waveforms. This method is aimed at detecting seismic signals from continuous waveform records. In our framework, detectability is automatically determined from Akaike’s Information Criterion (AIC). We applied the method of seismic signal detection to continuous records collected over two years. The results show that the maximum value of network cross-correlation coefficients sampled from each constant interval can be approximated by the theory of extreme value statistics, which provides a parametric probability density function of maxima. By using the function, outliers can be considered with a reasonable criterion.

Respiratory Threshold as A New Threshold Determination Method based on Respiratory Responses and It’s Success to Indicate Critical Power

Yeni bir solunumsal eşik türü olarak ortaya atılan respirasyon eşiği (RE) kademeli bir rampa testinde dakika ventilasyonu bölü ekspirasyon sonu CO2 kısmi basıncı (VE/PETCO2) değerindeki zamana bağlı değişimlerin analizine dayanır. Ancak RE tekniğinin yaygın olarak kullanılan önemli bir eşik belirleme yöntemi olan kritik gücü (KG) hangi düzeyde işaret edebildiğine odaklanan bir araştırma bulgusu rapor edilmemiştir. Bu çalışmanın amacı, RE tekniği kullanılarak elde edilen egzersiz şiddetinin, KG’yi hangi düzeyde karşılayabildiğini araştırmaktır. Çalışmaya iyi antrene on erkek bisiklet sporcusu katılmıştır. Sporcuların gaz değişim eşiği (GDE), solunumsal kompanzasyon noktası (SKN) ve RE düzeyleri kademeli rampa testleriyle belirlenmiştir. Bu testlerde GDE ve SKN düzeyleri, Innocor sistemi yoluyla VCO2-VO2 ve VE-VCO2 ilişkilerinde saptanan kırılmalar tespit edilerek değerlendirilmiştir. RE düzeyi SegReg paket programı kullanılarak VE/PETCO2-zaman ilişkisindeki en güçlü kırılma noktası tespit edilerek bulunmuştur. Sonrasında KG’yi hesaplamak için farklı günlerde sabit iş oranlarında testler uygulanmıştır. Tüm eşik göstergelerinin birbirleriyle ilişkilerinin sınanması için geçerlik analizleri yapılmıştır. Bulgular, RE (328±35,5 W; 4,23±0,39 L·dk−1) ile SKN (324±34,3 W; 4,21±0,45 L·dk−1) güç çıktıları ve her birine ait VO2 yanıtları arasında yüksek bir ilişki ve uyum olduğunu göstermiştir (p>0,05; t= 1,19; r>0,96; % tahmini standart hata <5). Ancak hem RE hem de SKN güç çıktıları KG'den (298±32 W) yaklaşık %10 daha yüksek bulunmuştur (p<0,001). Çalışmamız, iyi antrene bisikletçilerde VE/PETCO2-zaman ilişkisinde oluşan en güçlü kırılmanın çok büyük bir başarı ile SKN’yi gösterdiğini, fakat KG’yi doğrudan belirlemede başarısız olduğunu ortaya koymuştur.

Identification and Localisation Algorithm for Sugarcane Stem Nodes by Combining YOLOv3 and Traditional Methods of Computer Vision.

Sugarcane stem node identification is the core technology required for the intelligence and mechanization of the sugarcane industry. However, detecting stem nodes quickly and accurately is still a significant challenge. In this paper, in order to solve this problem, a new algorithm combining YOLOv3 and traditional methods of computer vision is proposed, which can improve the identification rate during automated cutting. First, the input image is preprocessed, during which affine transformation is used to correct the posture of the sugarcane and a rotation matrix is established to obtain the region of interest of the sugarcane. Then, a dataset is built to train the YOLOv3 network model and the position of the stem nodes is initially determined using the YOLOv3 model. Finally, the position of the stem nodes is further located accurately. In this step, a new gradient operator is proposed to extract the edge of the image after YOLOv3 recognition. Then, a local threshold determination method is proposed, which is used to binarize the image after edge extraction. Finally, a localization algorithm for stem nodes is designed to accurately determine the number and location of the stem nodes. The experimental results show that the precision rate, recall rate, and harmonic mean of the stem node recognition algorithm in this paper are 99.68%, 100%, and 99.84%, respectively. Compared to the YOLOv3 network, the precision rate and the harmonic mean are improved by 2.28% and 1.13%, respectively. Compared to other methods introduced in this paper, this algorithm has the highest recognition rate.

