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Impacts of rainfall extremes on wheat yield in semi-arid cropping systems in eastern Australia

CLIMATIC CHANGE 2018年第3-4期147卷 555-569页

作者： Feng, Puyu Wang, Bin Liu, De Li Xing, Hongtao Ji, Fei Macadam, Ian Ruan, Hongyan Yu, Qiang Univ Technol Sydney Sch Life Sci Fac Sci POB 123 Sydney NSW 2007 Australia Wagga Wagga Agr Inst NSW Dept Primary Ind Wagga Wagga NSW 2650 Australia Univ New South Wales Climate Change Res Ctr Sydney NSW 2052 Australia Univ New South Wales ARC Ctr Excellence Climate Syst Sci Sydney NSW 2052 Australia NSW Off Environm & Heritage Queanbeyan Australia Guangxi Univ Coll Agr Nanning 530000 Guangxi Peoples R China Northwest A&F Univ State Key Lab Soil Eros & Dryland Farming Loess P Yangling 712100 Shaanxi Peoples R China Univ Chinese Acad Sci Coll Resources & Environm Beijing 100049 Peoples R China

Investigating the relationships between climate extremes and crop yield can help us understand how unfavourable climatic conditions affect crop production. In this study, two statistical models, multiple linear regression and random forest, were used to identify rainfall extremes indices affecting wheat yield in three different regions of the New South Wales wheat belt. The results show that the random forest model explained 41-67% of the year-to-year yield variation, whereas the multiple linear regression model explained 34-58%. In the two models, 3-month timescale standardized precipitation index of Jun.-Aug. (SPIJJA), Sep.-Nov. (SPISON), and consecutive dry days (CDDs) were identified as the three most important indices which can explain yield variability for most of the wheat belt. Our results indicated that the inter-annual variability of rainfall in winter and spring was largely responsible for wheat yield variation, and pre-growing season rainfall played a secondary role. Frequent shortages of rainfall posed a greater threat to crop growth than excessive rainfall in eastern Australia. We concluded that the comparison between multiple linear regression and machine learning algorithm proposed in the present study would be useful to provide robust prediction of yields and new insights of the effects of various rainfall extremes, when suitable climate and yield datasets are available.

关键词： agricultural regions Climatic conditions Excess rainfall extremum values machine learning algorithms Rainfall rain Linear Regression

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Diagnosing Fault Types and Degrees of Transformer Winding Combining FRA Method With SOA-KELM

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IEEE ACCESS 2024年 12卷 50287-50299页

作者： Wang, Guohao Qiu, Shengxuan Xie, Fei Luo, Tengqi Song, Ying Wang, Song Northwest A&F Univ Dept Power & Elect Engn Xianyang 712100 Peoples R China

Power transformers are the vital and expensive components of the power system. Timely identifying and diagnosing the transformer faults is critical to maintaining the stability of the power grid. As a sensitive and economical tool, the frequency response analysis (FRA) method has been widely employed to detect winding faults. However, it is still a challenge to accurately identify the fault types and degrees only by the FRA method. In this article, a new diagnosis method that combines the FRA method with a kernel-based extreme learning machine (KELM) optimized by a seagull optimization algorithm (SOA), is proposed to diagnose the fault types and degrees of the winding. First, a series of FRA tests are performed on a laboratory winding model under three different faults to obtain the FRA dataset. Furthermore, various numerical indices are applied to extract the characteristics of FRA signatures to train the SOA-KELM model. Then, the trained SOA-KELM model is utilized to classify fault types and degrees of the winding. Finally, the feasibility and superiority of SOA-KELM are verified by comparing with SOA optimized support vector machine (SOA-SVM) and random forest (SOA-RF), particle swarm optimization (PSO) algorithm optimized KELM (PSO-KELM), PSO-SVM, PSO-RF, SVM, RF, and KELM from the aspects of diagnosis accuracy and running time. The comprehensive comparison results show that SOA-KELM has the best diagnosis performance.

关键词： Windings Support vector machines Numerical models Computational modeling Circuit faults machine learning algorithms Power transformers Frequency response Extreme learning machines Optimization methods Power transformer frequency response analysis (FRA) kernel extreme learning machine (KELM) seagull optimization algorithm (SOA)

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A Housekeeping Prognostic Health Management Framework for Microfluidic Systems

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IEEE TRANSACTIONS ON DEVICE AND MATERIALS RELIABILITY 2017年第2期17卷 438-449页

作者： Khan, H. Al-Gayem, Qais Richardson, A. M. COMSATS Inst Informat Technol Dept Elect Engn Islamabad 45550 Pakistan Univ Babylon Elect & Elect Dept Babylon Iraq Univ Lancaster Dept Engn Lancaster LA1 4YR England

