YOLSO: You Only Look Small Object

Small object detection is challenging and far from satisfactory. Most general object detectors suffer from two critical issues with small objects: (1) Feature extractor based on classification network cannot express the characteristics of small objects reasonably due to insufficient appearance information of targets and a large amount of background interference around them. (2) The detector requires a much higher location accuracy for small objects than for general objects. This paper proposes an effective and efficient small object detector YOLSO to address the above problems. For feature representation, we analyze the drawbacks in previous backbones and present a Half-Space Shortcut(HSSC) module to build a background-aware backbone. Furthermore, a coarse-to-fine Feature Pyramid Enhancement(FPE) module is introduced for layer-wise aggregation at a granular level to enhance the semantic discriminability. For loss function, we propose an exponential L1 loss to promote the convergence of regression, and a focal IOU loss to focus on prime samples with high classification confidence and high IOU. Both of them significantly improves the location accuracy of small objects. The proposed YOLSO sets state-of-the-art results on two typical small object datasets, MOCOD and VeDAI, at a speed of over 200 FPS. In the meantime, it also outperforms the baseline YOLOv3 by a wide margin on the common COCO dataset.     

Identification and U-control of a state-space system with time-delay

This article presents a state-space model with time-delay to map the relationship between known input-output data for discrete systems. For the given input-output data, a model identification algorithm combining parameter estimation and state estimation is proposed in line with the causality constraints. Consequently, this article proposes a least squares parameter estimation algorithm, and analyzes its convergence for the studied systems to prove that the parameter estimation errors converge to zero under the persistent excitation conditions. In control system design, the U-model based control is introduced to provide a unilateral platform to improve the design efficiency and generality. A simulation portfolio from modeling to control is provided with computational experiments to validate the derived results.     

喷油方式对斜齿轮齿面温度的影响

为探讨喷油方式对齿面温度的影响，以斜齿轮为研究对象，通过CFD仿真和试验研究不同喷油方式对斜齿轮齿面温度的影响。针对斜齿轮喷油润滑过程的特点，分析其齿面摩擦生热量，简化轮齿喷油润滑计算模型，并基于CFX进行齿面对流换热仿真研究，得到了不同喷油方式下的齿面温度；基于功率开放式斜齿轮试验平台，对不同喷油方式下的齿面温度进行了试验测量，与仿真计算结果进行对比分析。结果表明：在高速重载斜齿轮传动系统中，啮出侧喷油润滑的方式比啮入侧喷油润滑方式的冷却降温效果要好。     

A signal adaptive diffusion filter for video coding: Mathematical framework and complexity reductions

In this paper we combine video compression and modern image processing methods. We construct novel iterative filter methods for prediction signals based on Partial Differential Equation (PDE) based methods. The mathematical framework of the employed diffusion filter class is given and some desirable properties are stated. In particular, two types of diffusion filters are constructed: a uniform diffusion filter using a fixed filter mask and a signal adaptive diffusion filter that incorporates the structures of the underlying prediction signal. The latter has the advantage of not attenuating existing edges while the uniform filter is less complex. The filters are embedded into a software based on HEVC with additional QTBT (Quadtree plus Binary Tree) and MTT (Multi-Type-Tree) block structure. In this setting, several measures to reduce the coding complexity of the tool are introduced, discussed and tested thoroughly. The coding complexity is reduced by up to 70% while maintaining over 80% of the gain. Overall, the diffusion filter method achieves average bitrate savings of 2.27% for Random Access having an average encoder runtime complexity of 119% and 117% decoder runtime complexity. For individual test sequences, results of 7.36% for Random Access are accomplished.     

Semantically enriched Massive Open Online Courses (MOOCs) platform

Massive Open Online Courses (MOOCs) are becoming an essential source of information for both students and teachers. Noticeably, MOOCs have to adapt to the fast development of new technologies; they also have to satisfy the current generation of online students. The current MOOCs’ Management Systems, such as Coursera, Udacity, edX, etc., use content management platforms where content are organized in a hierarchical structure. We envision a new generation of MOOCs that support interpretability with formal semantics by using the SemanticWeb and the online social networks. Semantic technologies support more flexible information management than that offered by the current MOOCs’ platforms. Annotated information about courses, video lectures, assignments, students, teachers, etc., can be composed from heterogeneous sources, including contributions from the communities in the forum space. These annotations, combined with legacy data, build foundations for more efficient information discovery in MOOCs’ platforms. In this article we review various Collaborative Semantic Filtering technologies for building Semantic MOOCs’ management system, then, we present a prototype of a semantic middle-sized platform implemented at Western Kentucky University that answers these aforementioned requirements.     