Remote Sensing Monitoring of Changes in Lake Aquatic Vegetation before and after the Removal of the Fence Based on Sentinel-2: A Case Study in Lake Futou, Hubei Province

Aquatic vegetation is an important component and main primary producer of lake ecosystems and plays an important role in improving water quality and maintaining biodiversity, which is critical to diagnosing the health of aquatic ecosystems in shallow lakes. It is therefore important to accurately obtain information on dynamic changes and spatial-temporal distribution of aquatic vegetation. Based on the Sentinel-2 satellite remote sensing images from 2016–2022, we studied the feasibility of using remote sensing technology to monitor the spatial-temporal changes of aquatic vegetation before and after the removal of the fence, taking Futou Lake in Hubei Province as a case study. Two vegetation indices, Normalized Difference Vegetation Index (NDVI) and Submerged Aquatic Vegetation Index (SAVI) were applied to identify the open water and the aquatic vegetation through two threshold determination methods, Otsu algorithm and manual division threshold method. The results show that: (1) the classification based on the NDVI and manual division threshold method performs the best, with the overall classification accuracy of 94.44% and the Kappa coefficient of 85.23%. (2) The growth of aquatic vegetation is divided into stages, the first stage is enclosing culture, and the distribution of aquatic vegetation is less in 2016–2017, all around 10 km2. The second stage is after the removal of the fence, the distribution area of aquatic vegetation in 2018 is on an upward trend, and in 2019–2022 it is growing rapidly. (3) Spatially, the aquatic vegetation was mainly distributed at the former fence, specifically in the northeastern and southwestern waters of the Futou Lake and it spread to the core area of the lake, probably due to the elevation of the siltation of the lake bottom. (4) Potamogeton crispus and Trapa are the dominant species, the peak of the distribution range in Futou Lake occurs in 2021 with an area of about 50.89 km2, which needs to be controlled moderately. (5) The area covered by Potamogeton crispus in the Futou Lake has increased significantly, probably due to the siltation and accumulation of nutrients in the Futou Lake caused by the history of purse seine farming.

A Study on the Differentiation of Shariah Screening Criteria for Stocks

تُعدّ الأسهم الإسلاميّة إحدى الأدوات الماليّة الإسلاميّة المتداولة في أسواق رأس المال، وقد تمّ التأكيد على النظام الاقتصاديّ القائم على الشراكة في المراحل المبكرة من تاريخ الاقتصاد الإسلاميّ مع بدء مناقشة هذا الاقتصاد. على أنّ هيكلَ الشراكة وثقافتَها لا ينعكسان في المجال العمليّ بصورة متساوية. وقد ظَلَّ المساهمون بعيدين عن استثمارات الأسهم لمدّة طويلة لتعذّر إنشاءِ هيكل شركة يعمل بصفةٍ تتوافق والشريعة الإسلامية. وقد حالت دون تأسيس هذا الهيكل الشرعي للشركات جملةٌ من الشروط غيرِ المشروعة التي يطلبها النظام التقليديّ القائم. وقد طُرحت الآراء لقبول هيكل الشركات المساهمة الحالية في سبيل القضاء على تلك الصورة السلبيّة السائدة، ولتمكين تطوير رؤوس الأموال الإسلامية؛ وفي هذا السياق قرّر مجمع الفقه الإسلامي الدوليّ في عام 1992 أنّ مبدأ المسؤولية المحدودة جائز، وسمح بالاستثمار في الشركات المساهمة، وعليه فقد سُمح بالاستثمار في الشركات المساهمة في حالة استيفائها شروطًا شرعية معينة. ثم وُضعت مع قرار السماح هذا معاييرُ مختلفة للفرز الشرعي الذي يقام لتعيين علاقات الشركة مع المحتويات غير الشرعية، لاستثمارات الأسهم الإسلامية، تختلف باختلاف المؤسسات التي توَفِّر المؤشر، وهيكلِ المؤشرات، والدولِ التي تعمل فيها المؤشراتُ، وهيئةِ الرقابة الشرعيّة. في رأيي، على أنّ الاختلاف في المنهجية التي تستخدمها هيئة الرقابة الشرعيّة عند إصدار الأحكام يشكّل أهمَّ قضية مؤثّرة في هذا التنوع. تهدف هذه الدراسة إلى إظهار كيفية تأثير الاختلاف في المنهجية على تغيير المعايير الشرعية المستخدَمة في الفرز الشرعي. في هذه الدراسة، بحثنا في الممارسات الحالية وفقًا لتصنيف المنهجية الذي أنشأناه، وخلصنا إلى أن التغيير في المنهجية غيّر معايير الفرز الشرعي.

