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Regional wind power forecasting model with NWP grid data optimized

Zhao WANG, Weisheng WANG, Bo WANG

《能源前沿（英文）》 2017年 第11卷 第2期   页码 175-183 doi: 10.1007/s11708-017-0471-9

摘要： Unlike the traditional fossil energy, wind, as the clean renewable energy, can reduce the emission of the greenhouse gas. To take full advantage of the environmental benefits of wind energy, wind power forecasting has to be studied to overcome the troubles brought by the variable nature of wind. Power forecasting for regional wind farm groups is the problem that many power system operators care about. The high-dimensional feature sets with redundant information are frequently encountered when dealing with this problem. In this paper, two kinds of feature set construction methods are proposed which can achieve the proper feature set either by selecting the subsets or by transforming the original variables with specific combinations. The former method selects the subset according to the criterion of minimal-redundancy-maximal-relevance (mRMR), while the latter does so based on the method of principal component analysis (PCA). A locally weighted learning method is also proposed to utilize the processed feature set to produce the power forecast results. The proposed model is simple and easy to use with parameters optimized automatically. Finally, a case study of 28 wind farms in East China is provided to verify the effectiveness of the proposed method.

关键词： regional wind power forecasting     feature set     minimal-redundancy-maximal-relevance (mRMR)     principal component analysis (PCA)     locally weighted learning model    

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View-invariant human action recognition via robust locally adaptive multi-view learning

Jia-geng FENG,Jun XIAO

《信息与电子工程前沿（英文）》 2015年 第16卷 第11期   页码 917-920 doi: 10.1631/FITEE.1500080

摘要： Human action recognition is currently one of the most active research areas in computer vision. It has been widely used in many applications, such as intelligent surveillance, perceptual interface, and content-based video retrieval. However, some extrinsic factors are barriers for the development of action recognition; e.g., human actions may be observed from arbitrary camera viewpoints in realistic scene. Thus, view-invariant analysis becomes important for action recognition algorithms, and a number of researchers have paid much attention to this issue. In this paper, we present a multi-view learning approach to recognize human actions from different views. As most existing multi-view learning algorithms often suffer from the problem of lacking data adaptiveness in the nearest neighborhood graph construction procedure, a robust locally adaptive multi-view learning algorithm based on learning multiple local L1-graphs is proposed. Moreover, an efficient iterative optimization method is proposed to solve the proposed objective function. Experiments on three public view-invariant action recognition datasets, i.e., ViHASi, IXMAS, and WVU, demonstrate data adaptiveness, effectiveness, and efficiency of our algorithm. More importantly, when the feature dimension is correctly selected (i.e.,>60), the proposed algorithm stably outperforms state-of-the-art counterparts and obtains about 6% improvement in recognition accuracy on the three datasets.

关键词： View-invariant     Action recognition     Multi-view learning     L1-norm     Local learning    

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基于含隐变量的贝叶斯网络质量相关局部加权的非平稳过程软测量方法 Research Articles

《信息与电子工程前沿（英文）》 2021年 第22卷 第9期   页码 1234-1246 doi: 10.1631/FITEE.2000426

摘要：

在工业过程中，软测量技术被广泛用于预测难以测量的质量变量。构建一个应对过程非平稳性的自适应模型非常必要。本文针对非平稳过程，设计了一种基于含有隐变量贝叶斯网络的质量相关局部加权软测量方法。提出一种有监督贝叶斯网络提取质量相关的隐变量，并应用于一种双层相似度测量算法。所提软测量方法试图通过质量相关信息为非平稳过程寻找到一般方法，且详细解释了局部相似度和窗口置信度的概念。通过一个数值算例和脱丁烷塔的应用验证了所提方法的性能。结果表明所提方法预测关键质量变量的精确度优于竞争方法。

关键词： 软测量；有监督贝叶斯网络；隐变量；局部加权建模；质量预测    

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radiation fraction induced by interaction burning of tri-symmetric propane fires in open space based on weightedmulti-point source model

Jie JI, Junrui DUAN, Huaxian WAN

《能源前沿（英文）》 2022年 第16卷 第6期   页码 1017-1026 doi: 10.1007/s11708-020-0716-x

摘要： The interaction of multiple fires may lead to a higher flame height and more intense radiation flux than a single fire, which increases the possibility of flame spread and risks to the surroundings. Experiments were conducted using three burners with identical heat release rates (HRRs) and propane as the fuel at various spacings. The results show that flames change from non-merging to merging as the spacing decreases, which result in a complex evolution of flame height and merging point height. To facilitate the analysis, a novel merging criterion based on the dimensionless spacing / was proposed. For non-merging flames ( / >0.368), the flame height is almost identical to a single fire; for merging flames ( / ≤0.368), based on the relationship between thermal buoyancy and thrust (the pressure difference between the inside and outside of the flame), a quantitative analysis of the flame height, merging point height, and air entrainment was formed, and the calculated merging flame heights show a good agreement with the measured experimental values. Moreover, the multi-point source model was further improved, and radiation fraction of propane was calculated. The data obtained in this study would play an important role in calculating the external radiation of propane fire.