Micro-electro-mechanical systems and microfluidics are becoming popular solutions for sensing, diagnostics, and control applications. Reliability and validation of function is of increasing importance in the majority of these applications. On-line testing strategies for these devices have the potential to provide real-time condition monitoring information. It is shown that this information can be used to diagnose and prognose the health of the device. This information can also be used to provide an early failure warning system by predicting the remaining useful life. Diagnostic and prognostic outcomes can also be lever-aged to improve the reliability, dependability, and availability of these devices. This paper has delivered a methodology for a "lightweight" prognostics solution for a microfluidic device based on real-time diagnostics. An oscillation-based test methodology is used to extract diagnostic information that is processed using a linear discriminant analysis-based classifier. This enables the identification of current health-based on pre-defined health levels. As the deteriorating device is periodically classified, the rate at which the device degrades is used to predict the devices remaining useful life.

关键词： Microfluidics prognostics and health management microelectrodes design for testability machine learning algorithms

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Complex Human Activity Recognition Using a Local Weighted Approach

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IEEE ACCESS 2022年 10卷 101207-101219页

作者： Asuroglu, Tunc Tampere Univ Fac Med & Hlth Technol Tampere 33720 Finland

Due to the life expectancy increase, there will be a workforce shortage in elderly care sector in forthcoming years. Ambient Assisted Living (AAL) systems can cope with this issue. A subset of AAL, Human activity recognition (HAR) provides an efficient way to tackle this issue. It can help with evaluating general health and welfare of elderly by automatically tracking their activities. Lifelogging and home diary applications will reduce the load on physicians and caregivers. On the other hand, complex activities play a vital role as they have high level semantic characteristics that truly represent daily life of the user. The main objective is to track these high-level semantic motions with low-cost single sensor systems with efficient machine learning frameworks. To achieve this objective, a framework is proposed to predict complex human activities from a single sensor using a machine learning approach. Time and frequency features are extracted from PAAL ADL Accelerometry Dataset and fed to Locally Weighted Random Forest (LWRF) machine learning algorithm. This algorithm is a hybrid structure that utilizes local weighting by introducing neighboring samples on Random Forest tree building phases. Proposed approach achieved 91% accuracy for HAR and 91.3% for gender recognition, outperforming other machine learning algorithms and previous study on the same dataset. This is the first study that utilize a local weighted approach for accelerometer signal domain. For prospective application, proposed framework can be embedded in lifelogging and home diary applications in home environments to track mental status of elderlies.

关键词： Feature extraction Accelerometers Older adults machine learning machine learning algorithms Task analysis Random forests Ambient assisted living Wearable sensors Assistive technologies Accelerometers activity recognition ambient assisted living machine learning wearable sensors

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Hyperparameter Optimization Using Iterative Decision Tree (IDT)

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IEEE ACCESS 2022年 10卷 106812-106827页

作者： Saum, Narith Sugiura, Satoshi Piantanakulchai, Mongkut Hokkaido Univ Div Engn & Policy Sustainable Environm Sapporo Hokkaido 0600808 Japan Thammasat Univ Sch Civil Engn & Technol Sirindhorn Int Inst Technol Pathum Thani 12121 Thailand

machine learning and deep learning have gained a lot of attention from researchers because of their promising predictive performance and the availability of extensive high-dimensional data and high-performance computational hardware. However, the performance of these algorithms is susceptible to the choice of hyperparameters, while optimizing these hyperparameters is usually computationally expensive. For this reason, this study proposed a novel sequential search algorithm using decision tree regression as the surrogate function. In each iteration of this algorithm, several new combinations of hyperparameters were selected from a few best-performed leaves of the decision tree, called Iterative Decision Tree (IDT). Our approach could reduce the computational time from repetitive training the surrogate function compared to conventional sequential search algorithms and enable parallel computing. To confirm the effectiveness of the proposed algorithm, it was compared with six popular benchmark optimization algorithms, including Grid Search, Random Search, Bayesian Optimization, Random Forest, Tree-Structured Parzen Estimation, and Genetic Algorithm. The comparison was examined by optimizing three benchmark nonconvex functions, and hyperparameter tuning of two machine learning algorithms (Support Vector machine and Random Forest) and two deep learning models (Autoencoder and Convolutional Neural Networks). As a result, the proposed algorithm achieved competitive performance with high stability in addition to the feature importance metrics.