Garland chrysanthemum consumption ameliorates age-related hearing loss in C57BL/6 mouse; model system to explore hearing loss prevention foods in a short period.

Garland chrysanthemum (Glebionis coronaria L.) is an antioxidant-rich leafy vegetable. We found that garland chrysanthemum consumption ameliorated age-related hearing loss (AHL) in C57BL/6J mice, an early onset model. We also found that AHL progression was significantly ameliorated by three of ten products. Metabolome analysis of the 10 products using nuclear magnetic resonance (NMR) spectroscopy indicated that phytosterols may be involved in the amelioration of AHL. However, the direct inhibitory effect of phytosterol mixture on mouse AHL progression was not identified. These results suggest that garland chrysanthemum consumption delays AHL development in mice and its efficiency varies depending on the source of the product. Our findings also suggest that phytosterol content in garland chrysanthemum functions as an evaluation marker for the efficiency. Furthermore, to accelerate the search for foods that prevent AHL, we have used these data to develop an automatic threshold determination method for auditory brainstem response using machine learning.

An Integrated Algorithm for Extracting Terrain Feature-Point Clusters Based on DEM Data

Terrain feature points, such as the peaks and saddles, are the basic framework of surface topography and its undulations, which significantly affect the spatial distribution of surface topography. In the past, terrain feature points were extracted separately for each type, while the internal connections between the terrain feature points were ignored. Therefore, this work proposes an integrated algorithm for extracting terrain feature-point clusters, including the peaks, saddles and runoff nodes, based on the DEM data. This method includes two main processes: positive terrain-constrained ridgeline extraction and terrain feature-point cluster extraction. Firstly, a threshold determination method of flow accumulation in the hydrological analysis is proposed by combining morphological characteristics with runoff simulation, and the ridgelines are extracted based on this threshold. Subsequently, the peaks and their control areas are extracted by space segmentation. Meanwhile, the saddles and runoff nodes are obtained by spatial intersection. Finally, the integrated terrain feature-point clusters are obtained by merging the three extracted terrain feature points. This method was experimented with in the six typical sample areas in Shaanxi Province and verified its results by contour lines and optical images. It shows that the spatial positions of the extracted terrain feature clusters are accurate, and the coupling relationships are great. Finally, the experiments show that the statistical attributes of point clusters and their spatial distribution trends have an obvious correlation with geomorphic types and geomorphic zoning, which can provide an important reference for geomorphic zoning and mapping.