关键词： flame interaction     air entrainment     flame height     multi-point source model     thermal radiation    

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Development of machine learning multi-city model for municipal solid waste generation prediction

《环境科学与工程前沿（英文）》 2022年 第16卷 第9期 doi: 10.1007/s11783-022-1551-6

摘要：

● A database of municipal solid waste (MSW) generation in China was established.

关键词： Municipal solid waste     Machine learning     Multi-cities     Gradient boost regression tree    

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Financing Model Decision of Inter-basin Water Transfer Projects

Ji-wei Zhu,Li-nan Zhou,Zhao Zhai,Cong Wang

《工程管理前沿（英文）》 2016年 第3卷 第4期   页码 396-403 doi: 10.15302/J-FEM-2016060

摘要： Inter-basin Water Transfer Projects require the appropriate financing model to attract large amounts of social investment. Therefore, financing model decision becomes the key of engineering construction. In three aspects, such as the subject, the object and the target of the financing model, Grey Target Model is established in this paper. First, the complex financing mode decision problems of Inter-basin Water Transfer Projects are decomposed by using hierarchical decomposition method. Then Analytical Hierarchy Process (AHP) method is used to calculate the comprehensive weight of evaluation index. Experts’ opinions financing model are transformed into the evaluation matrix based on the Dephi method. The Weighted Grey Target Model is used to calculate the approaching degree of financing model and assists financing mode decision. In addition, this paper takes the water diversion project from the Han to the Wei River of Shaanxi Province as a verification example for the model. For other water diversion projects, the evaluation results are also reliable and provide theoretical references for the financing model decision of Inter-basin Water Transfer Projects.

关键词： Inter-basin Water Transfer Projects     financing model     Weighted Grey Target Model     water diversion     Han River     Wei River    

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Pyogenic liver abscess as initial presentation in locally advanced right colon cancer invading the liver

Kai Qu, Chang Liu, Aasef M A Mansoor, Bo Wang, Jincai Chen, Liang Yu, Yi Lv

《医学前沿（英文）》 2011年 第5卷 第4期   页码 434-437 doi: 10.1007/s11684-011-0157-3

摘要： Locally advanced colorectal cancer complicated with adjacent organic invasion may remain confined to the local area with minimal metastasis. In the present paper, we report on a patient with advanced right colon cancer, including liver, gallbladder, and duodenal invasion behind the scene of liver abscess. resection was performed on the patient, with right-hemicolectomy, cholecystectomy, partial duodental resection, and hepatectomy. Postoperative management was administered, including nutritional support in the early postoperative period, effective anti-infection treatment, and adjuvant chemotherapy (FOLFOX4). The patient survived for 16 months after the operation. Common clinical manifestations of colorectal cancer were digestive symptoms and changes in defecation. However, the clinical manifestation of locally advanced colon cancer was extremely complicated. Extended or multivisceral resection may offer patients a chance to survive an acute crisis and allow for treatment with adjuvant therapy.

关键词： liver abscess     locally advanced colon cancer     multiorganic invasion    

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Machine learning and neural network supported state of health simulation and forecasting model for lithium-ion

《能源前沿（英文）》 doi: 10.1007/s11708-023-0891-7

摘要： As the intersection of disciplines deepens, the field of battery modeling is increasingly employing various artificial intelligence (AI) approaches to improve the efficiency of battery management and enhance the stability and reliability of battery operation. This paper reviews the value of AI methods in lithium-ion battery health management and in particular analyses the application of machine learning (ML), one of the many branches of AI, to lithium-ion battery state of health (SOH), focusing on the advantages and strengths of neural network (NN) methods in ML for lithium-ion battery SOH simulation and prediction. NN is one of the important branches of ML, in which the application of NNs such as backpropagation NN, convolutional NN, and long short-term memory NN in SOH estimation of lithium-ion batteries has received wide attention. Reports so far have shown that the utilization of NN to model the SOH of lithium-ion batteries has the advantages of high efficiency, low energy consumption, high robustness, and scalable models. In the future, NN can make a greater contribution to lithium-ion battery management by, first, utilizing more field data to play a more practical role in health feature screening and model building, and second, by enhancing the intelligent screening and combination of battery parameters to characterize the actual lithium-ion battery SOH to a greater extent. The in-depth application of NN in lithium-ion battery SOH will certainly further enhance the science, reliability, stability, and robustness of lithium-ion battery management.

