An integrated approach for real-time model-based state-of-charge estimation of lithium-ion batteries

Lithium-ion batteries have been widely adopted in electric vehicles (EVs), and accurate state of charge (SOC) estimation is of paramount importance for the EV battery management system. Though a number of methods have been proposed, the SOC estimation for Lithium-ion batteries, such as LiFePo4 battery, however, faces two key challenges: the flat open circuit voltage (OCV) vs SOC relationship for some SOC ranges and the hysteresis effect. To address these problems, an integrated approach for real-time model-based SOC estimation of Lithium-ion batteries is proposed in this paper. Firstly, an auto-regression model is adopted to reproduce the battery terminal behaviour, combined with a non-linear complementary model to capture the hysteresis effect. The model parameters, including linear parameters and non-linear parameters, are optimized off-line using a hybrid optimization method that combines a meta-heuristic method (i.e., the teaching learning based optimization method) and the least square method. Secondly, using the trained model, two real-time model-based SOC estimation methods are presented, one based on the real-time battery OCV regression model achieved through weighted recursive least square method, and the other based on the state estimation using the extended Kalman filter method (EKF). To tackle the problem caused by the flat OCV-vs-SOC segments when the OCV-based SOC estimation method is adopted, a method combining the coulombic counting and the OCV-based method is proposed. Finally, modelling results and SOC estimation results are presented and analysed using the data collected from LiFePo4 battery cell. The results confirmed the effectiveness of the proposed approach, in particular the joint-EKF method.

A floating point CORDIC based SVD processor

An SVD processor system is presented in which each processing element is implemented using a simple CORDIC unit. The internal recursive loop within the CORDIC module is exploited, with pipelining being used to multiplex the two independent micro-rotations onto a single CORDIC processor. This leads to a high performance and efficient hardware architecture. In addition, a novel method for scale factor correction is presented which only need be applied once at the end of the computation. This also reduces the computation time. The net result is an SVD architecture based on a conventional CORDIC approach, which combines high performance with high silicon area efficiency.

Two-Stage Orthogonal Least Squares Methods for Neural Network Construction

A number of neural networks can be formulated as the linear-in-the-parameters models. Training such networks can be transformed to a model selection problem where a compact model is selected from all the candidates using subset selection algorithms. Forward selection methods are popular fast subset selection approaches. However, they may only produce suboptimal models and can be trapped into a local minimum. More recently, a two-stage fast recursive algorithm (TSFRA) combining forward selection and backward model refinement has been proposed to improve the compactness and generalization performance of the model. This paper proposes unified two-stage orthogonal least squares methods instead of the fast recursive-based methods. In contrast to the TSFRA, this paper derives a new simplified relationship between the forward and the backward stages to avoid repetitive computations using the inherent orthogonal properties of the least squares methods. Furthermore, a new term exchanging scheme for backward model refinement is introduced to reduce computational demand. Finally, given the error reduction ratio criterion, effective and efficient forward and backward subset selection procedures are proposed. Extensive examples are presented to demonstrate the improved model compactness constructed by the proposed technique in comparison with some popular methods.

Synchrophasor-Based Islanding Detection for Distributed Generation Systems Using Systematic Principal Component Analysis Approaches

Systematic principal component analysis (PCA) methods are presented in this paper for reliable islanding detection for power systems with significant penetration of distributed generations (DGs), where synchrophasors recorded by Phasor Measurement Units (PMUs) are used for system monitoring. Existing islanding detection methods such as Rate-of-change-of frequency (ROCOF) and Vector Shift are fast for processing local information, however with the growth in installed capacity of DGs, they suffer from several drawbacks. Incumbent genset islanding detection cannot distinguish a system wide disturbance from an islanding event, leading to mal-operation. The problem is even more significant when the grid does not have sufficient inertia to limit frequency divergences in the system fault/stress due to the high penetration of DGs. To tackle such problems, this paper introduces PCA methods for islanding detection. Simple control chart is established for intuitive visualization of the transients. A Recursive PCA (RPCA) scheme is proposed as a reliable extension of the PCA method to reduce the false alarms for time-varying process. To further reduce the computational burden, the approximate linear dependence condition (ALDC) errors are calculated to update the associated PCA model. The proposed PCA and RPCA methods are verified by detecting abnormal transients occurring in the UK utility network.

, Lexicographic Preferences in Discrete Choice Experiments: Consequences on Individual-Specific Willingness to Pay Estimates

In discrete choice experiments respondents are generally assumed to consider all of the attributes across each of the alternatives, and to choose their most preferred. However, results in this paper indicate that many respondents employ simplified lexicographic decision-making rules, whereby they have a ranking of the attributes, but their choice of an alternative is based solely on the level of their most important attribute(s). Not accounting for these simple decision-making heuristics introduces systemic errors and leads to biased point estimates, as they are a violation of the continuity axiom and a departure from the use of compensatory decision-making. In this paper the implications of lexicographic preferences are examined. In particular, using a mixed logit specification this paper investigates the sensitivity of individual-specific willingness to pay (WTP) estimates conditional on whether lexicographic decision-making rules are accounted for in the modelling of discrete choice responses. Empirical results are obtained from a discrete choice experiment that was carried out to address the value of a number of rural landscape attributes in Ireland

Content Validity in Landscape Valuation: A transdisciplinary approach to valuing the landscape of the peninsula of Sorrento, Italy.

