LSTM Pose Machines

We observed that recent state-of-the-art results on single image human pose estimation were achieved by multi-stage Convolution Neural Networks (CNN). Notwithstanding the superior performance on static images, the application of these models on videos is not only computationally intensive, it also suffers from performance degeneration and flicking. Such suboptimal results are mainly attributed to the inability of imposing sequential geometric consistency, handling severe image quality degradation (e.g. motion blur and occlusion) as well as the inability of capturing the temporal correlation among video frames. In this paper, we proposed a novel recurrent network to tackle these problems. We showed that if we were to impose the weight sharing scheme to the multi-stage CNN, it could be re-written as a Recurrent Neural Network (RNN). This property decouples the relationship among multiple network stages and results in significantly faster speed in invoking the network for videos. It also enables the adoption of Long Short-Term Memory (LSTM) units between video frames. We found such memory augmented RNN is very effective in imposing geometric consistency among frames. It also well handles input quality degradation in videos while successfully stabilizes the sequential outputs. The experiments showed that our approach significantly outperformed current state-of-the-art methods on two large-scale video pose estimation benchmarks. We also explored the memory cells inside the LSTM and provided insights on why such mechanism would benefit the prediction for video-based pose estimations.Comment: Poster in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 201

Estimating and Visualizing Drivers’ Emotions Using the Internet of Digital Reality

Recently, the development of self-driving technology has progressed rapidly. However, self-driving cars have not yet become widespread. Thus, with an aging population, accidents such as road rage and acceleration and brake accidents are likely to continue. Stress is one key reason for such dangerous driving. Thus, technologies must be developed to provide mental support to drivers as required. In this study, we considered estimating driver emotions as a first step along these lines. To this end, we developed a technology to estimate emotions by collecting data on biological signals such as brain waves, heart rate, body movement, and data on a driver's operating status while they are driving. In addition, we introduce a Positive and Negative Affect Schedule (PANAS) to express the psychological states experienced by drivers. We further present the results of an analysis of data on a driver's emotions from PANAS and data obtained from electroencephalogram (EEG) readings and other biological signals from a car. In addition, the relationship between this experimental environment and the Internet of Digital Reality (IoD) is described

Microstructure and performance of rare earth element-strengthened plasma-facing tungsten material

Pure W and W-(2%, 5%, 10%) Lu alloys were manufactured via mechanical alloying for 20 h and a spark plasma sintering process at 1, 873 K for 2 min. The effects of Lu doping on the microstructure and performance of W were investigated using various techniques. For irradiation performance analysis, thermal desorption spectroscopy (TDS) measurements were performed from room temperature to 1, 000 K via infrared irradiation with a heating rate of 1 K/s after implantations of He+ and D+ ions. TDS measurements were conducted to investigate D retention behavior. Microhardness was dramatically enhanced, and the density initially increased and then decreased with Lu content. The D retention performance followed the same trend as the density. Second-phase particles identified as Lu2O3 particles were completely distributed over the W grain boundaries and generated an effective grain refinement. Transgranular and intergranular fracture modes were observed on the fracture surface of the sintered W-Lu samples, indicating some improvement of strength and toughness. The amount and distribution of Lu substantially affected the properties of W. Among the investigated alloy compositions, W-5%Lu exhibited the best overall performance

3D Protein structure prediction with genetic tabu search algorithm

Abstract Background Protein structure prediction (PSP) has important applications in different fields, such as drug design, disease prediction, and so on. In protein structure prediction, there are two important issues. The first one is the design of the structure model and the second one is the design of the optimization technology. Because of the complexity of the realistic protein structure, the structure model adopted in this paper is a simplified model, which is called off-lattice AB model. After the structure model is assumed, optimization technology is needed for searching the best conformation of a protein sequence based on the assumed structure model. However, PSP is an NP-hard problem even if the simplest model is assumed. Thus, many algorithms have been developed to solve the global optimization problem. In this paper, a hybrid algorithm, which combines genetic algorithm (GA) and tabu search (TS) algorithm, is developed to complete this task. Results In order to develop an efficient optimization algorithm, several improved strategies are developed for the proposed genetic tabu search algorithm. The combined use of these strategies can improve the efficiency of the algorithm. In these strategies, tabu search introduced into the crossover and mutation operators can improve the local search capability, the adoption of variable population size strategy can maintain the diversity of the population, and the ranking selection strategy can improve the possibility of an individual with low energy value entering into next generation. Experiments are performed with Fibonacci sequences and real protein sequences. Experimental results show that the lowest energy obtained by the proposed GATS algorithm is lower than that obtained by previous methods. Conclusions The hybrid algorithm has the advantages from both genetic algorithm and tabu search algorithm. It makes use of the advantage of multiple search points in genetic algorithm, and can overcome poor hill-climbing capability in the conventional genetic algorithm by using the flexible memory functions of TS. Compared with some previous algorithms, GATS algorithm has better performance in global optimization and can predict 3D protein structure more effectively

The utilization of nanotechnology in the female reproductive system and related disorders