关键词： machine learning     lithium-ion battery     state of health     neural network     artificial intelligence    

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An assessment of surrogate fuel using Bayesian multiple kernel learning model in sight of sooting tendency

《能源前沿（英文）》 2022年 第16卷 第2期   页码 277-291 doi: 10.1007/s11708-021-0731-6

摘要： An integrated and systematic database of sooting tendency with more than 190 kinds of fuels was obtained through a series of experimental investigations. The laser-induced incandescence (LII) method was used to acquire the 2D distribution of soot volume fraction, and an apparatus-independent yield sooting index (YSI) was experimentally obtained. Based on the database, a novel predicting model of YSI values for surrogate fuels was proposed with the application of a machine learning method, named the Bayesian multiple kernel learning (BMKL) model. A high correlation coefficient (0.986) between measured YSIs and predicted values with the BMKL model was obtained, indicating that the BMKL model had a reliable and accurate predictive capacity for YSI values of surrogate fuels. The BMKL model provides an accurate and low-cost approach to assess surrogate performances of diesel, jet fuel, and biodiesel in terms of sooting tendency. Particularly, this model is one of the first attempts to predict the sooting tendencies of surrogate fuels that concurrently contain hydrocarbon and oxygenated components and shows a satisfying matching level. During surrogate formulation, the BMKL model can be used to shrink the surrogate candidate list in terms of sooting tendency and ensure the optimal surrogate has a satisfying matching level of soot behaviors. Due to the high accuracy and resolution of YSI prediction, the BMKL model is also capable of providing distinguishing information of sooting tendency for surrogate design.

关键词： sooting tendency     yield sooting index     Bayesian multiple kernel learning     surrogate assessment     surrogate formulation    

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Hybrid deep learning model for risk prediction of fracture in patients with diabetes and osteoporosis

《医学前沿（英文）》 2022年 第16卷 第3期   页码 496-506 doi: 10.1007/s11684-021-0828-7

摘要： The fracture risk of patients with diabetes is higher than those of patients without diabetes due to hyperglycemia, usage of diabetes drugs, changes in insulin levels, and excretion, and this risk begins as early as adolescence. Many factors including demographic data (such as age, height, weight, and gender), medical history (such as smoking, drinking, and menopause), and examination (such as bone mineral density, blood routine, and urine routine) may be related to bone metabolism in patients with diabetes. However, most of the existing methods are qualitative assessments and do not consider the interactions of the physiological factors of humans. In addition, the fracture risk of patients with diabetes and osteoporosis has not been further studied previously. In this paper, a hybrid model combining XGBoost with deep neural network is used to predict the fracture risk of patients with diabetes and osteoporosis, and investigate the effect of patients’ physiological factors on fracture risk. A total of 147 raw input features are considered in our model. The presented model is compared with several benchmarks based on various metrics to prove its effectiveness. Moreover, the top 18 influencing factors of fracture risks of patients with diabetes are determined.

关键词： XGBoost     deep neural network     healthcare     risk prediction    

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Topology-independent end-to-end learning model for improving the voltage profile in microgrids-integrated

《能源前沿（英文）》 2023年 第17卷 第2期   页码 211-227 doi: 10.1007/s11708-022-0847-3

摘要： With multiple microgrids (MGs) integrated into power distribution networks in a distributed manner, the penetration of renewable energy like photovoltaic (PV) power generation surges. However, the operation of power distribution networks is challenged by the issues of multiple power flow directions and voltage security. Accordingly, an efficient voltage control strategy is needed to ensure voltage security against ever-changing operating conditions, especially when the network topology information is absent or inaccurate. In this paper, we propose a novel data-driven voltage profile improvement model, denoted as system-wide composite adaptive network (SCAN), which depends on operational data instead of network topology details in the context of power distribution networks integrated with multiple MGs. Unlike existing studies that realize topology identification and decision-making optimization in sequence, the proposed end-to-end model determines the optimal voltage control decisions in one shot. More specifically, the proposed model consists of four modules, Pre-training Network and modified interior point methods with adversarial networks (Modified IPMAN) as core modules, and discriminator generative adversarial network (Dis-GAN) and Volt convolutional neural network (Volt-CNN) as ancillary modules. In particular, the generator in SCAN is trained by the core modules in sequence so as to form an end-to-end mode from data to decision. Numerical experiments based on IEEE 33-bus and 123-bus systems have validated the effectiveness and efficiency of the proposed method.