AN OPTIMIZED REMOTE SENSING RECOGNITION APPROACH FOR STRAW BURNING IN HENAN PROVINCE, CHINA

Abstract. In the paper, Henan Province with prevalent straw burning was taken as the study case. Aimed at remote sensing recognition of the straw burning fire points in a large-scale area with complex terrain and climate conditions, an optimized recognition strategy of straw burning was proposed based on the MOD021KM data. In our new recognition strategy, the threshold determination method was optimised by systematic statistical analysis of spectral features. Then the non-straw burning fire points were eliminated by extracting the agricultural land from MCD12Q1 image. Thus, the true straw-burning fire points were determined. The results showed that the recognition accuracy of straw burning fire points was effectively improved by optimized threshold determination method.

An improved quantification method for characterisation of clay microstructure using SEM

Clay is a material widely used in environmental geotechnics. Quantification of clay microstructure properties using scanning electron microscopy (SEM) images is intuitively the simplest way, but the challenges remain in extracting more credible quantified micro-parameters that can represent the holistic macroscopic properties of clay from SEM images and with higher efficiency. In this paper, an improved quantification method is proposed to deal with this issue. The influence of different milling and drying methods on quantification results was analysed to improve reliability and efficiency. The A–K threshold determination method (A–K method) was proposed to calculate the optimal threshold in image processing. Also, the interval estimation method was introduced to obtain the optimal magnification and number of images. Then, this quantification method was applied to Hangzhou clay. The results show that anisotropy in the clay microstructure should be considered when choosing the observed surface. Moreover, there is no significant difference in micro-parameters between liquid nitrogen frozen-vacuum drying and critical point drying. A comparison among the A–K method, artificial method and theoretical methods that can determine thresholds shows that the proposed A–K method combines the advantages of all methods. The optimal magnification and number of images can be determined by mathematical theory, which also improves reliability and efficiency.

A critical barrier analysis framework to the development of rural distributed PV in China

Rural distributed photovoltaic (PV) is conducive to low-carbon development of China. Nevertheless, the development is hampered by multiple barriers and therefore lags behind expectations. Some studies have been devoted to this, but there are still problems including incomplete identification of obstacles and non-objective analysis methods. Thus, this paper is dedicated to establishing a complete and objective research framework to effectively identify obstacles and promote high level development of rural distributed PV. Firstly, a two-stage process is adopted to extract obstacles from the entire life cycle of rural distributed PV, and 10 obstacles are identified. Then the K-means clustering algorithm are introduced to improve the DEMATEL-ISM method for threshold determination and barrier analysis. Interactions between obstacles are divided into three levels and a network hierarchy that reflects these interactions is created. The results show that imperfect policies and regulations, diminished subsidy, high initial investment and aging equipment and poor quality are critical barriers, and corresponding suggestions on the collaboration between enterprises and the government are put forward. This paper comprehensively studies the obstacles to the development of rural distributed PV by objective methods and proposes suggestions for improvement, which is beneficial to the development of rural distributed PV in China.

Multi-criteria group decision making with a partial-ranking-based ordinal consensus reaching process for automotive development management

The consensus reaching process (CRP) aims at reconciling the conflicts between individual preferences when eliciting collective preferences. The ordinal CRP based on the positional orders of alternatives in linear rankings is straightforward and robust; however, for partial rankings involving preference, indifference and incomparability relations, there is no explicit positional order but are binary relations. This study focuses on partial rankings that may occur when using the ORESTE (organísation, rangement et Synthèse de données relarionnelles, in French) method for making decisions, and designs an ordinal CRP pertaining to the binary relations of alternatives. Concretely, we propose an enhanced ordinal consensus measure with two hierarchies to measure the agreement levels between individual partial rankings. Consensus degrees are calculated based on the frequency distribution of binary relation types, which can avoid subjective axiomatic assumptions on the relations themselves. Besides, a consensus threshold determination method close to cognitive expression is developed. A feedback mechanism is designed to aid experts to modify preferences towards group consensus. An example about the evaluation of automotive design schemes is presented to validate the proposed ordinal CRP. A ranking result that allows the incomparability relations of design schemes is obtained after the information exchange among experts.