Ordered successive noise projection cancellation algorithm for dual lattice-reduction-aided MIMO detection

A novel nonlinear multi-input multi-output MIMO detection algorithm is proposed which is referred to as an ordered successive noise projection cancellation OSNPC algorithm. It is capable of improving the computation performance of the MIMO detector with the conventional ordered successive interference cancellation OSIC algorithm. In contrast to the OSIC in which the known interferences in the input signal vector are successively cancelled the OSNPC successively cancels the known noise projections from the decision statistic vector. Analysis indicates that the OSNPC is equivalent to the OSIC in error performance but it has significantly less complexity in computation.Furthermore when the OSNPC is applied to the MIMO detection with the preprocessing of dual lattice reduction DLR the computational complexity of the proposed OSNPC-based DLR-aided detector is further reduced due to the avoidance of the inverse of the reduced basis of the dual lattice in computation compared to that of the OSIC-based one. Simulation results validate the theoretical conclusions with regard to both the performance and complexity of the proposed MIMO detection scheme.

The continuous wavelet projections algorithm: A practical spectral-feature-mining approach for crop detection

Spectroscopy can be used for detecting crop characteristics. A goal of crop spectrum analysis is to extract effective features from spectral data for establishing a detection model. An ideal spectral feature set should have high sensitivity to target parameters but low information redundancy among features.However, feature-selection methods that satisfy both requirements are lacking. To address this issue,in this study, a novel method, the continuous wavelet projections algorithm(CWPA), was developed,which has advantages of both continuous wavelet analysis(CWA) and the successive projections algorithm(SPA) for generating optimal spectral feature set for crop detection. Three datasets collected for crop stress detection and retrieval of biochemical properties were used to validate the CWPA under both classification and regression scenarios. The CWPA generated a feature set with fewer features yet achieving accuracy comparable to or even higher than those of CWA and SPA. With only two to three features identified by CWPA, an overall accuracy of 98% in classifying tea plant stresses was achieved, and high coefficients of determination were obtained in retrieving corn leaf chlorophyll content(R^(2)= 0.8521)and equivalent water thickness(R^(2)= 0.9508). The mechanism of the CWPA ensures that the novel algorithm discovers the most sensitive features while retaining complementarity among features. Its ability to reduce the data dimension suggests its potential for crop monitoring and phenotyping with hyperspectral data.

A novel NIRS modelling method with OPLS-SPA and MIX-PLS for timber evaluation

The identification of timber properties is important for safe application.Near Infrared Spectroscopy(NIRS)technology is widely-used because of its simplicity,efficiency,and positive environmental attributes.However,in its application,weak signals are extracted from complex,overlapping and changing information.This study focused on the stability of NIR modeling.The Orthogonal Partial Least Squares(OPLS)and Successive Projections Algorithm(SPA)eliminates noise and extracts effective spectra,and an ensemble learning method MIX-PLS,is applied to establish the model.The elastic modulus of timber is taken as an example,and 201 wood samples of three species,Xylosmacongesta(Lour.)Merr.,Acer pictum subsp.mono,and Betula pendula,samples were divided into three groups to investigate modelling performance.The results show that OPLS can preprocess the near-infrared spectroscopy information according to the target object in the face of the system error and reduce errors to minimum.SPA finally selects 13 spectral bands,simplifies the NIR spectral data and improves model accuracy.The Pearson's correlation coefficient of Calibration(Rc)and the Pearson's correlation coefficient of Prediction(Rp)of Mix Partial Least Squares(MIX-PLS)were 0.95 and 0.90,and Root Mean Square Error of Calibration(RMSEC)and Root Mean Square Error of Prediction(RMSEP)are 2.075 and 6.001,respectively,which shows the model has good generalization abilities.

Relationship between Project Consultants’ Performance and Project Success in the Rwandan Construction Industry