Superior foetal electrocardiogram signal elicitation using a novel artificial intelligent Bayesian methodology

Innumerable casualties due to intrauterine hypoxia are a major worry during prenatal phase besides advanced patient monitoring with latest science and technology. Hence, the analysis of foetal electrocardiogram (fECG) signals is very vital in order to evaluate the foetal heart status for timely recognition of cardiac abnormalities. Regrettably, the latest technology in the cutting edge field of biomedical signal processing does not seem to yield the desired quality of fECG signals required by physicians, which is the major cause for the pathetic condition. The focus of this work is to extort non-invasive fECG signal with highest possible quality with a motive to support physicians in utilizing the methodology for the latest intrapartum monitoring technique called STAN (ST analysis) for forecasting intrapartum foetal hypoxia. However, the critical quandary is that the non-invasive fECG signals recorded from the maternal abdomen are affected by several interferences like power line interference, baseline drift interference, electrode motion interference, muscle movement interference and the maternal electrocardiogram (mECG) being the dominant interference. A novel hybrid methodology called BANFIS (Bayesian adaptive neuro fuzzy inference system) is proposed. The BANFIS includes a Bayesian filter and an adaptive neuro fuzzy filter for mECG elimination and non-linear artefacts removal to yield high quality fECG signal. Kalman filtering frame work has been utilized to estimate the nonlinear transformed mECG component in the abdominal electrocardiogram (aECG). The adaptive neuro fuzzy filter is employed to discover the nonlinearity of the nonlinear transformed version of mECG and to align the estimated mECG signal with the maternal component in the aECG signal for annulment. The outcomes of the investigation by the proposed BANFIS system proved valuable for STAN system for efficient prediction of foetal hypoxia.     

Optimal filtering and control for wireless networked closed-loop control systems

This article studies the optimal filtering and control for wireless networked control systems (WNCSs). In WNCSs, packets may be lost in both control and feedback channels and user datagram protocol is usually used to improve the performance of the real-time control. Relevant literature indicates that the conventional optimal filtering for such a system cannot be applied in practice due to the complex calculation with Gaussian mixtures. This paper proposes a novel scheme to realize the optimal filtering and the linear quadratic Gaussian control for WNCSs, in which the controlled node performs a local estimation and the remote-control node performs the final estimation and control, and a synchronization of two estimators is guaranteed by a communication mechanism. An optimal filtering algorithm is developed, the stability condition of the filtering error covariance is obtained, optimal finite-horizon and infinite-horizon control are derived, and the stability of the closed-loop control system is proved. Numerical simulations show the validity and feasibility of the theoretical results.     

Enhanced thermoelectric properties of atomic-layer-deposited Hf:Zn16O/18O superlattice films by interface-engineering

This study examines three novel approaches for enhancing the thermoelectric (TE) properties of atomic-layer-deposited (ALD) ZnO thin films: 1) Hf-doping, which preserved the crystallinity of ZnO and provided effective phonon scattering owing to Hf's similar atomic radius to and large mass difference with Zn, leading to high power factor (PF) and low thermal conductivity (κ); 2) controlling the distribution of Hf into an alternating scattered phase/clustered phase superlattice, which balanced the high PF of the scattered phases with the low κ of the clustered phases, while providing significant energy-filtering effect to raise the Seebeck coefficient; 3) introducing ¹⁸O/¹⁶O periodicity into the Hf:ZnO films—by alternately using H₂¹⁶O and H₂¹⁸O as oxidants in the ALD processes, which further suppressed κ without compromising PF. The combination of the three approaches resulted in a maximum improvement in ZT of ~1600% over that of the undoped ZnO.     

Preparation of CaO granules using the granulation method

ABSTRACT

Improving the hydration resistance of CaO particle in manufacturing and application of free CaO-containing materials has practical significance. In this study, CaO granules was made from Ca(OH)₂ particles, which were fabricated by the granulation method. The results showed that the hydration resistance of the CaO granules which was prepared under 1700?r?min^?1 was the best, the CaO granules was sintered well in calcination process, the shell of CaO granules was relatively dense, which improves the hydration resistance of CaO granules, and the rate of hydration weight increment was 0.58% after placed in the air for 20 days under a temperature of 10–14°C and a relative humidity of 57–81%.