关键词： Optimization machine learning algorithms Linear programming Training Genetic algorithms Deep learning Search problems Decision tree deep learning feature importance hyperparameter optimization machine learning

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Array-Based Disease Diagnostics Using Lipid/Polydiacetylene Vesicles Encapsulated in a Sol-Gel Matrix

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ANALYTICAL CHEMISTRY 2012年第14期84卷 5925-5931页

作者： Kolusheva, S. Yossef, R. Kugel, A. Katz, M. Volinsky, R. Welt, M. Hadad, U. Drory, V. Kliger, M. Rubin, E. Porgador, A. Jelinek, R. Ben Gurion Univ Negev Fac Hlth Sci Shraga Segal Dept Microbiol & Immunol Beer Sheva Israel Ben Gurion Univ Negev Fac Nat Sci Ilse Katz Inst IL-84105 Beer Sheva Israel Aalto Univ Dept Biomed Engn & Computat Sci Helsinki Biophys & Biomembrane Grp Espoo Finland Tel Aviv Univ Tel Aviv Sourasky Med Ctr Dept Neurol IL-69978 Tel Aviv Israel Tel Aviv Univ Sackler Sch Med IL-69978 Tel Aviv Israel Medasense Biometr Ltd IL-87516 Ofakim Israel Ben Gurion Univ Negev Natl Inst Biotechnol Negev IL-84105 Beer Sheva Israel

We demonstrate a novel array-based diagnostic platform comprising lipid/polydiacetylene (PDA) vesicles embedded within a I transparent silica-gel matrix. The diagnostic scheme is based upon the unique chromatic properties of PDA, which undergoes blue-red transformations induced by interactions with amphiphilic or membrane-active analytes. We show that constructing a gel matrix array hosting PDA vesicles with different lipid compositions and applying to blood plasma obtained from healthy individuals and from patients suffering from disease, respectively, allow distinguishing among the disease conditions through application of a simple machine-learning algorithm, using the colorimetric response of the lipid/PDA/gel matrix as the input. Importantly, the new colorimetric diagnostic approach does not require a priori knowledge on the exact metabolite compositions of the blood plasma, since the concept relies only on identifying statistically significant changes in overall disease induced chromatic response. The chromatic lipid/PDA/gel array-based "fingerprinting" concept is generic, easy to apply, and could be implemented for varied diagnostic and screening applications.

关键词： Diagnosis vesicles Encapsulated Mass lipid composition Sol-gels maladjustment machine learning algorithms

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Privacy-Preserving Push-Pull Method for Decentralized Optimization via State Decomposition

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IEEE TRANSACTIONS ON SIGNAL AND INFORMATION PROCESSING OVER NETWORKS 2024年 10卷 513-526页

作者： Cheng, Huqiang Liao, Xiaofeng Li, Huaqing Lu, Qingguo Zhao, You Chongqing Univ Coll Comp Sci Key Lab Dependable Serv Comp Cyber Phys Soc Minist Educ Chongqing 400044 Peoples R China Southwest Univ Coll Elect & Informat Engn Chongqing Key Lab Nonlinear Circuits & Intelligent Chongqing 400715 Peoples R China

Distributed optimization is manifesting great potential in multiple fields, e.g., machine learning, control, resource allocation, etc. Existing decentralized optimization algorithms require sharing explicit state information among the agents, which raises the risk of private information leakage. To ensure privacy security, combining information security mechanisms, such as differential privacy and homomorphic encryption, with traditional decentralized optimization algorithms is a commonly used means. However, this may either sacrifice optimization accuracy or incur a heavy computational burden. To overcome these shortcomings, we develop a novel privacy-preserving decentralized optimization algorithm, named PPSD, that combines gradient tracking with a state decomposition mechanism. Specifically, each agent decomposes its state associated with the gradient into two substates. One substate is used for interaction with neighboring agents, and the other substate containing private information acts only on the first substate and thus is entirely agnostic to other agents. When the objective function is smooth and satisfies the Polyak-& Lstrok;ojasiewicz (PL) condition, PPSD attains an R-linear convergence rate. Moreover, the algorithm can preserve the normal agents' private information from being leaked to honest-but-curious attackers. Simulations further confirm the results.

关键词： Optimization Privacy Heuristic algorithms Linear programming Convergence machine learning algorithms Information processing Privacy protection decentralized optimization push-pull state decomposition

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Functional Knowledge Transfer for High-accuracy Prediction of Under-studied Biological Processes

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PLOS COMPUTATIONAL BIOLOGY 2013年第3期9卷 1-14页

作者： Park, Christopher Y. Wong, Aaron K. Greene, Casey S. Rowland, Jessica Guan, Yuanfang Bongo, Lars A. Burdine, Rebecca D. Troyanskaya, Olga G. Princeton Univ Dept Comp Sci Princeton NJ 08544 USA Princeton Univ Lewis Sigler Inst Integrat Genom Princeton NJ 08544 USA Princeton Univ Dept Mol Biol Princeton NJ 08544 USA Univ Michigan Dept Computat Med & Bioinformat Ann Arbor MI 48109 USA Univ Tromso Dept Comp Sci Tromso Norway