This paper addresses the representation of landscape complexity in stated preferences research. It integrates landscape ecology and landscape economics and conducts the landscape analysis in a three-dimensional space to provide ecologically meaningful quantitative landscape indicators that are used as variables for the monetary valuation of landscape in a stated preferences study. Expected heterogeneity in taste intensity across respondents is addressed with a mixed logit model in Willingness to Pay space. The results suggest that the integration of landscape ecology metrics in a stated preferences model provides useful insights for valuing landscape and landscape changes

Integrin genetic variants and stage-specific tumor recurrence in patients with stage II and III colon cancer

Integrins (ITGs) are key elements in cancer biology, regulating tumor growth, angiogenesis and lymphangiogenesis through interactions of the tumor cells with the microenvironment. Moving from the hypothesis that ITGs could have different effects in stage II and III colon cancer, we tested whether a comprehensive panel of germline single-nucleotide polymorphisms (SNPs) in ITG genes could predict stage-specific time to tumor recurrence (TTR). A total of 234 patients treated with 5-fluorouracil-based chemotherapy at the University of Southern California were included in this study. Whole-blood samples were analyzed for germline SNPs in ITG genes using PCR-restriction fragment length polymorphism or direct DNA sequencing. In the multivariable analysis, stage II colon cancer patients with at least one G allele for ITGB3 rs4642 had higher risk of recurrence (hazard ratio (HR)=4.027, 95% confidence interval (95% CI) 1.556-10.421, P=0.004). This association was also significant in the combined stage II-III cohort (HR=1.975, 95% CI 1.194-3.269, P=0.008). The predominant role of ITGB3 rs4642 in stage II diseases was confirmed using recursive partitioning, showing that ITGB3 rs4642 was the most important factor in stage II diseases. In contrast, in stage III diseases the combined analysis of ITGB1 rs2298141 and ITGA4 rs7562325 allowed to identify three distinct prognostic subgroups (P=0.009). The interaction between stage and the combined ITGB1 rs2298141 and ITGA4 rs7562325 on TTR was significant (P=0.025). This study identifies germline polymorphisms in ITG genes as independent stage-specific prognostic markers for stage II and III colon cancer. These data may help to select subgroups of patients who may benefit from ITG-targeted treatments.

Common cancer stem cell gene variants predict colon cancer recurrence

PURPOSE: Recent evidence suggests that cancer stem cells (CSC) are responsible for key elements of colon cancer progression and recurrence. Germline variants in CSC genes may result in altered gene function and/or activity, thereby causing interindividual differences in a patient's tumor recurrence capacity and chemoresistance. We investigated germline polymorphisms in a comprehensive panel of CSC genes to predict time to tumor recurrence (TTR) in patients with stage III and high-risk stage II colon cancer.

EXPERIMENTAL DESIGN: A total of 234 patients treated with 5-fluorouracil-based chemotherapy at the University of Southern California were included in this study. Whole blood samples were analyzed for germline polymorphisms in genes that have been previously associated with colon CSC (CD44, Prominin-1, DPP4, EpCAM, ALCAM, Msi-1, ITGB1, CD24, LGR5, and ALDH1A1) by PCR-RFLP or direct DNA-sequencing.

RESULTS: The minor alleles of CD44 rs8193 C>T, ALCAM rs1157 G>A, and LGR5 rs17109924 T>C were significantly associated with increased TTR (9.4 vs. 5.4 years; HR, 0.51; 95% CI: 0.35-0.93; P = 0.022; 11.3 vs. 5.7 years; HR, 0.56; 95% CI: 0.33-0.94; P = 0.024, and 10.7 vs. 5.7 years; HR, 0.33; 95% CI: 0.12-0.90; P = 0.023, respectively) and remained significant in the multivariate analysis stratified by ethnicity. In recursive partitioning, a specific gene variant profile including LGR5 rs17109924, CD44 rs8193, and ALDH1A1 rs1342024 represented a high-risk subgroup with a median TTR of 1.7 years (HR, 6.71, 95% CI: 2.71-16.63, P < 0.001).

CONCLUSION: This is the first study identifying common germline variants in colon CSC genes as independent prognostic markers for stage III and high-risk stage II colon cancer patients.

