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建模与仿真 2024年第2期13卷 1265-1278页

作者：吕园园赵大胜金志尧上海理工大学机械工程学院，上海

中国是一个资源大国，拥有丰富的草地。这些草原生态系统是保持我国生态平衡的关键防线，同时为经济发展提供稳固的基础。然而，随着畜牧业的迅速发展，草地退化问题日益突出，甚至在一些地区呈现沙漠化趋势。面对这一情况，迫切需要提... 详细信息

中国是一个资源大国，拥有丰富的草地。这些草原生态系统是保持我国生态平衡的关键防线，同时为经济发展提供稳固的基础。然而，随着畜牧业的迅速发展，草地退化问题日益突出，甚至在一些地区呈现沙漠化趋势。面对这一情况，迫切需要提供科学合理的草地管理方式。因此，对土壤状态的准确预测对于草原的可持续保护和合理开发至关重要。本文通过对历年统计数据的分析，首先通过Softmax逻辑回归模型，结合处理后的数据，得到六种土壤状态与不同放牧策略的四分类模型，建立了不同放牧策略对草原土壤状态影响的数学模型。然后使用LSTM模型并基于多年份同放牧强度和放牧小区的土壤不同状态下的数据来进行训练，预测得到2023年的同条件下的土壤在不同状态下的数据。再利用沙漠化程度指数预测模型和数据来确定不同放牧强度下监测点的沙漠化程度数值。最后使用有机量、含水量、叶面积指数等指标代替用于衡量土壤状态的指标，有土壤肥力变化、土壤湿度、植被覆盖等，综合2014~2022年的土壤数据，使用LSTM模型预测得到2024年同月的土壤状态数据。

关键词： Softmax逻辑回归模型 LSTM模型衡量指标土壤状态 Softmax Logistic Regression Model LSTM Model evaluation metrics Soil Condition

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An Efficient Medical Image Deep Fusion Model Based on Convolutional Neural Networks

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Computers, Materials & Continua 2023年第2期74卷 2905-2925页

作者： Walid El-Shafai Noha A.El-Hag Ahmed Sedik Ghada Elbanby Fathi E.Abd El-Samie Naglaa F.Soliman Hussah Nasser AlEisa Mohammed E.Abdel Samea Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Laboratory Department of Computer SciencePrince Sultan UniversityRiyadh11586Saudi Arabia Higher Institute of Commercial Science Management Information SystemsEl-Mahala El-KobraEgypt Department of the Robotics and Intelligent Machines Faculty of Artificial IntelligenceKafrelsheikh UniversityKafr El-SheikhEgypt Department of Industrial Electronics and Control Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadhSaudi Arabia Medical Imaging and Interventional Radiology National Liver InstituteMenoufia UniversityEgypt

Medical image fusion is considered the best method for obtaining one image with rich details for efficient medical diagnosis and therapy.Deep learning provides a high performance for several medical image analysis applications.This paper proposes a deep learning model for the medical image fusion process.This model depends on Convolutional Neural Network(CNN).The basic idea of the proposed model is to extract features from both CT and MR images.Then,an additional process is executed on the extracted features.After that,the fused feature map is reconstructed to obtain the resulting fused image.Finally,the quality of the resulting fused image is enhanced by various enhancement techniques such as Histogram Matching(HM),Histogram Equalization(HE),fuzzy technique,fuzzy type,and Contrast Limited Histogram Equalization(CLAHE).The performance of the proposed fusion-based CNN model is measured by various metrics of the fusion and enhancement quality.Different realistic datasets of different modalities and diseases are tested and implemented.Also,real datasets are tested in the simulation analysis.

