Weakly supervised object detection is an interesting yet challenging research topic in computer vision community, which aims at learning object models to localize and detect the corresponding objects ...of interest only under the supervision of image-level annotation. For addressing this problem, this paper establishes a novel weakly supervised learning framework to leverage both the instance-level prior-knowledge and the image-level prior-knowledge based on a novel collaborative self-paced curriculum learning (C-SPCL) regime. Under the weak supervision, C-SPCL can leverage helpful prior-knowledge throughout the whole learning process and collaborate the instance-level confidence inference with the image-level confidence inference in a robust way. Comprehensive experiments on benchmark datasets demonstrate the superior capacity of the proposed C-SPCL regime and the proposed whole framework as compared with state-of-the-art methods along this research line.
Regardless of whether the global navigation satellite system (GNSS)/inertial navigation system (INS) is integrated or the INS operates independently during GNSS outages, the stochastic error of the ...inertial sensor has an important impact on the navigation performance. The structure of stochastic error in low-cost inertial sensors is quite complex; therefore, it is difficult to identify and separate errors in the spectral domain using classical stochastic error methods such as the Allan variance (AV) method and power spectral density (PSD) analysis. However, a recently proposed estimation, based on generalized wavelet moment estimation (GMWM), is applied to the stochastic error modeling of inertial sensors, giving significant advantages. Focusing on the online implementation of GMWM and its integration within a general navigation filter, this paper proposes an algorithm for online stochastic error calibration of inertial sensors in urban cities. We further develop the autonomous stochastic error model by constructing a complete stochastic error model and determining model ranking criterion. Then, a detecting module is designed to work together with the autonomous stochastic error model as feedback for the INS/GNSS integration. Finally, two experiments are conducted to compare the positioning performance of this algorithm with other classical methods. The results validate the capability of this algorithm to improve navigation accuracy and achieve the online realization of complex stochastic models.
In this paper, we present a column-and-constraint generation algorithm to solve two-stage robust optimization problems. Compared with existing Benders-style cutting plane methods, the ...column-and-constraint generation algorithm is a general procedure with a unified approach to deal with optimality and feasibility. A computational study on a two-stage robust location-transportation problem shows that it performs an order of magnitude faster.
We consider a system where a massive multiple-input multiple-output (MIMO) base station (BS) transmits information and energy to multiple energy harvesting receivers. Each receiver has no power ...source and needs to harvest sufficient energy in order to decode its message from the received signal. Under either the power splitting mode or the time switching mode at the receivers, we consider two design problems. One is to maximize the minimum transmission rate among all receivers and the other is to optimize the system energy efficiency (EE) through jointly designing the power allocation proportions at the BS and the power splitting (or time switching) factors at the receivers. The optimal solutions to these problems are obtained either in terms of closed-form expressions or efficient algorithms by leveraging the asymptotic channel orthogonality and hardening effects of massive MIMO. The simulation results indicate that the power splitting mode outperforms the time switching mode in terms of both the minimum transmission rate and the system EE.
•Ionic liquid was covalently immobilized onto cellulose microspheres via radiation method.•ILFC had practicality, reusability and selectivity for Cr(VI) removal.•ILFC exhibited good adsorption ...performance toward Cr(VI) in column.
Combining the advantages of both cellulose and ionic liquid, ionic liquid functionalized cellulose (ILFC) as adsorbent was prepared through radiation grafting glycidyl methacrylate onto cellulose microsphere following by reaction with ionic liquid 1-aminopropyl-3-methyl imidazolium nitrate. Its adsorption properties towards Cr(VI) were investigated in batch and column experiments. In batch experiments, the adsorption kinetics was well fitted with pseudo-second-order mode with equilibrium time of 2 h and the adsorption capacity reached 181.8 mg/g at pH 2 calculated from Langmuir model. In fixed column, both Yoon-Nelson and Thomas models gave satisfactory fit to experimental data and breakthrough curves, and equilibrium adsorption capacity calculated by Thomas model was 161.0 mg/g. Moreover, ILFC exhibited high selectivity towards Cr(VI) even in synthetic chrome-plating wastewater. Besides, adsorption/desorption test revealed ILFC can be regenerated and reused several times without obvious decrease in adsorbed amount. The adsorption process was demonstrated to anion exchange-reduction mechanism via XPS analysis.
Vanishing point is an important geometric element in sports video. In this paper, a new calibration algorithm is proposed by using the algebraic and geometric properties of vanishing points, which ...resolves the three main problems of the traditional camera calibration technology based on vanishing points: (1) calibrating camera with varied focal length; (2) screening out outliers from a set of images; (3) estimation of distortion coefficients. The principal line passes through the principal point, and the algebraic relationship between it and the vanishing points is deduced. Using the geometry feature of the principal line, problems (1) and (2) can be easily solved. The linear relationship between the point and the line is used to estimate the distortion coefficient under the condition of obtaining the principal point. Simulation and real experiments show the validity and robustness of the proposed algorithm, and satisfactory results can be obtained by solving the above three problems.
