This paper aims to study distribution system operations by the ant colony search algorithm (ACSA). The objective of this study is to present new algorithms for solving the optimal feeder ...reconfiguration problem, the optimal capacitor placement problem, and the problem of a combination of the two. The ACSA is a relatively new and powerful swarm intelligence method for solving optimization problems. It is a population-based approach that uses exploration of positive feedback as well as ldquogreedyrdquo search. The ACSA was inspired from the natural behavior of ants in locating food sources and bring them back to their colony by the formation of unique trails. Therefore, through a collection of cooperative agents called ldquoants,rdquo the near-optimal solution to the feeder reconfiguration and capacitor placement problems can be effectively achieved. In addition, the ACSA applies the state transition, local pheromone-updating, and global pheromone-updating rules to facilitate the computation. Through simultaneous operation of population agents, process stagnation can be effectively prevented. Optimization capability can be significantly enhanced. The proposed approach is demonstrated using two example systems from the literature. Computational results show that simultaneously taking into account both feeder reconfiguration and capacitor placement is more effective than considering them separately.
In this paper, we study the challenging problem of categorizing videos according to high-level semantics such as the existence of a particular human action or a complex event. Although extensive ...efforts have been devoted in recent years, most existing works combined multiple video features using simple fusion strategies and neglected the utilization of inter-class semantic relationships. This paper proposes a novel unified framework that jointly exploits the feature relationships and the class relationships for improved categorization performance. Specifically, these two types of relationships are estimated and utilized by imposing regularizations in the learning process of a deep neural network (DNN). Through arming the DNN with better capability of harnessing both the feature and the class relationships, the proposed regularized DNN (rDNN) is more suitable for modeling video semantics. We show that rDNN produces better performance over several state-of-the-art approaches. Competitive results are reported on the well-known Hollywood2 and Columbia Consumer Video benchmarks. In addition, to stimulate future research on large scale video categorization, we collect and release a new benchmark dataset, called FCVID, which contains 91,223 Internet videos and 239 manually annotated categories.
This work presents the feasibility of monitoring state of charge (SoC) and state of health (SoH) of lithium-ion pouch batteries with acousto-ultrasonic guided waves. The guided waves are propagated ...and sensed using low-profile, built-in piezoelectric disc transducers that can be retrofitted onto off-the-shelf batteries. Both experimental and analytical studies are performed to understand the relationship between guided waves generated in a pitch-catch mode and battery SoC/SoH. The preliminary experiments on representative pouch cells show that the changes in time of flight (ToF) and signal amplitude (SA) resulting from shifts in the guided wave signals correlate strongly with the electrochemical charge-discharge cycling and aging. An analytical acoustic model is developed to simulate the variations in electrode moduli and densities during cycling, which correctly validates the absolute values and range of experimental ToF. It is further illustrated via a statistical study that ToF and SA can be used in a prediction model to accurately estimate SoC/SoH. Additionally, by using multiple sensors in a network configuration on the same battery, a significantly more reliable and accurate SoC/SoH prediction is achieved. The indicative results from this study can be extended to develop a unified guided-wave-based framework for SoC/SoH monitoring of many lithium-ion battery applications.
Display omitted
•Guided waves precisely estimate a lithium-ion battery's state of charge and health.•A simple implementation involves low-profile, built-in piezoelectric transducers.•Time of flight and signal amplitude are indicative of state of charge and health.•Signals from multiple propagation paths simplify computation and enhance accuracy.•Analytical results relate acoustic signature with changes in modulus and density.
Due to its low cost, environmentally friendly process, and lack of secondary contamination, the photodegradation of dyes is regarded as a promising technology for industrial wastewater treatment. ...This technology demonstrates the light-enhanced generation of charge carriers and reactive radicals that non-selectively degrade various organic dyes into water, CO2, and other organic compounds via direct photodegradation or a sensitization-mediated degradation process. The overall efficiency of the photocatalysis system is closely dependent upon operational parameters that govern the adsorption and photodegradation of dye molecules, including the initial dye concentration, pH of the solution, temperature of the reaction medium, and light intensity. Additionally, the charge-carrier properties of the photocatalyst strongly affect the generation of reactive species in the heterogeneous photodegradation and thereby dictate the photodegradation efficiency. Herein, this comprehensive review discusses the pseudo kinetics and mechanisms of the photodegradation reactions. The operational factors affecting the photodegradation of either cationic or anionic dye molecules, as well as the charge-carrier properties of the photocatalyst, are also fully explored. By further analyzing past works to clarify key active species for photodegradation reactions and optimal conditions, this review provides helpful guidelines that can be applied to foster the development of efficient photodegradation systems.
Semi-Supervised Hashing for Large-Scale Search Jun Wang; Kumar, S.; Shih-Fu Chang
IEEE transactions on pattern analysis and machine intelligence,
12/2012, Letnik:
34, Številka:
12
Journal Article
Recenzirano
Odprti dostop
Hashing-based approximate nearest neighbor (ANN) search in huge databases has become popular due to its computational and memory efficiency. The popular hashing methods, e.g., Locality Sensitive ...Hashing and Spectral Hashing, construct hash functions based on random or principal projections. The resulting hashes are either not very accurate or are inefficient. Moreover, these methods are designed for a given metric similarity. On the contrary, semantic similarity is usually given in terms of pairwise labels of samples. There exist supervised hashing methods that can handle such semantic similarity, but they are prone to overfitting when labeled data are small or noisy. In this work, we propose a semi-supervised hashing (SSH) framework that minimizes empirical error over the labeled set and an information theoretic regularizer over both labeled and unlabeled sets. Based on this framework, we present three different semi-supervised hashing methods, including orthogonal hashing, nonorthogonal hashing, and sequential hashing. Particularly, the sequential hashing method generates robust codes in which each hash function is designed to correct the errors made by the previous ones. We further show that the sequential learning paradigm can be extended to unsupervised domains where no labeled pairs are available. Extensive experiments on four large datasets (up to 80 million samples) demonstrate the superior performance of the proposed SSH methods over state-of-the-art supervised and unsupervised hashing techniques.