A key challenge in genetics is identifying the functional roles of genes in pathways. Numerous functional genomics techniques (e.g. machine learning) that predict protein function have been developed to address this question. These methods generally build from existing annotations of genes to pathways and thus are often unable to identify additional genes participating in processes that are not already well studied. Many of these processes are well studied in some organism, but not necessarily in an investigator's organism of interest. Sequence-based search methods (e.g. BLAST) have been used to transfer such annotation information between organisms. We demonstrate that functional genomics can complement traditional sequence similarity to improve the transfer of gene annotations between organisms. Our method transfers annotations only when functionally appropriate as determined by genomic data and can be used with any prediction algorithm to combine transferred gene function knowledge with organism-specific high-throughput data to enable accurate function prediction. We show that diverse state-of-art machine learning algorithms leveraging functional knowledge transfer (FKT) dramatically improve their accuracy in predicting gene-pathway membership, particularly for processes with little experimental knowledge in an organism. We also show that our method compares favorably to annotation transfer by sequence similarity. Next, we deploy FKT with state-of-the-art SVM classifier to predict novel genes to 11,000 biological processes across six diverse organisms and expand the coverage of accurate function predictions to processes that are often ignored because of a dearth of annotated genes in an organism. Finally, we perform in vivo experimental investigation in Danio rerio and confirm the regulatory role of our top predicted novel gene, wnt5b, in leftward cell migration during heart development. FKT is immediately applicable to many bioinformatics techniques and will hel

关键词： Genome annotation Gene prediction machine learning Heart Gene ontologies machine learning algorithms Zebrafish Genetic networks

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WhatsNextApp: LSTM-Based Next-App Prediction With App Usage Sequences

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IEEE ACCESS 2022年 10卷 18233-18247页

作者： Katsarou, Katerina Yu, Geunhye Beierle, Felix Tech Univ Berlin Serv Ctr Networking D-10587 Berlin Germany Univ Wurzburg Inst Clin Epidemiol & Biometry D-97080 Wurzburg Germany

Next app prediction can help enhance user interface design, pre-loading of apps, and network optimizations. Prior work has explored this topic, utilizing multiple different approaches but challenges like the user cold-start problem, data sparsity, and privacy concerns related to contextual data like location histories, persist. The user cold-start problem occurs when a user has recently registered to the smartphone app system and there is not enough information about his/her preferences and his/her history of smartphone usage. In this work, we try to address the above issues. We introduce WhatsNextApp, an approach based on LSTM (Long Short-Term Memory) networks using sequences of app usage logs. Our approach is inspired by Word Embeddings and treats sequences of app usage logs as sequences of words. We collect a real-life data set consisting of 975 Android users with over 22 million app usage events. We build a generic (user-independent) WhatsNextApp model and the evaluation with our data set shows that it outperforms related studies for existing users where we achieve a recall@8 (recall for the top 8 apps) of 92%. For the user cold-start problem with the 500 most frequent apps, we achieve a recall@8 of 82.7%.

关键词： Markov processes Data models Predictive models Computational modeling Training data Prediction algorithms Licenses Human-centered computing smartphone machine learning algorithms LSTM

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An Integrated Novel Framework for Coping Missing Values Imputation and Classification

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IEEE ACCESS 2022年 10卷 69373-69387页

作者： Jena, Monalisa Dehuri, Satchidananda Fakir Mohan Univ Dept Comp Sci Balasore 756019 Odisha India

This work presents an integrated framework for imputation of missing values and prediction of class label of unseen samples by using the best features of rule based inductive decision tree (DT) and Support Vector machine (SVM) classifier (DT-SVM). In this work, the decision tree is used for imputation of missing values of the datasets containing both categorical and numerical valued attributes. In addition, some of the other popular and simple missing value imputation techniques like drop, mean, median, mode, and k-nearest neighbor (kNN) are used for a comparative analysis. The imputed datasets are then classified using SVM. The performance of the proposed integrated novel framework DT-SVM has been compared with Drop-SVM, Mean-SVM, Median-SVM, Mode-SVM, and kNN-SVM and it is found that DT-SVM outperforms others. Further, a new variant of kNN named it as approximated kNN (A-kNN) has been proposed to overcome some of the shortcomings of canonical kNN while learning from a training set imputed by DT. Unlike canonical kNN, A-kNN does not scan the entire training set. Instead, it processes some of the representative instances from the training dataset to identify the nearest neighbor. The class centroid approach is adopted to find the representative instances of the training set. The effectiveness in term of accuracy as well as computational time of A-kNN is examined by comparing with canonical kNN. It is found that computational time of the proposed A-kNN is drastically reduced as compared to canonical kNN without compromising with the classification accuracy.

关键词： Decision trees Support vector machines Correlation Training Data mining machine learning algorithms Licenses Classification data mining decision tree kNN classifier missing values imputation SVM

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