Model reduction for control systems with restricted complexity controllers

The problem of reduced-order modelling is considered in connection with the design of restricted complexity controllers. The suggested reduction method develops in two phases: (i) a simple frequency response of the overall feedback control system is determined according to the design specifications; (ii) a reduced-order transference of the controlled plant is obtained by solving a linear set of equations in such a way that its behaviour approximates that of the original plant at frequencies which are meaningful for the overall transfer function derived in the first step (e.g. resonance and cutoff frequencies). An example shows how the procedure yields a reduced-order model suitable for designing robust controllers whereas other standard methods, based on properties of the plant only, fail.     

Predicting future sedentary behaviour using wearable and mobile devices

Sedentarism is a common problem that can affect human health and wellbeing. Predicting sedentary behaviour is an emerging area that can benefit from data collected from sensors available in ubiquitous devices, such as wearables and smartphones. In this paper, we present an approach aiming at predicting the sedentary behaviour of a user from data collected from sensors installed in wearable/mobile devices. We compare personal and impersonal models using a real-life dataset consisting of sensing data of 48 users during 10 weeks. We found that impersonal models using Deep Neural Networks were able to accurately predict the subject’s future sedentary behaviour.     

Knowledge-aware sequence modelling with deep learning for online course recommendation

The recent boom in online courses has necessitated personalized online course recommendation. Modelling the learning sequences of users is key for course recommendation because the sequences contain the dynamic learning interests of the users. However, current course recommendation methods ignore heterogeneous course information and collective sequential dependency between courses when modelling the learning sequences. We thus propose a novel online course recommendation method based on knowledge graph and deep learning which models course information via a course knowledge graph and represents courses using TransD. It then develops a bidirectional long short-term memory network, convolutional neural network, and multi-layer perceptron for learning sequence modelling and course recommendation. A public dataset called MOOCCube was used to evaluate the proposed method. Experimental results show that: (1) employing the course knowledge graph in learning sequence modelling improves averagely the performance of our method by 13.658%, 16.42%, and 15.39% in terms of HR@K, MRR@K, and NDCG@K; (2) modelling the collective sequential dependency improves averagely the performance by 4.11%, 6.37%, and 5.47% in terms of the above metrics; and (3) our method outperforms popular methods with the course knowledge graph in most cases.     

An evaluation of document clustering and topic modelling in two online social networks: Twitter and Reddit

Methods for document clustering and topic modelling in online social networks (OSNs) offer a means of categorising, annotating and making sense of large volumes of user generated content. Many techniques have been developed over the years, ranging from text mining and clustering methods to latent topic models and neural embedding approaches. However, many of these methods deliver poor results when applied to OSN data as such text is notoriously short and noisy, and often results are not comparable across studies. In this study we evaluate several techniques for document clustering and topic modelling on three datasets from Twitter and Reddit. We benchmark four different feature representations derived from term-frequency inverse-document-frequency (tf-idf) matrices and word embedding models combined with four clustering methods, and we include a Latent Dirichlet Allocation topic model for comparison. Several different evaluation measures are used in the literature, so we provide a discussion and recommendation for the most appropriate extrinsic measures for this task. We also demonstrate the performance of the methods over data sets with different document lengths. Our results show that clustering techniques applied to neural embedding feature representations delivered the best performance over all data sets using appropriate extrinsic evaluation measures. We also demonstrate a method for interpreting the clusters with a top-words based approach using tf-idf weights combined with embedding distance measures.     