关键词： end-to-end learning     microgrids     voltage profile improvement     generative adversarial network    

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evaluation of renal function using diffusion weighted imaging and diffusion tensor imaging in type 2

null

《医学前沿（英文）》 2014年 第8卷 第4期   页码 471-476 doi: 10.1007/s11684-014-0365-8

摘要：

This work aims to estimate the value of diffusion weighted imaging (DWI) and diffusion tensor imaging (DTI) in detecting early-stage kidney injury in type 2 diabetic patients with normoalbuminuria (NAU) versus microalbuminuria (MAU) prospectively. A total of 30 T2DM patients with normal kidney function were recruited and assigned to the NAU group (n = 14) or MAU group (n= 16) according to 8 h overnight urinary albuminuria excretion rate (AER) results. A contemporary cohort of health check-up recipients were included as controls (n = 12). DWI and DTI scans were performed on bilateral kidney using SE single-shot EPI, and apparent diffusion coefficient (ADC) and fractional anisotropy (FA) of the renal parenchyma was determined from ADC and FA maps of the three groups. ADC and FA values were compared among the three groups. According to DWI with a b value of 400 s/mm2, the MAU and NAU groups showed significantly lowered mean ADC values compared with the healthy controls (P<0.01). The mean ADC in the MAU group [(2.22±0.07)×10–3 mm2/s] was slightly lower than that of the NAU group [(2.31±0.22)×10–3 mm2/s], but this difference was not statistically significant (P>0.05). The FA value in the MAU group was higher than that in the control group (0.45±0.07 vs. 0.39±0.03, P = 0.004) but did not differ from that in the NAU group (0.42±0.03) (P>0.05). ADC and FA values may be more sensitive than urine AER in reflecting early-stage kidney injury and, hence, may facilitate earlier detection and quantitative evaluation of kidney injury in T2DM patients. Combined evaluation of ADC and FA values may provide a better quantitative approach for identifying diabetic nephropathy at early disease stages.

关键词： type 2 diabetes mellitus     microalbuminuria     diffusion weighted imaging     diffusion tensor imaging     early-stage kidney injury    

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An integrated approach for machine-learning-based system identification of dynamical systems under control: application towards the model predictive control of a highly nonlinear reactor system

《化学科学与工程前沿（英文）》 2022年 第16卷 第2期   页码 237-250 doi: 10.1007/s11705-021-2058-6

摘要： Advanced model-based control strategies, e.g., model predictive control, can offer superior control of key process variables for multiple-input multiple-output systems. The quality of the system model is critical to controller performance and should adequately describe the process dynamics across its operating range while remaining amenable to fast optimization. This work articulates an integrated system identification procedure for deriving black-box nonlinear continuous-time multiple-input multiple-output system models for nonlinear model predictive control. To showcase this approach, five candidate models for polynomial and interaction features of both output and manipulated variables were trained on simulated data and integrated into a nonlinear model predictive controller for a highly nonlinear continuous stirred tank reactor system. This procedure successfully identified system models that enabled effective control in both servo and regulator problems across wider operating ranges. These controllers also had reasonable per-iteration times of ca. 0.1 s. This demonstration of how such system models could be identified for nonlinear model predictive control without prior knowledge of system dynamics opens further possibilities for direct data-driven methodologies for model-based control which, in the face of process uncertainties or modelling limitations, allow rapid and stable control over wider operating ranges.

关键词： nonlinear model predictive control     black-box modeling     continuous-time system identification     machine learning     industrial applications of process control    

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A hybrid deep learning model for robust prediction of the dimensional accuracy in precision milling of