As discovered, today’s Rwandan construction industry is developing day by day, which plays a very important role in the country’s economic growth. In the construction industry, project consultants play a very significant role in providing services for such projects. The project consultants provide services from the beginning of the project to the completion of the project. Using project consultants is very useful for construction projects because it can ameliorate project efficiency and effectiveness. The main purpose of this paper is to investigate the relationship between project consultants’ performance and project success in the Rwandan construction industry. The data used in this study were obtained from primary and secondary sources. The secondary data was attained through an elaborated literature review of various books, articles, and papers related to this research to outlining and describing the chief ideas of this research title. The primary data was compiled through a questionnaire survey that was directed to 110 selected professionals in the construction projects in Rwanda to collect data from the site for statistical analysis of the research to test the hypothesis. However, a total of 90 usable responses were received within the scheduled period representing the response rate of 81.82%, which is likely to be representative and acceptable. Data collected from the questionnaire surveys were analyzed using the Statistical Package for Social Scientists (SPSS), excel spreadsheets, and Relative Importance Index (RII), which provide more merit presentations. The survey results show that even if there are many obstacles in the use of project consultants in the Rwandan construction industry such as lack of knowledge and practice in project consulting, lack of well-trained project consulting professionals, lack of training opportunities in project consulting, lack of knowledge and experience in addition to the senior management opposition, and lack of local project consulting guidelines and information;they are needed in the construction project to make it more successful through reducing and saving the overall project’s life cycle cost according to client’s wishes, keeping time of construction project, improving quality of the project products in the present and future, removing major variations that affect construction project with its attendant cost overrun, and advising on construction project process. Therefore, it is more important to remark that the good performance of the project consultants in any industry especially in the construction industry will contribute to the successful implementation of the project. From the results of the study, the performance of project consultants is closely related to the success of the construction industry in Rwanda. The project consultants’ leading skills and knowledge automatically guide the project to complete with accurate time, budget, and quality to make the project successful. In this paper, we will also consider the project success criteria;the role and responsibility of project consultants;the factors affecting the performance of project consultants;and the reasons that hinder the implementation of project consulting in the Rwandan construction industry.

The Relationship between Financial Reporting System and Project Success: A Case of Construction Projects in UAE

The purpose of this paper is to deliver a better perception of the project success,and how reporting system might increase the probability of project success in construction projects in UAE.Further,it aims at exploring the relationship between effective reporting system in terms of the characteristic of its outputs(mainly effectiveness of financial reporting system)and project success.Semi-structured interviews with a number of interior auditors,accountants,and chief financial officers(CFOs)from different corporations in construction sector in UAE in order to recognize how effective reporting system in terms of the characteristic of its outputs(broad scope,timeliness,aggregation,and integration)might contribute to increase the probability of project success.There are several success factors in construction projects in UAE,thus it is very hard to capture all these factors in one paper.Hence,this paper is not considering all project success factors rather it focuses on the characteristic of outputs of the reporting system generally and the financial reporting system particularly(broad scope,timeliness,aggregation,and integration),as most of the studies in the literature considered the reporting system as a key factor of project success.This paper adds to both project management and accounting research by evidencing results from an exploration how effective reporting might impact the project success,with valuable implications for standard officials,customers,investors,stakeholders,sponsors,shareholders,CFOs,project developer,consultants,internal auditors,and accounting academics.The effective reporting system in United Arab Emirates(UAE)construction projects enables possessors,customers,and contractors of projects to get timely information about the progress of project in a brief and significant format which in turn improve the decision-making process and contribute to project success.This paper implies a contribution for both project management literature and accounting research by investigative the effectiveness of reporting system in project successes from historical point of view and contemporary point of view.

A Successful Integrated Project

In recent years,the Juxian County Family Planning Association of Shandong Province has vigorously initiated various income-generating activities and achieved encouraging results in integrating the family plan-ning programme with the development of the rural cconomny,improving living standards and building up the mental civilization of the people.

Successful Completion of AI-Ahmadi Refinery Restoration Project in Kuwait

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Sino-Japanese Cooperation Project in Tarim Basin Successfully Wound up

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BP神经网络结合变量选择方法在牛奶蛋白质含量检测中的应用

牛奶中的蛋白质含量会影响牛奶的品质,利用高光谱图像的光谱特征信息研究对牛奶蛋白质含量预测的可行性。本文提出一种基于竞争性自适应重加权算法(competitive adaptive reweighted sampling, CARS)和连续投影算法(successive projections algorithm, SPA)结合多层前馈神经网络(back propagation, BP)的预测建模方法,实验以含有不同浓度蛋白质的牛奶为对象,利用可见光/近红外高光谱成像系统共采集到5种牛奶共计250组高光谱数据,通过实验对比选择采用标准化方法对获取到的吸收光谱预处理,然后采用CARS结合SPA筛选特征波长,得到18个特征波长,建立CARS-SPA-BP模型,经过试验,CARS-SPA-BP模型的训练集决定系数和测试集决定系数R;和R;分别达到0.971和0.968,训练集均方根误差(root mean square error of calibration,RMSEC)和测试集均方根误差(root mean square error of prediction,RMSEP)达到了0.033和0.034。研究发现,采用CARS结合SPA筛选的牛奶特征波长建立的多层前馈神经网络模型,其模型预测结果与全波长建模相比并没有明显降低,因此将CARS结合SPA用于波长筛选并且结合BP神经网络基本可以完成对牛奶蛋白质含量的预测。为验证CARS-SPA-BP模型的预测能力,在相同数据环境下,使用较为传统的偏最小二乘回归(partial least squares regression, PLSR)进行建模,实验结果表明,CARS-SPA-BP相较于PLSR,R;和RMSEP均有明显提升。研究表明,CARS-SPA-BP可充分利用牛奶光谱特征信息实现较高精度的牛奶蛋白质含量检测。