Participation of Smallholders in Agroforestry Agri-Environmental Scheme: A lesson from the rural Mount Kenya region

The likelihood of smallholder farmers not participating in agroforestry agri-environmental schemes and payments for ecosystem services (PES) may be due to limited farmland endowment and formal credit constraints. These deficits may lead to an ‘exclusive club’ of successful farmers, which are not necessarily poor, enjoying the benefits of agri-environmental schemes and PES although agrienvironmental schemes and PES have been devised as a means of fostering rural sustainable development and improving the livelihood of poor smallholder farmers. Smallholder farmers in parts of rural Kenya continue to enroll in ‘The International Small Group Tree Planting Programme’ (TIST), an agri-environmental scheme, promoting agroforestry, carbon sequestration and conservation agriculture (CA). The question remains if these farmers are really poor? This study examines factors that determine the participation of smallholder farmers in TIST in parts of rural Kenya. We use survey data compiled in 2013 on 210 randomly selected smallholder farmers from Embu, Meru and Nanyuki communities; the sample consists of TIST and non-TIST members. A random utility model and logit regression were used to test a set of non-monetary and monetary factors that influence participation in the TIST. The utility function is conceptualized to give non-monetary factors, particularly the common medium of communication in rural areas – formal and informal – a central role. Furthermore, we investigate other factors (incl. credit accessibility and interest rate) that reveal the nature of farmers participating in TIST. The findings suggest that spread of information via formal and informal networks is a major driver of participation in the TIST program. Furthermore, variables such credit constrains, age and labour supply positively correlate with TIST participation, while for education the opposite is true. It is important to mention that these correlations, although somewhat consistent, were all found to be weak. The results indicate that participation in the TIST program is not influenced by farm size; therefore we argue that the TIST scheme is NOT an ‘exclusive club’ comprising wealthy and successful farmers. Older farmers’ being more likely to join the TIST is an argument for their long- rather than widely assumed short-term planning horizon and a new contribution to the literature. Given the importance of poverty alleviation and climate smart agriculture in developing countries, sustainable policy should strengthening the social and human capital as well as informal networks in rural areas. Extension services should effectively communicate benefits to less educated and credit constrained farmers.

A Fast Algorithm for Sparse Support Vector Machines for Mobile Computing Applications

This research presents a fast algorithm for projected support vector machines (PSVM) by selecting a basis vector set (BVS) for the kernel-induced feature space, the training points are projected onto the subspace spanned by the selected BVS. A standard linear support vector machine (SVM) is then produced in the subspace with the projected training points. As the dimension of the subspace is determined by the size of the selected basis vector set, the size of the produced SVM expansion can be specified. A two-stage algorithm is derived which selects and refines the basis vector set achieving a locally optimal model. The model expansion coefficients and bias are updated recursively for increase and decrease in the basis set and support vector set. The condition for a point to be classed as outside the current basis vector and selected as a new basis vector is derived and embedded in the recursive procedure. This guarantees the linear independence of the produced basis set. The proposed algorithm is tested and compared with an existing sparse primal SVM (SpSVM) and a standard SVM (LibSVM) on seven public benchmark classification problems. Our new algorithm is designed for use in the application area of human activity recognition using smart devices and embedded sensors where their sometimes limited memory and processing resources must be exploited to the full and the more robust and accurate the classification the more satisfied the user. Experimental results demonstrate the effectiveness and efficiency of the proposed algorithm. This work builds upon a previously published algorithm specifically created for activity recognition within mobile applications for the EU Haptimap project [1]. The algorithms detailed in this paper are more memory and resource efficient making them suitable for use with bigger data sets and more easily trained SVMs.

Stated preference methods and landscape ecology indicators: an example of transdisciplinarity in landscape economic valuation

This paper addresses the representation of landscape complexity in stated preferences research. It integrates landscape ecology and landscape economics and conducts the landscape analysis in a three-dimensional space to provide ecologically meaningful quantitative landscape indicators that are used as variables for the monetary valuation of landscape in a stated preferences study. Expected heterogeneity in taste intensity across respondents is addressed with a mixed logit model in Willingness to Pay space. Our methodology is applied to value, in monetary terms, the landscape of the Sorrento Peninsula in Italy, an area that has faced increasing pressure from urbanization affecting its traditional horticultural, herbaceous, and arboreal structure, with loss of biodiversity, and an increasing risk of landslides. We find that residents of the Sorrento Peninsula would prefer landscapes characterized by large open views and natural features. Residents also appear to dislike heterogeneous landscapes and the presence of lemon orchards and farmers' stewardship, which are associated with the current failure of protecting the traditional landscape. The outcomes suggest that the use of landscape ecology metrics in a stated preferences model may be an effective way to move forward integrated methodologies to better understand and represent landscape and its complexity.

Recent developments in the Thomson Parabola Spectrometer diagnostic for laser-driven multi-species ion sources

Ongoing developments in laser-driven ion acceleration warrant appropriate modifications to the standard Thomson Parabola Spectrometer (TPS) arrangement in order to match the diagnostic requirements associated to the particular and distinctive properties of laser-accelerated beams. Here we present an overview of recent developments by our group of the TPS diagnostic aimed to enhance the capability of diagnosing multi-species high-energy ion beams. In order to facilitate discrimination between ions with same Z / A , a recursive differential filtering technique was implemented at the TPS detector in order to allow only one of the overlapping ion species to reach the detector, across the entire energy range detectable by the TPS. In order to mitigate the issue of overlapping ion traces towards the higher energy part of the spectrum, an extended, trapezoidal electric plates design was envisaged, followed by its experimental demonstration. The design allows achieving high energy-resolution at high energies without sacrificing the lower energy part of the spectrum. Finally, a novel multi-pinhole TPS design is discussed, that would allow angularly resolved, complete spectral characterization of the high-energy, multi-species ion beams.