The explosive growth in Big Data has attracted much attention in designing efficient indexing and search methods recently. In many critical applications such as large-scale search and pattern ...matching, finding the nearest neighbors to a query is a fundamental research problem. However, the straightforward solution using exhaustive comparison is infeasible due to the prohibitive computational complexity and memory requirement. In response, approximate nearest neighbor (ANN) search based on hashing techniques has become popular due to its promising performance in both efficiency and accuracy. Prior randomized hashing methods, e.g., locality-sensitive hashing (LSH), explore data-independent hash functions with random projections or permutations. Although having elegant theoretic guarantees on the search quality in certain metric spaces, performance of randomized hashing has been shown insufficient in many real-world applications. As a remedy, new approaches incorporating data-driven learning methods in development of advanced hash functions have emerged. Such learning-to-hash methods exploit information such as data distributions or class labels when optimizing the hash codes or functions. Importantly, the learned hash codes are able to preserve the proximity of neighboring data in the original feature spaces in the hash code spaces. The goal of this paper is to provide readers with systematic understanding of insights, pros, and cons of the emerging techniques. We provide a comprehensive survey of the learning-to-hash framework and representative techniques of various types, including unsupervised, semisupervised, and supervised. In addition, we also summarize recent hashing approaches utilizing the deep learning models. Finally, we discuss the future direction and trends of research in this area.
Nod-like receptor family, pyrin domain-containing 3 (NLRP3) regulates the secretion of proinflammatory cytokines interleukin 1 beta (IL-1β) and IL-18. We previously showed that influenza virus M2 or ...encephalomyocarditis virus (EMCV) 2B proteins stimulate IL-1β secretion following activation of the NLRP3 inflammasome. However, the mechanism by which severe acute respiratory syndrome coronavirus (SARS-CoV) activates the NLRP3 inflammasome remains unknown. Here, we provide direct evidence that SARS-CoV 3a protein activates the NLRP3 inflammasome in lipopolysaccharide-primed macrophages. SARS-CoV 3a was sufficient to cause the NLRP3 inflammasome activation. The ion channel activity of the 3a protein was essential for 3a-mediated IL-1β secretion. While cells uninfected or infected with a lentivirus expressing a 3a protein defective in ion channel activity expressed NLRP3 uniformly throughout the cytoplasm, NLRP3 was redistributed to the perinuclear space in cells infected with a lentivirus expressing the 3a protein. K
efflux and mitochondrial reactive oxygen species were important for SARS-CoV 3a-induced NLRP3 inflammasome activation. These results highlight the importance of viroporins, transmembrane pore-forming viral proteins, in virus-induced NLRP3 inflammasome activation.
Participation in social activities is one of important factors for older adults' health. The present study aims to examine the cross-sectional association between social activities and cognitive ...function among Chinese elderly. A total of 8966 individuals aged 60 and older from the 2015 China Health and Retirement Longitudinal Study were obtained for this study. Telephone interviews of cognitive status, episodic memory, and visuospatial abilities were assessed by questionnaire. We used the sum of all three of the above measures to represent the respondent's cognitive status as a whole. Types and frequencies of participation in social groups were used to measure social activities. Multiple linear regression analysis was used to explore the relationship between social activities and cognitive function. After adjustment for demographics, smoking, drinking, depression, hypertension, diabetes, basic activities of daily living, instrumental activities of daily living, and self-rated health, multiple linear regression analysis revealed that interaction with friends, participating in hobby groups, and sports groups were associated with better cognitive function among both men and women (
< 0.05); doing volunteer work was associated with better cognitive function among women but not among men (
< 0.05). These findings suggest that there is a cross-sectional association between participation in social activities and cognitive function among Chinese elderly. Longitudinal studies are needed to examine the effects of social activities on cognitive function.
A single-feed single-patch triband antenna with different senses of circular polarization in different operating bands is presented in this letter. The first three pairs of the orthogonal modes of a ...single patch, TM 10 /TM 01 , TM 20 /TM 02 , and TM 30 /TM 03 modes, are excited to serve the triband circularly polarized operation, by loading stubs and etching slots in both x - and y - directions. Benefited from the asymmetrical radiating patch and aperture coupling on the diagonal line, the orthogonal modes can be well excited and controlled to realize the different senses of circular polarization in different operation bands by tuning the stubs and slots. A patch antenna prototype has been designed and fabricated for experimental validation. The measured 3 dB axial ratios (ARs) of the three bands are 2.439-2.452 GHz, 3.343-3.365 GHz, and 4.342-4.378 GHz, respectively, and the corresponding polarizations are left-hand circularly polarized (LHCP), right-hand circularly polarized (RHCP), and RHCP, respectively.
The authors describe transmission of the novel coronavirus from a woman who had been living for several months in Wuhan, China, to her husband, after her return to their home in Taiwan in January ...2020. No secondary case from this couple has been identified.
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