关键词： Image fusion CNN deep learning feature extraction evaluation metrics medical diagnosis

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Supervised and Semi-supervised Methods for Abdominal Organ Segmentation: A Review

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International Journal of Automation and computing 2021年第6期18卷 887-914页

作者： Isaac Baffour Senkyire Zhe Liu School of Computer Science and Communication Engineering Jiangsu UniversityZhenjiang 212013China Computer Science Department Ghana Communication Technology UniversityAccraGhana

Abdominal organ segmentation is the segregation of a single or multiple abdominal organ(s) into semantic image segments of pixels identified with homogeneous features such as color and texture, and intensity. The abdominal organ(s) condition is mostly connected with greater morbidity and mortality. Most patients often have asymptomatic abdominal conditions and symptoms, which are often recognized late;hence the abdomen has been the third most common cause of damage to the human body. That notwithstanding,there may be improved outcomes where the condition of an abdominal organ is detected earlier. Over the years, supervised and semi-supervised machine learning methods have been used to segment abdominal organ(s) in order to detect the organ(s) condition. The supervised methods perform well when the used training data represents the target data, but the methods require large manually annotated data and have adaptation problems. The semi-supervised methods are fast but record poor performance than the supervised if assumptions about the data fail to hold. Current state-of-the-art methods of supervised segmentation are largely based on deep learning techniques due to their good accuracy and success in real world applications. Though it requires a large amount of training data for automatic feature extraction, deep learning can hardly be used. As regards the semi-supervised methods of segmentation, self-training and graph-based techniques have attracted much research attention. Self-training can be used with any classifier but does not have a mechanism to rectify mistakes early. Graph-based techniques thrive on their convexity, scalability, and effectiveness in application but have an out-of-sample problem. In this review paper, a study has been carried out on supervised and semi-supervised methods of performing abdominal organ segmentation. An observation of the current approaches, connection and gaps are identified, and prospective future research opportunities are enumerated.

关键词： Abdominal organ supervised segmentation semi-supervised segmentation evaluation metrics image segmentation machine learning

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Classical and state-of-the-art approaches for underwater image defogging: a comprehensive survey

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Frontiers of Information Technology & Electronic Engineering 2020年第12期21卷 1745-1769页

作者： Jing-chun ZHOU De-huan ZHANG Wei-shi ZHANG College of Information Science and Technology Dalian Maritime UniversityDalian 116026China

In underwater scenes,the quality of the video and image acquired by the underwater imaging system suffers from severe degradation,influencing target detection and recognition.Thus,restoring real scenes from blurred videos and images is of great significance.Owing to the light absorption and scattering by suspended particles,the images acquired often have poor visibility,including color shift,low contrast,noise,and blurring issues.This paper aims to classify and compare some of the significant technologies in underwater image defogging,presenting a comprehensive picture of the current research landscape for researchers.First we analyze the reasons for degradation of underwater images and the underwater optical imaging model.Then we classify the underwater image defogging technologies into three categories,including image restoration approaches,image enhancement approaches,and deep learning approaches.Afterward,we present the objective evaluation metrics and analyze the state-of-the-art approaches.Finally,we summarize the shortcomings of the defogging approaches for underwater images and propose seven research directions.

关键词： Underwater image defogging Restoration approaches Enhancement approaches evaluation metrics

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CloudRank-D： benchmarking and ranking cloud computing systems for data processing applications

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Frontiers of Computer Science 2012年第4期6卷 347-362页

作者： Chunjie LUO Jianfeng ZHAN Zhen JIA Lei WANG Gang LU Lixin ZHANG Cheng-Zhong XU Ninghui SUN State Key Laboratory of Computer Architecture Institute of Computing TechnologyChinese Academy of SciencesBeijing 100019China Department of Electrical and Computer Engineering Wayne State UniversityDetroitM148202USA Shenzhenlnstitute of Advanced Technology Chinese Academy of SciencesShenzhen 518055China

With the explosive growth of information, more and more organizations are deploying private cloud systems or renting public cloud systems to process big data. However, there is no existing benchmark suite for evaluating cloud performance on the whole system level. To the best of our knowledge, this paper proposes the first benchmark suite CloudRank-D to benchmark and rank cloud computing sys- tems that are shared for running big data applications. We an- alyze the limitations of previous metrics, e.g., floating point operations, for evaluating a cloud computing system, and propose two simple metrics： data processed per second and data processed per Joule as two complementary metrics for evaluating cloud computing systems. We detail the design of CloudRank-D that considers representative applications, di- versity of data characteristics, and dynamic behaviors of both applications and system software platforms. Through experi- ments, we demonstrate the advantages of our proposed met- tics. In several case studies, we evaluate two small-scale de- ployments of cloud computing systems using CloudRank-D.