Radioactive molecular iodine (I
) and organic iodides, mainly methyl iodide (CH
I), coexist in the off-gas stream of nuclear power plants at low concentrations, whereas few adsorbents can effectively ...adsorb low-concentration I
and CH
I simultaneously. Here we demonstrate that the I
adsorption can occur on various adsorptive sites and be promoted through intermolecular interactions. The CH
I adsorption capacity is positively correlated with the content of strong binding sites but is unrelated to the textural properties of the adsorbent. These insights allow us to design a covalent organic framework to simultaneously capture I
and CH
I at low concentrations. The developed material, COF-TAPT, combines high crystallinity, a large surface area, and abundant nucleophilic groups and exhibits a record-high static CH
I adsorption capacity (1.53 g·g
at 25 °C). In the dynamic mixed-gas adsorption with 150 ppm of I
and 50 ppm of CH
I, COF-TAPT presents an excellent total iodine capture capacity (1.51 g·g
), surpassing various benchmark adsorbents. This work deepens the understanding of I
/CH
I adsorption mechanisms, providing guidance for the development of novel adsorbents for related applications.
LiDAR has emerged as one of the most pivotal sensors in the field of navigation, owing to its expansive measurement range, high resolution, and adeptness in capturing intricate scene details. This ...significance is particularly pronounced in challenging navigation scenarios where GNSS signals encounter interference, such as within urban canyons and indoor environments. However, the copious volume of point cloud data poses a challenge, rendering traditional iterative closest point (ICP) methods inadequate in meeting real-time odometry requirements. Consequently, many algorithms have turned to feature extraction approaches. Nonetheless, with the advent of diverse scanning mode LiDARs, there arises a necessity to devise unique methods tailored to these sensors to facilitate algorithm migration. To address this challenge, we propose a weighted point-to-plane matching strategy that focuses on local details without relying on feature extraction. This improved approach mitigates the impact of imperfect plane fitting on localization accuracy. Moreover, we present a classification optimization method based on the normal vectors of planes to further refine algorithmic efficiency. Finally, we devise a tightly coupled LiDAR-inertial odometry system founded upon optimization schemes. Notably, we pioneer the derivation of an online gravity estimation method from the perspective of S2 manifold optimization, effectively minimizing the influence of gravity estimation errors introduced during the initialization phase on localization accuracy. The efficacy of the proposed method was validated through experimentation employing various LiDAR sensors. The outcomes of indoor and outdoor experiments substantiate its capability to furnish real-time and precise localization and mapping results.
WiFi fingerprinting-based indoor positioning system (IPS) using received signal strength (RSS) has been considered to be one solution for indoor positioning. However, there are two major bottlenecks ...that hamper its large-scale implementation. One widely recognized problem is the construction of a proper fingerprint database with high efficiency and accuracy. Second is to improve the online positioning accuracy on the basis of the fingerprint database. To address these issues comprehensively, this paper proposes a novel system-Digital navigation center IPS (DncIPS), an IPS that enables automatic online radio map construction, and step-by-step positioning, aiming for the high-accuracy RSS estimation and high-precision positioning. DncIPS can capture WiFi data packets transmitted in WiFi traffic so that they obtain the MAC addresses, frequency, and RSS of any WiFi access point (AP) at any point. DncIPS uses Gaussian process regression model based on a fireworks algorithm to approximate the RSS distribution of an indoor environment and to estimate the location of APs increasing the flexibility of DncIPS work environment. This system also consists of a coarse localizer detecting the outliers and dividing clustering area and a fine localizer followed to improve the online positioning accuracy. Extensive experiments results indicate the proposed system DncIPS leads to improvement on radio map updating and localization accuracy.
Despite a well‐developed and growing body of work in Cu catalysis, the potential of Cu to serve as a photocatalyst remains underexplored. Reported herein is the first example of visible‐light‐induced ...Cu‐catalyzed decarboxylative C(sp3)−H alkylation of glycine for preparing α‐alkylated unnatural α‐amino acids. It merits mentioning that the mild conditions and the good functional‐group tolerance allow the modification of peptides using this method. The mechanistic studies revealed that a radical–radical coupling pathway is involved in the reaction.
Out of the clear blue: The title reaction was developed for preparing α‐alkylated non‐natural α‐amino acids by transformation of glycine moieties. Mild conditions and good functional‐group tolerance allow the modification of peptides using this method. Mechanistic studies revealed that a radical–radical coupling pathway is involved in the reaction.