Looking deep in the eyes: Investigating interpretation methods for neural models on reading tasks using human eye-movement behaviour

This paper provides the first broad overview of the relation between different interpretation methods and human eye-movement behaviour across different tasks and architectures. The interpretation methods of neural networks provide the information the machine considers important, while the human eye-gaze has been believed to be a proxy of the human cognitive process. Thus, comparing them explains machine behaviour in terms of human behaviour, leading to improvement in machine performance through minimising their difference. We consider three types of natural language processing (NLP) tasks: sentiment analysis, relation classification and question answering, and four interpretation methods based on: simple gradient, integrated gradient, input-perturbation and attention, and three architectures: LSTM, CNN and Transformer. We leverage two corpora annotated with eye-gaze information: the Zuco dataset and the MQA-RC dataset. This research sets up two research questions. First, we investigate whether the saliency (importance) of input-words conform with those from human eye-gaze features. To this end, we compute a saliency distance (SD) between input words (by an interpretation method) and an eye-gaze feature. SD is defined as the KL-divergence between the saliency distribution over input words and an eye-gaze feature. We found that the SD scores vary depending on the combinations of tasks, interpretation methods and architectures. Second, we investigate whether the models with good saliency conformity to human eye-gaze behaviour have better prediction performances. To this end, we propose a novel evaluation device called “SD-performance curve” (SDPC) which represents the cumulative model performance against the SD scores. SDPC enables us to analyse the underlying phenomena that were overlooked using only the macroscopic metrics, such as average SD scores and rank correlations, that are typically used in the past studies. We observe that the impact of good saliency conformity between humans and machines on task performance varies among the combinations of tasks, interpretation methods and architectures. Our findings should be considered when introducing eye-gaze information for model training to improve the model performance.     

Multimodal data as a means to understand the learning experience

Most work in the design of learning technology uses click-streams as their primary data source for modelling & predicting learning behaviour. In this paper we set out to quantify what, if any, advantages do physiological sensing techniques provide for the design of learning technologies. We conducted a lab study with 251 game sessions and 17 users focusing on skill development (i.e., user's ability to master complex tasks). We collected click-stream data, as well as eye-tracking, electroencephalography (EEG), video, and wristband data during the experiment. Our analysis shows that traditional click-stream models achieve 39% error rate in predicting learning performance (and 18% when we perform feature selection), while for fused multimodal the error drops up to 6%. Our work highlights the limitations of standalone click-stream models, and quantifies the expected benefits of using a variety of multimodal data coming from physiological sensing. Our findings help shape the future of learning technology research by pointing out the substantial benefits of physiological sensing.     

季节交替或气温的突然变化对电气设备的影响

季节交替或气温的突然变化对大电流设备的接头影响很大,接头变松造成接触电阻变大,接头发热导致绝缘损坏相问短路,设备停运。本文重点介绍了我厂#5炉A磨煤机口线,因季节交替变化对接头影响的情况。分享对季节交替或气温的突然变化对大电流设备的接头影响的一些认识了解。     

地下水化学反应途径模拟方法──以地下水氯化物污染为例

比较了形态分布模拟和反应途径模拟的差异，总结了反应途径模拟的实现方法，对PH但算法进行了改进，作为实例，模拟了地下水氯上物污染过程。     

地表遥感蒸散发模型研究进展

蒸散发(Evapotranspiration,ET)包括植被蒸腾与土壤蒸发,是地表能量平衡与水量平衡的重要组成部分,也是陆面过程研究的关键参数。卫星遥感技术的兴起,使获取大尺度非均匀下垫面的地表特征参数成为可能,一系列旨在精确估算地表实际蒸散量的遥感模型由此应运而生,以满足局地、区域乃至全球尺度蒸散发估算的需求。通过分析净辐射通量和地表与近地面大气层湍流热通量交换的方式,将遥感蒸散发模型分为单层模型和双层模型,总结其模型原理,讨论典型模型的优缺点及适用条件,分析蒸散发反演存在的遥感模型精度的图像范围依赖性和分辨率依赖性、时间尺度扩展及不确定性、模型验证以及地表辐射温度和阻抗5个问题。指出今后应加强地表温度反演精度、时间尺度扩展、有云日地表蒸散发、地面观测等方面的研究。     

钢-混凝土组合结构的抗火性能研究进展

对钢-混凝土组合结构的抗火性能研究进展进行了回顾,总结了钢管混凝土柱、钢-混凝土组合楼盖体系、梁柱节点和整体结构抗火性能的最新研究成果,并对组合结构的抗火研究趋势进行了展望。     