A variable differential consensus method for improving the quantitative near-infrared spectroscopic analysis

Consensus methods have presented promising tools for improving the reliability of quantitative models in near-infrared(NIR) spectroscopic analysis.A strategy for improving the performance of consensus methods in multivariate calibration of NIR spectra is proposed.In the approach,a subset of non-collinear variables is generated using successive projections algorithm(SPA) for each variable in the reduced spectra by uninformative variables elimination(UVE).Then sub-models are built using the variable subsets and the calibration subsets determined by Monte Carlo(MC) re-sampling,and the sub-model that produces minimal error in cross validation is selected as a member model.With repetition of the MC re-sampling,a series of member models are built and a consensus model is achieved by averaging all the member models.Since member models are built with the best variable subset and the randomly selected calibration subset,both the quality and the diversity of the member models are insured for the consensus model.Two NIR spectral datasets of tobacco lamina are used to investigate the proposed method.The superiority of the method in both accuracy and reliability is demonstrated.

Nondestructive determination of IMP content in chilled chicken based on hyperspectral data combined with chemometrics

This study was conducted to investigate the potential of hyperspectral imaging technique(900-1700 nm)for nondestructive determination of inosinic acid(IMP)in chicken.Hyperspectral images of chicken flesh samples were acquired,and their mean spectra within the images were extracted.The quantitative relationship between the mean spectra and reference IMP value was fitted by partial least squares(PLS)regression algorithm.A PLS model(MAS-PLS)built with moving average smoothing(MAS)spectra showed better performance in predicting IMP content,leading to correlation coefficients(RP)of 0.951,root mean square error(RMSEP)of 0.046 mg/g,and residual predictive deviation(RPD)of 3.152.Regression coefficient(RC),successive projections algorithm(SPA),stepwise,competitive adaptive reweighted sampling(CARS),and uninformative variable elimination(UVE)were used to select the optimal wavelengths to optimize the MAS-PLS model.Based on the 18 optimal wavelengths(907.14,917.02,918.67,926.90,930.20,936.78,956.54,1004.28,1135.89,1211.56,1302.07,1367.94,1397.60,1488.31,1680.17,1683.49,1686.80 and 1695.10 nm)selected from MAS spectra by SPA,the MAS-SPA-PLS model was built with R_(P) of 0.920,RMSEP of 0.056 mg/g and RPD of 3.220,which was similar to the MAS-PLS model.The overall study indicated that hyperspectral imaging in the 900-1700 nm range combined with PLS and SPA could be used to predict the IMP content in chicken flesh.

Identifying the driving factors of successful megaproject construction management:Findings from three Chinese cases

The construction of megaprojects has always resulted in extensive and long-term impacts on the society.However,the performance of megaproject management is poor,and improving it remains an urgent and necessary issue.Although many studies on megaproject success have been conducted,existing studies on the driving factors of successful megaproject construction are rather limited.Therefore,this study aims to systematically explore the key factors that can lead to successful megaproject construction management based on three cases:The Beijing-Shanghai High-Speed Railway,the Three Gorges Dam,and the Hong Kong-Zhuhai-Macao Bridge.Mixed research methods,such as literature review,case studies,and expert interviews,were used in this study.Consequently,11 driving factors,namely,government support,public support,accumulation and application of technology and experience,development and innovation of technology,innovation and application of management system,organizational mode and structure,top management support,project culture,megaproject citizenship behavior,corporate reputation,and fulfillment of social responsibilities,were identified and grouped into five categories,namely,project environment,construction capabilities,organization,positive culture and behavior,and requirements for sustainable development.The contributions of this study lie in two aspects.First,the driving factors of successful megaproject construction are identified to deepen the understanding of industrial practitioners,assist them in focusing on key factors,and aid them in effectively managing megaprojects.Second,researchers could use the identified driving factors in conducting further empirical studies and apply them in future projects to enhance their chances of success.