关键词： data center systems clouds big data applica- tions benchmarks evaluation metrics

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Efficient Leave-One-Out Strategy for Supervised Feature Selection

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Tsinghua Science and Technology 2013年第6期18卷 629-635页

作者： Dingcheng Feng Feng Chen Wenli Xu National Laboratory for Information Science and Technology Tsinghua University

Feature selection is a key task in statistical pattern recognition. Most feature selection algorithms have been proposed based on specific objective functions which are usually intuitively reasonable but can sometimes be far from the more basic objectives of the feature selection. This paper describes how to select features such that the basic objectives, e.g., classification or clustering accuracies, can be optimized in a more direct way. The analysis requires that the contribution of each feature to the evaluation metrics can be quantitatively described by some score function. Motivated by the conditional independence structure in probabilistic distributions, the analysis uses a leave-one-out feature selection algorithm which provides an approximate solution. The leave-one- out algorithm improves the conventional greedy backward elimination algorithm by preserving more interactions among features in the selection process, so that the various feature selection objectives can be optimized in a unified way. Experiments on six real-world datasets with different feature evaluation metrics have shown that this algorithm outperforms popular feature selection algorithms in most situations.

关键词： leave-one-out feature selection objectives evaluation metrics

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Fuzzy Comprehensive evaluation for Autonomy Levels of Unmanned

Fuzzy Comprehensive Evaluation for Autonomy Levels of Unmann...

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2011 International Conference on Management Science and Intelligent Control(ICMSIC 2011)

作者： Yi-bo LI Shenyang Aerospace University Shenyang China Xin-xing WANG Shenyang Aerospace University Shenyang China

In this paper, a new autonomy level model of unmanned platforms is constructed. The new model is further improvement of Autonomy Levels for Unmanned Systems （ALFUS） model which is presented by Ad Hoc working group. Firstly, this model’s evaluation metrics are simple and clear. Then the weights of evaluation metrics are decided by analytic hierarchy process （AHP）.A unmanned platform is combined with hard equipments and software programs. As a result, its metrics in hardware and software are divided into four sub-metrics to evaluate the autonomy level of the unmanned platform. The method of fuzzy set theory could quantify the four sub-metrics. Fuzzy comprehensive evaluation is applied to each level metric to get the scores of this level in hardware and software. The final autonomy level is decided by the final scores. Finally, the new model is applied to evaluate the autonomy level of Global Hawk which is compared with X-45A, X-48B and Shadow 200.

关键词： unmanned platforms autonomy level evaluation metrics comprehensive evaluation

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供应商分级研究及量化分析

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贸易经济 2009年第4期 42-45页

作者：周世平刘晋

世界经济的一体化，企业经营的全球化，客户需求的个性化多变性，使供应链与供应链之间的竞争异常激烈，而供应商又是整个供应链“源头”和上游控制的“先导因素”，对其进行相关研究至关重要。所以对供应商要进行分级研究，建立供应商... 详细信息

世界经济的一体化，企业经营的全球化，客户需求的个性化多变性，使供应链与供应链之间的竞争异常激烈，而供应商又是整个供应链“源头”和上游控制的“先导因素”，对其进行相关研究至关重要。所以对供应商要进行分级研究，建立供应商级别评价指标体系，并运用三级模糊综合评判模型对供应商级别进行模糊量化分析，这样，为企业对供应商进行分级管理提供了依据。

关键词：供应商级别评价指标体系模糊量化分析　　suppliers class evaluation metrics fuzzy quantitative analysis

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