Corporate decision-making for allocations to research and development

The analysis developed here suggests that conventional project based approaches to R & D budget determination do not provide a satisfactory explanation of the actual behaviour of typical large modern corporations. An alternative frame of reference for modelling such behaviour based on a gestalt interpretation is suggested and an examination conducted of differences in corporate R & D budgeting techniques described in a number of studies. It is suggested that the frame of reference developed here provides a useful explanation of differences in budgeting conventions.The interpretation developed is also used to analyse variation in industrial innovative activity. Studies in this area have frequently been based on the concept of rival firms matching each others R & D budgets. Models based on this concept have produced conflicting evidence in this respect. It is suggested here that an alternative approach based on a gestalt interpretation may provide a rather better explanation.     

Modelling of linear discrete-time systems using modified cauer continued fraction

An algorithm, amenable for programming on a digital computer, has been presented for the modelling of linear discrete-time systems, as an alternative to the procedure of Shamash (1). The transformations inherent in the procedure are easily accomplished by the synthetic division technique. With the use of modified Cauer form of continued fraction (MCF), the new method matches a set of both the time-moments and Markov parameters of the system and of the model, as in the procedure of Parthasarathy and Singh (2), giving a better approximation to the system response at all times. A distinct feature of the proposed algorithm compared with the earlier methods of discrete system reduction (1),(2), is that a number of reduced-order models are generated simultaneously; this allows scope for better selection in choosing the right model for system analysis and design.     

Complex adaptive filtering user profile using graphical models

This article explores how to develop complex data driven user models that go beyond the bag of words model and topical relevance. We propose to learn from rich user specific information and to satisfy complex user criteria under the graphical modelling framework. We carried out a user study with a web based personal news filtering system, and collected extensive user information, including explicit user feedback, implicit user feedback and some contextual information. Experimental results on the data set collected demonstrate that the graphical modelling approach helps us to better understand the complex domain. The results also show that the complex data driven user modelling approach can improve the adaptive information filtering performance. We also discuss some practical issues while learning complex user models, including how to handle data noise and the missing data problem.     

Phospholipid—polymer amphiphile hybrid assemblies and their interaction with macrophages

Recently, the combination of lipids and block copolymers has become an alternative to liposomes and polymersomes as nano-sized drug carriers. We synthesize novel block copolymers consisting of poly(cholesteryl acrylate) as the hydrophobic core and poly(N-isopropylacrylamide) (PNIPAAm) as the hydrophilic extensions. Their successful phospholipid-assisted assembly into vesicles is demonstrated using the evaporation-hydration method. The preserved thermo-responsive property of the lipid-polymer hybrids is shown by a temperature dependent adsorption behaviour of the vesicles to poly(l lysine) coated surfaces. As expected, the vesicle adsorption is found to be higher at elevated temperatures. The cellular uptake efficiency of hybrids is assessed using macrophages with applied shear stress. The amount of adhering macrophages is affected by the time and level of applied shear stress. Further, it is found that shorter PNIPAAm extensions lead to higher uptake of the assemblies by the macrophages with applied shear stress. No inherent cytotoxicity is observed at the tested conditions. Taken together, this first example of responsive lipid-polymer hybrids, and their positive biological evaluation makes them promising nano-sized drug carrier candidates.     

Minstrel-ODM: a basic office data model

Minstrel-ODM is a basic office data model that supports the filing and retrieval of office data. It is based on a modelling paradigm where office data is viewed as existing in the form of office objects and the modelling formalism is of the semantic (hierarchy) data model kind. In developing the model, ideas have been drawn from work by others on semantic data models and functional data models in particular. The model was developed as part of ESPRIT project 59 entitled “Models for Information Storage and Retrieval”. Currently the model is being implemented as a prototype, which will be used to investigate user interface techniques. Further research on office data models is planned and Minstrel-ODM will be modified and extended as appropriate.     