The health of the reproductive system is intricately linked to female fertility and quality of life. There has been a growing prevalence of reproductive system disorders among women, particularly in younger age groups, resulting in significant adverse effects on their reproductive health. Consequently, there is an urgent need for effective treatment modalities. Nanotechnology, as an advanced discipline, provides innovative avenues for managing and treating diseases of the female reproductive system by enabling precise manipulation and regulation of biological molecules and cells. By utilizing nanodelivery systems, drugs can be administered with pinpoint accuracy, leading to reduced side effects and improved therapeutic efficacy. Moreover, nanomaterial imaging techniques enhance diagnostic precision and sensitivity, aiding in the assessment of disease severity and progression. Furthermore, the implementation of nanobiosensors facilitates early detection and prevention of ailments. This comprehensive review aims to summarize recent applications of nanotechnology in the treatment of female reproductive system diseases. The latest advancements in drug delivery, diagnosis, and treatment approaches will be discussed, with an emphasis on the potential of nanotechnology to improve treatment outcomes and overall quality of life

In Situ Synthesis of Poly(methyl methacrylate)/SiO2 Hybrid Nanocomposites via “Grafting Onto” Strategy Based on UV Irradiation in the Presence of Iron Aqueous Solution

Poly(methyl methacrylate)/SiO2 (PMMA/SiO2) hybrid composites were prepared via “grafting onto” strategy based on UV irradiation in the presence of iron aqueous solution. Two steps were used to graft polymethyl methacrylate (PMMA) onto the surface of nanosilica, anchoring 3-(methacryloxy) propyl trimethoxysilane (MPTS) onto the surface of nanosilica to modify it with double bonds, and then grafting PMMA onto the surface of nanosilica with FeCl3 as photoinitiator. The products were characterized by FT-IR, TGA, TEM, DLS, and XPS. The results showed that it is easy to graft PMMA onto the surface of nanosilica under UV irradiation, and the hybrid particles are monodisperse and have core-shell structure with nanosilica as the core and PMMA layers as the shell. Furthermore, the products initiated by FeCl3 have higher monomer conversion, percent grafting, and better monodispersion compared with the products initiated by traditional photoinitiator such as 2-hydroxy-4-(2-hydroxyethoxy)-2-methyl-propiophenone (Irgacure 2959)

An interactive image retrieval method

© 2016 IEEE. In this paper, we propose an interactive image retrieval method based on interactive image segmentation and relevance feedback. For testing the performance of the algorithm, we built an image database by web crawlers, and added a background label to each image by histogram analysis. For image retrieval, an interactive image segmentation scheme based on GrabCut has been applied to get the region of interest (ROI), and then we use an automatic labeling method to get the training samples of relevance feedback, and then incorporate the background labels into the similarity measurement to decrease the influence of clutters. The experimental results show that this method can reduce the influence of image background on image retrieval, and optimize the search results by the feedback of users

The Fast Detection of Abnormal ETC Data Based on an Improved DTW Algorithm

As one of the largest Internet of Things systems in the world, China’s expressway electronic toll collection (ETC) generates nearly one billion pieces of transaction data every day, recording the traffic trajectories of almost all vehicles on the expressway, which has great potential application value. However, there are inevitable missed transactions and false transactions in the expressway ETC system, which leads to certain false and missing rates in ETC data. In this work, a dynamic search step SegrDTW algorithm based on an improved DTW algorithm is proposed according to the characteristics of expressway ETC data with origin–destination (OD) data constraints and coupling between the gantry path and the vehicle trajectory. Through constructing the spatial window of segment retrieval, the spatial complexity of the DTW algorithm is effectively reduced, and the efficiency of the abnormal ETC data detection is greatly improved. In real traffic data experiments, the SegrDTW algorithm only needs 3.36 s to measure the abnormal events of a single set of OD path data for 10 days. Compared with the mainstream algorithms, the SegrDTW performs best. Therefore, the proposal provides a feasible method for the abnormal event detection of expressway ETC data in a province and even the whole country

Effect of Mo, V and Zr on the microstructure and mechanical properties of Ti2AlNb alloys

Small button ingots of Ti2AlNb alloys with different contents of Mo, V and Zr were melted by vacuum non-consumable arc furnace. Due to the rapid cooling rate during melting process, only β grains without precipitation were observed in most of the button ingots and no regular phenomenon was found. However, when the samples were heated to β phase region and then furnace cooled to room temperate, different morphologies and quantities of primary α phase and second O phase formed from the β grains of different samples. It is suggested that the morphology of α phase was changed from lamellar to quadrilateral with increasing V and the lath O increased with increasing Zr. Besides, the residual β/B2 phase increased with increasing Mo and V. The EDS results showed that Al and Zr were enriched in α phase whereas Nb, Mo and V were enriched in β/B2 phase. The micro-hardness of these samples before and after heat treatment was detected and the micro-hardness increased with increasing Zr and decreasing Mo and V

Effect of Mo, V and Zr on the microstructure and mechanical properties of Ti

Small button ingots of Ti2AlNb alloys with different contents of Mo, V and Zr were melted by vacuum non-consumable arc furnace. Due to the rapid cooling rate during melting process, only β grains without precipitation were observed in most of the button ingots and no regular phenomenon was found. However, when the samples were heated to β phase region and then furnace cooled to room temperate, different morphologies and quantities of primary α phase and second O phase formed from the β grains of different samples. It is suggested that the morphology of α phase was changed from lamellar to quadrilateral with increasing V and the lath O increased with increasing Zr. Besides, the residual β/B2 phase increased with increasing Mo and V. The EDS results showed that Al and Zr were enriched in α phase whereas Nb, Mo and V were enriched in β/B2 phase. The micro-hardness of these samples before and after heat treatment was detected and the micro-hardness increased with increasing Zr and decreasing Mo and V