An efficient polymer moist-electric generator is developed on the basis of conventional polyelectrolyte membrane to output considerable electric power under moisture.

Converting ubiquitous environmental energy into electric power holds tremendous social and financial interests. Traditional harvesters converters are limited by the specific materials complex configuration of devices. Herein, it is presented that can be directly produced from pristine graphene oxide (GO) without any pretreatment or additives once encountering water vapor, which will generate an open-circuit-voltage up to 0.4-0.7 V a short-circuit-current-density 2-25 µA cm-2 on single piece...

Spontaneous electricity generation through water evaporation is becoming a hot research area. However, low power output, limited material availability, and unscalable fabrication largely hinder its wide applications. Here, we report scalable painting blade coating approaches for the mass production of flexible hydroelectric films (HEFs) based on solid oxides (e.g., Al2O3), which are tolerance to mechanical deformation compatible with three-dimensional diverse configuration. The generated...

Paper materials are utilized in moist-electric power generation, and a ‘power’ book that harvests moist-electricity is demonstrated.

Internet services are increasingly being deployed using cloud computing. However, the workload of an service is not constant; therefore, required computing resources need to be allocated elastically minimize associated costs. Thus, this study proposes a proactive resource scheduling framework. First, we propose new prediction method—named adaptive two-stage multi-neural network based on long short-term memory (LSTM)—which can adaptively route tasks corresponding LSTM sub-model according...

Under the educational reform background of "cultivating students' core literacy", physical education teaching in middle schools is transforming from imparting single skills to "all-round development". Based on three competencies motor ability, healthy behavior and social adaptation proposed "Compulsory Education Physical Health Curriculum Standards (2022 Edition)", this article analyzes limitations traditional methods cultivation competencies, systematically constructs innovative strategies...

Abstract With the recent growth in development of augmented reality (AR) technologies, it is becoming important to study human perception AR scenes. In order detect whether users will suffer more from visual and operator fatigue when watching virtual objects through optical see‐through head‐mounted displays (OST‐HMDs), compared with real world, we propose a comparative experiment including magic cube task task. The scores subjective questionnaires (SQ) values critical flicker frequency (CFF)...

Handwritten character recognition (HCR) is a mainstream mobile device input method that has attracted significant research interest. Although previous studies have delivered reasonable accuracy, it remains difficult to directly embed the advanced HCR service into software and obtain excellent but fast results. Cloud computing relatively new online computational resource provider which can satisfy elastic requirements of with high-recognition accuracy. However, owing delay sensitivity...

Compressed sensing theory is a new kind of making full use signal sparsity or compressible sampling theory. The suggests that collecting small amount values can realize accurate reconstruction sparse compressed signal. Through the research and summary existing algorithm, paper proposes adaptive matching pursuit algorithm based on regularization Regularized Adaptive Matching Pursuit (RAMP) for reconstruction, called blocking regularized (BSARMP) algorithms. In order to reduce scale single...

Traditional object detection methods require large amounts of training data. Eliminating the need to generate this data saves time and manual or computational effort. In field re-identification (Re-ID) similarity objects is defined by comparing query target images. Given a image from an object, these algorithms are able retrieve in image, even if class not known network. industrial environments, CAD usually exists for mechanical components. We present system that uses images components real...

This paper develops a new algorithm for sensor network self-localization, which is an enhanced version of the existing Crocco’s method in [11]. The explores noisy time flight (TOF) measurements that quantify distances between nodes to be localized and sources also at unknown positions. newly proposed technique first obtains rough estimates node source positions, then it refines via least squares estimator (LSE). LSE takes into account geometrical constraints introduced by desired global...

Word sense disambiguation is key to many application problems in natural language processing. In this paper, a specific classifier of word introduced into machine translation system order improve the quality output translation. Firstly, ambiguous deleted from Chinese sentence. Secondly, disambiguated and classification labels are translations word. Thirdly, these two combined. 50 sentences including words collected for test experiments. Experimental results show that improved after proposed...

Semantic feature modeling is a new trend of CAD technology. It very important for modifying and editing models automatically semi-automatically. In this paper, semantic method proposed, in which geometric constraints are solved. The history-independent. At the same time, architecture given. According to principles 3 dimensional rigid bodies, basic can be expressed. Experiment results show an instance modeled by proposed method.

Word sense disambiguation is widely applied to information retrieval, semantic comprehension and automatic summarization. It an important research problem in natural language processing. In this paper, the center window determined from target ambiguous word. The words are extracted as discriminative features. At same time, a new method of word proposed classifier given. optimized tested on SemEval-2007 #Task5 corpus. Experimental results show that accuracy rate arrives at 64.2%.

Semantic feature modeling is an important research topic in CAD, which geometric constraints model are solved automatically. A semantic method given this paper. Firstly, the dependent graph built based on constraints. Secondly, decomposed according to complexity of subgraphs and whole problem solving divided into several small ones. Thirdly, these problems solved. At same time, architecture decomposition given. Experimental results show that when proposed applied, performance improved.

Compressed sensing is a new signal sampling theory that fully makes use of signal's sparsity or compressibility.The shows that, the acquisition small amount sparse compressible value can be used for exact reconstruction.Based on study and summarization existing reconstruction algorithms, this paper proposes novel blocking variable step size forward-backward pursuit (BVSSFBP).This proposed algorithm by introducing concept phase to deal with different situations.The also divides...

Sense disambiguation is an important problem in pattern recognition. In this paper, a new algorithm of sense proposed, which part-of-speech tags the left word and right around ambiguous are extracted as discriminative features. At same time, bayesian model selected classifier it built based on The architecture classification given. trained sense-annotated corpus. Then used to determine its category. Experimental results show that accuracy rate arrives at 60%.