《机械工程前沿（英文）》 2022年 第17卷 第3期 doi: 10.1007/s11465-022-0688-0

摘要： The use of artificial intelligence to process sensor data and predict the dimensional accuracy of machined parts is of great interest to the manufacturing community and can facilitate the intelligent production of many key engineering components. In this study, we develop a predictive model of the dimensional accuracy for precision milling of thin-walled structural components. The aim is to classify three typical features of a structural component—squares, slots, and holes—into various categories based on their dimensional errors (i.e., “high precision,” “pass,” and “unqualified”). Two different types of classification schemes have been considered in this study: those that perform feature extraction by using the convolutional neural networks and those based on an explicit feature extraction procedure. The classification accuracy of the popular machine learning methods has been evaluated in comparison with the proposed deep learning model. Based on the experimental data collected during the milling experiments, the proposed model proved to be capable of predicting dimensional accuracy using cutting parameters (i.e., “static features”) and cutting-force data (i.e., “dynamic features”). The average classification accuracy obtained using the proposed deep learning model was 9.55% higher than the best machine learning algorithm considered in this paper. Moreover, the robustness of the hybrid model has been studied by considering the white Gaussian and coherent noises. Hence, the proposed hybrid model provides an efficient way of fusing different sources of process data and can be adopted for prediction of the machining quality in noisy environments.

关键词： precision milling     dimensional accuracy     cutting force     convolutional neural networks     coherent noise    

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Latent source-specific generative factor learning for monaural speech separation using weighted-factor

Jing-jing Chen, Qi-rong Mao, You-cai Qin, Shuang-qing Qian, Zhi-shen Zheng,2221808071@stmail.ujs.edu.cn,mao_qr@ujs.edu.cn,2211908026@stmail.ujs.edu.cn,2211908025@stmail.ujs.edu.cn,3160602062@stmail.ujs.edu.cn

《信息与电子工程前沿（英文）》 2020年 第21卷 第11期   页码 1535-1670 doi: 10.1631/FITEE.2000019

摘要： Much recent progress in monaural (MSS) has been achieved through a series of architectures based on s, which use an encoder to condense the input signal into compressed features and then feed these features into a decoder to construct a specific audio source of interest. However, these approaches can neither learn of the original input for MSS nor construct each audio source in mixed speech. In this study, we propose a novel weighted-factor (WFAE) model for MSS, which introduces a regularization loss in the objective function to isolate one source without containing other sources. By incorporating a latent attention mechanism and a supervised source constructor in the separation layer, WFAE can learn source-specific and a set of discriminative features for each source, leading to MSS performance improvement. Experiments on benchmark datasets show that our approach outperforms the existing methods. In terms of three important metrics, WFAE has great success on a relatively challenging MSS case, i.e., speaker-independent MSS.

关键词： 语音分离；生成因子；自动编码器；深度学习    

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标题 作者 时间 类型 操作

Regional wind power forecasting model with NWP grid data optimized

Zhao WANG, Weisheng WANG, Bo WANG

期刊论文

View-invariant human action recognition via robust locally adaptive multi-view learning

Jia-geng FENG,Jun XIAO

期刊论文

基于含隐变量的贝叶斯网络质量相关局部加权的非平稳过程软测量方法

期刊论文

radiation fraction induced by interaction burning of tri-symmetric propane fires in open space based on weightedmulti-point source model

Jie JI, Junrui DUAN, Huaxian WAN

期刊论文

Development of machine learning multi-city model for municipal solid waste generation prediction

期刊论文

Financing Model Decision of Inter-basin Water Transfer Projects

Ji-wei Zhu,Li-nan Zhou,Zhao Zhai,Cong Wang

期刊论文

Pyogenic liver abscess as initial presentation in locally advanced right colon cancer invading the liver

Kai Qu, Chang Liu, Aasef M A Mansoor, Bo Wang, Jincai Chen, Liang Yu, Yi Lv

期刊论文

Machine learning and neural network supported state of health simulation and forecasting model for lithium-ion

期刊论文

An assessment of surrogate fuel using Bayesian multiple kernel learning model in sight of sooting tendency

期刊论文

Hybrid deep learning model for risk prediction of fracture in patients with diabetes and osteoporosis

期刊论文

Topology-independent end-to-end learning model for improving the voltage profile in microgrids-integrated

期刊论文

evaluation of renal function using diffusion weighted imaging and diffusion tensor imaging in type 2

null

期刊论文

An integrated approach for machine-learning-based system identification of dynamical systems under control: application towards the model predictive control of a highly nonlinear reactor system

期刊论文

A hybrid deep learning model for robust prediction of the dimensional accuracy in precision milling of

期刊论文

Latent source-specific generative factor learning for monaural speech separation using weighted-factor

Jing-jing Chen, Qi-rong Mao, You-cai Qin, Shuang-qing Qian, Zhi-shen Zheng,2221808071@stmail.ujs.edu.cn,mao_qr@ujs.edu.cn,2211908026@stmail.ujs.edu.cn,2211908025@stmail.ujs.edu.cn,3160602062@stmail.ujs.edu.cn

期刊论文