An Inquiry into Success Factors for Post-disaster Housing Reconstruction Projects: A Case of Kerala, South India

The 2004 Indian Ocean Tsunami triggered significant destruction to housing and related infrastructures across various coastal districts of south India.Research shows that tsunami reconstruction projects in Kerala experienced different degrees of success and failure.On this background,this study explored factors that contributed to the successful implementation of tsunami housing projects in Kerala by(1)consolidating various critical success factors(CSFs)for post-disaster reconstruction(PDR)projects under‘‘project management success traits’’through content analysis of existing literature;(2)deriving a conceptual model that envisages project success in PDR contexts;and(3)assessing the impacts of those success traits on tsunami housing projects using confirmatory factor analysis.Necessary data were gathered through a survey of various stakeholders involved in tsunami reconstruction projects in Kerala using structured questionnaires.The research revealed that PDR project success is attributed to critical dimensions of project management such as institutional mechanisms,reconstruction strategies,project implementation,and stakeholder management.A conceptual model with the interplay of project success,success traits,as well as their CSFs identified the project management actions that must be monitored during reconstruction.Since the project management approach is widely recognized for PDR projects,these success traits hold huge potential for effective organization and management of housing reconstruction projects.The study also helped to identify project management traits that need improvements for the successful implementation of post-disaster housing projects in Kerala.Thus the research findings can serve as a foundational study for formulating project management strategies appropriate to PDR projects in Kerala.

Application of a new SPA-SVM coupling method for QSPR study of electrophoretic mobilities of some organic and inorganic compounds

In this work, two chemometrics methods are applied for the modeling and prediction of electrophoretic mobilities of some organic and inorganic compounds. The successive projection algorithm, feature selection （SPA） strategy, is used as the descriptor selection and model development method. Then, the support vector machine （SVM） and multiple linear regression （MLR） model are utilized to construct the non-linear and linear quantitative structure-property relationship models. The results obtained using the SVM model are compared with those obtained using MLR reveal that the SVM model is of much better predictive value than the MLR one. The root-mean-square errors for the training set and the test set for the SVM model were 0.1911 and 0.2569, respectively, while by the MLR model, they were 0.4908 and 0.6494, respectively. The results show that the SVM model drastically enhances the ability of prediction in QSPR studies and is superior to the MLR model.

Measuring project performance by applying social network analyses

It is often argued that the core of organizational success is efficient collaboration.Some authors even posit that efficient collaboration is more important to organizational innovation and performance than individual skills or expertise.However,the lack of efficient models to manage collaboration properly is a major constraint for organizations to profit from internal and external collaborative initiatives.Currently,much of the collaboration in organizations occurs through virtual network channels,such as e-mail,Yammer,Jabber,Microsoft Teams,Skype,and Zoom.These are even more important in situations where different time zones and even threats of a pandemic constrain face-to-face human interactions.This work introduces a multidisciplinary heuristic model developed based on project risk management and social network analysis centrality metrics graph-theory to quantitatively measure dynamic organizational collaboration in the project environment.A case study illustrates the proposed model’s implementation and application in a real virtual project organizational context.The major benefit of applying this proposed model is that it enables organizations to quantitatively measure different collaborative,organizational,and dynamic behavioral patterns,which can later correlate with organizational outcomes.The model analyzes three collaborative project dimensions:network collaboration cohesion evolution,network collaboration degree evolution,and network team set variability evolution.This provides organizations an innovative approach to understand and manage possible collaborative project risks that may emerge as projects are delivered.Organizations can use the proposed model to identify projects’critical success factors by comparing successful and unsuccessful delivered projects’dynamic behaviors if a substantial number of both project types are analyzed.The proposed model also enables organizations to make decisions with more information regarding the support for changes in observed collaborative patterns as demonstrated by statistical models in general,and linear regressions in particular.Further,the proposed model provides organizations with a completely bias-free data-collection process that eliminates organizational downtime.Finally,applying the proposed model in organizations will reduce or eliminate the risks associated with virtual collaborative dynamics,leading to the optimized use of resources;this will transform organizations to become more lean-oriented and significantly contribute to economic,social,and environmental global sustainability.

Application of the Level-Set Model with Constraints in Image Segmentation

We proposeand analyze a constrained level-set method forsemi-automatic image segmentation.Our level-set model with constraints on the level-set function enables us to specify which parts of the image lie inside respectively outside the segmented objects.Such a-priori information can be expressed in terms of upper and lower constraints prescribed for the level-set function.Constraints have the same conceptual meaning as initial seeds of the popular graph-cuts based meth-ods for image segmentation.A numerical approximation scheme is based on the complementary-finite volumes method combined with the Projected successive over-relaxation method adopted for solving constrained linear complementarity prob-lems.The advantage of the constrained level-set method is demonstrated on several artificial images as well as on cardiac MRI data.