Sensitivity analysis of stamping processes using various friction models appropriate for non-stationary contact problems

This paper looks at the sensitivity of thickness to variation of friction. The models of friction used are: the classic Amontons-Coulomb; a nonlinear pressure-dependent model proposed by Wriggers, vu Van and Stein; and a velocity-dependent model proposed by Molinari, Estrin and Mercier. They are coded in FORTRAN for use with finite element program ABAQUS. The contact problem is then formulated in the total Lagrangian formulation for contact between an elastic-plastic body and rigid tools. The variational (weak) form of the formulation is given and this is discretized by finite element method. To test and compare the models, one common metal forming processes is simulated: deep drawing of a square-cup. The sensitivity graphs showing each of the three friction models together is given at the end. One other conclusion although not major part of this work is that Amonton-Coulomb is not the best model suited for contact conditions in metal forming processes, because Wriggers et al. model and Molinari et al. model provide better results for modelling bends and corners.     

Intrinsic partial linear models for manifold-valued data

This paper aims to propose an intrinsic partial linear modelling (IPLM) framework for characterizing the complex relationship between the response manifold-valued data and a set of explanatory variables such as age, education years, or gender. Such manifold value data are widespread in medical imaging, gesture recognition, computer vision, feature tracking, shape modeling, and others. Compared with most nonparametric and parametric models for manifold-valued data, our IPLM as a semi-parametric model contains both parametric and nonparametric components, leading to better adaptability, interpretation, fitting, and robustness. Furthermore, we propose an iterative estimation strategy to estimate unknown components in IPLM and use simulation experiments to display the performance of the proposed estimation methods. Finally, we apply the proposed IPLM to model the association between the brain subcortical region Corpus Callosum (CC) 2D shape and multiple covariates, such as age, gender, or disease diagnosis, show its wide application in estimating the continuous 2D shape trajectories and comparing the difference in different groups.     

Waiting for chiropody: contextual results from an ethnographic study of the information behaviour among attendees at community clinics

The importance of including contextual factors when studying information behaviour is illustrated using findings from a recent field study in which ethnographic methods and social network theory are used to investigate the flow of human services information (HSI) among nurses, the elderly and other individuals at community-based foot clinics. Four types of contextual factors are identified: physical environment, clinic activities, the nurse's situation, and the senior's situation. While each type of factor has particular implications for the participants' information behaviour, it is shown how the interaction of multiple factors led to the conceptualization of such notions as an information ground, which may be used for guiding further research on the flow of information in community settings. This concept of an information ground is further based on a social constructionist definition of human services information. The implications of the researcher's presence at the field site as an additional contextual factor are also discussed.     

两种统计降尺度模型在太湖流域的应用对比

本研究采用A1B情景下大气环流模式BCCR输出的大气环流预报因子,同时应用两种统计降尺度模型ASD(Automated Statistical Downscaling Model)和SDSM(Statistical Downscaling Model),预测太湖流域8个站点未来两个时期(2046年-2065年和2081年-2100年)的日最高、最低气温和降水。重点针对两种模型中的预报因子选择、模型率定和验证以及建立的未来气候变化情景结果,对比分析两种模型在太湖流域的适用性。结果表明:在预报因子选择方面,依靠人为主观判断的SDSM模型和提供自动预报因子选择方法的ASD模型选择的预报因子基本一致,因此ASD相对优化了预报因子的选择过程。在率定和验证效果上,两种模型对气温的模拟效果较好,对降水的模拟效果不佳,但是ASD的模拟效果要好于SDSM。在未来两个时期,两种模型模拟的未来最高和最低气温变化并不显著,但从多年平均来看,ASD模拟的气温略有上升,而SDSM模拟的气温略有下降;相较气温,未来降水变化较为显著,ASD模拟的降水量增幅明显高于SDSM模拟的降水增幅。综合考虑模拟的结果并结合他人的研究成果,认为ASD对太湖流域未来气候的模拟优于SDSM。