Stratified Transfer Learning for Cross-domain Activity Recognition

In activity recognition, it is often expensive and time-consuming to acquire sufficient activity labels. To solve this problem, transfer learning leverages the labeled samples from the source domain to annotate the target domain which has few or none labels. Existing approaches typically consider learning a global domain shift while ignoring the intra-affinity between classes, which will hinder the performance of the algorithms. In this paper, we propose a novel and general cross-domain learning framework that can exploit the intra-affinity of classes to perform intra-class knowledge transfer. The proposed framework, referred to as Stratified Transfer Learning (STL), can dramatically improve the classification accuracy for cross-domain activity recognition. Specifically, STL first obtains pseudo labels for the target domain via majority voting technique. Then, it performs intra-class knowledge transfer iteratively to transform both domains into the same subspaces. Finally, the labels of target domain are obtained via the second annotation. To evaluate the performance of STL, we conduct comprehensive experiments on three large public activity recognition datasets~(i.e. OPPORTUNITY, PAMAP2, and UCI DSADS), which demonstrates that STL significantly outperforms other state-of-the-art methods w.r.t. classification accuracy (improvement of 7.68%). Furthermore, we extensively investigate the performance of STL across different degrees of similarities and activity levels between domains. And we also discuss the potential of STL in other pervasive computing applications to provide empirical experience for future research.Comment: 10 pages; accepted by IEEE PerCom 2018; full paper. (camera-ready version

Numerical study on the effects of intake charge on oxy-fuel combustion in a dual-injection spark ignition engine at economical oxygen-fuel ratios

In order to achieve carbon neutrality by decreasing Carbon Dioxide (CO2) emissions, Oxy-Fuel Combustion (OFC) technology with Carbon Capture and Storage (CCS) is becoming a hot topic in the field of Internal Combustion Engine (ICE). However, almost no research has been reported about the implementation of OFC in dual-injection Spark Ignition (SI) engine. This article presents a numerical study about the effects of Water Injection (WI) strategies on OFC in a dual-injection Spark Ignition (SI) engine, with Gasoline Direct Injection (GDI), Port Fuel Injection (PFI) and P50-G50 (50% PFI and 50% GDI) three injection strategies. The results show that compared to Conventional Air Combustion (CAC), there is a significant increase in BSFC under OFC. θ_F is significantly prolonged, and the spark timing is obviously advanced. The θ_C of PFI is a bit shorter than that of GDI and P50-G50. There is a small benefit to BSFC under low R_wf. However, with the further increase of R_wf from 0.2 to 0.9, there is an increment of 4.29%, 3.6% and 3.77% in BSFC for GDI, P50-G50 and PFI, respectively. As t_WI postpones to around -30 °CA under the conditions of R_wf ≥ 0.8, BSFC has a sharp decrease of more than 6 g/kWh, and this decline is more evident under GDI injection strategy. The variation of Pmax and φ_CA50 is less affected by T_WI compared to the effects of R_wf or t_WI. BSFC just has a small decline with the increase of T_WI from 298K to 368K regardless of the injection strategy. Consequently, it is feasible to implement appropriate WI strategies to control OFC characteristics in a dual-injection SI engine, but the benefit in fuel economy is limited

Selection of Optimal Ancestry Informative Markers for Classification and Ancestry Proportion Estimation in Pigs

Using small sets of ancestry informative markers (AIMs) constitutes a cost-effective method to accurately estimate the ancestry proportions of individuals. This study aimed to generate a small and effective number of AIMs from ∼60 K single nucleotide polymorphism (SNP) data of porcine and estimate three ancestry proportions [East China pig (ECHP), South China pig (SCHP), and European commercial pig (EUCP)] from Asian breeds and European domestic breeds. A total of 186 samples of 10 pure breeds were divided into three groups: ECHP, SCHP, and EUCP. Using these samples and a one-vs.-rest SVM classifier, we found that using only seven AIMs could completely separate the three groups. Subsequently, we utilized supervised ADMIXTURE to calculate ancestry proportions and found that the 129 AIMs performed well on ancestry estimates when pseudo admixed individuals were used. Furthermore, another 969 samples of 61 populations were applied to evaluate the performance of the 129 AIMs. We also observed that the 129 AIMs were highly correlated with estimates using ∼60 K SNP data for three ancestry components: ECHP (Pearson correlation coefficient (r) = 0.94), SCHP (r = 0.94), and EUCP (r = 0.99). Our results provided an example of using a small number of pig AIMs for classifications and estimating ancestry proportions with high accuracy and in a cost-effective manner

The VHL/HIF Axis in the Development and Treatment of Pheochromocytoma/Paraganglioma

Pheochromocytomas and paragangliomas (PPGLs) are rare neuroendocrine tumors originating from chromaffin cells in the adrenal medulla (PCCs) or extra-adrenal sympathetic or parasympathetic paraganglia (PGLs). About 40% of PPGLs result from germline mutations and therefore they are highly inheritable. Although dysfunction of any one of a panel of more than 20 genes can lead to PPGLs, mutations in genes involved in the VHL/HIF axis includin

Non-Fragile Observer-Based Adaptive Integral Sliding Mode Control for a Class of T-S Fuzzy Descriptor Systems With Unmeasurable Premise Variables

The issue of non-fragile observer-based adaptive integral sliding mode control for a class of Takagi–Sugeno (T-S) fuzzy descriptor systems with uncertainties and unmeasurable premise variables is investigated. General nonlinear systems are represented by nonlinear T-S fuzzy descriptor models, because premise variables depend on unmeasurable system states and fuzzy models have different derivative matrices. By introducing the system state derivative as an auxiliary state vector, original fuzzy descriptor systems are transformed into augmented systems for which system properties remain the same. First, a novel integral sliding surface, which includes estimated states of the sliding mode observer and controller gain matrices, is designed to obtain estimation error dynamics and sliding mode dynamics. Then, some sufficient linear matrix inequality (LMI) conditions for designing the observer and the controller gains are derived using the appropriate fuzzy Lyapunov functions and Lyapunov theory. This approach yields asymptotically stable sliding mode dynamics. Corresponding auxiliary variables are introduced, and the Finsler's lemma is employed to eliminate coupling of controller gain matrices, observer gain matrices, Lyapunov function matrices, and/or observer gain perturbations. An observer-based integral sliding mode control strategy is obtained to assure that reachability conditions are satisfied. Moreover, a non-fragile observer and a non-fragile adaptive controller are developed to compensate for system uncertainties and perturbations in both the observer and the controller. Finally, example results are presented to illustrate the effectiveness and merits of the proposed method