Constrained Discrete Phase Control of a Heaving Wave Energy Converter in Irregular Seas Using Reinforcement Learning

Designed for offshore deployment in irregular seas, the point absorber wave energy conversion (WEC) system is promisingly attractive amongst the currently available WEC technologies. The effectiveness of phase control when applied to a heaving point absorber through a hydraulic power take-off (PTO) system is systematically investigated in both regular and irregular waves. For this purpose, two phase control accumulators are utilized in the hydraulic PTO system. Simulations are performed in MATLAB® using the Cummins equation to model the dynamics of the heaving point absorber in the time domain. For a given sea state, the opening instant of the control valves of the phase control accumulators relative to the wave excitation peak and the volumetric displacement of the hydraulic motor are utilized as parameters in a number of simulation runs. In regular waves, the parametric investigation demonstrates that in most cases there is a trade off between maximizing the mean generated power and minimizing the maximum motion amplitude. In fully developed irregular seas, a parametric investigation of different sea states in the North Atlantic demonstrates that by utilizing phase control a significant increase in the power absorption efficiency can be obtained compared to the WEC system operation without phase control. The problem of providing an effective phase-control strategy that maximizes the mean generated power of the WEC system subject to motion amplitude constraints is formulated and solved using a Reinforcement Learning (RL) approach based on the Q-learning algorithm. The RL-based controller chooses actions that determine the opening instant of the phase control accumulator valves and the volumetric displacement of the hydraulic motor. As demonstrated in both regular and irregular waves, the RL-based controller is successful in finding the optimal phase-control strategy. Finally, the prediction of the wave excitation force is performed using a Radial Basis Function (RBF) network ensemble in order to evaluate the impact of the prediction accuracy on the RL-controller\u27s performance. The results show that the computed mean generated power and maximum motion amplitude values using the RBF network ensemble predictions compare very well with the corresponding values computed assuming perfect knowledge of the future wave excitation

Learning to embed semantic similarity for joint image-text retrieval

We present a deep learning approach for learning the joint semantic embeddings of images and captions in a Euclidean space, such that the semantic similarity is approximated by the L2 distances in the embedding space. For that, we introduce a metric learning scheme that utilizes multitask learning to learn the embedding of identical semantic concepts using a center loss. By introducing a differentiable quantization scheme into the end-to-end trainable network, we derive a semantic embedding of semantically similar concepts in Euclidean space. We also propose a novel metric learning formulation using an adaptive margin hinge loss, that is refined during the training phase. The proposed scheme was applied to the MS-COCO, Flicke30K and Flickr8K datasets, and was shown to compare favorably with contemporary state-of-the-art approaches.Comment: in IEEE Transactions on Pattern Analysis and Machine Intelligence, 202

A Neural Network Approach to Identify Hyperspectral Image Content

A Hyperspectral is the imaging technique that contains very large dimension data with the hundreds of channels. Meanwhile, the Hyperspectral Images (HISs) delivers the complete knowledge of imaging; therefore applying a classification algorithm is very important tool for practical uses. The HSIs are always having a large number of correlated and redundant feature, which causes the decrement in the classification accuracy; moreover, the features redundancy come up with some extra burden of computation that without adding any beneficial information to the classification accuracy. In this study, an unsupervised based Band Selection Algorithm (BSA) is considered with the Linear Projection (LP) that depends upon the metric-band similarities. Afterwards Monogenetic Binary Feature (MBF) has consider to perform the ‘texture analysis’ of the HSI, where three operational component represents the monogenetic signal such as; phase, amplitude and orientation. In post processing classification stage, feature-mapping function can provide important information, which help to adopt the Kernel based Neural Network (KNN) to optimize the generalization ability. However, an alternative method of multiclass application can be adopt through KNN, if we consider the multi-output nodes instead of taking single-output node

Uncertainty Analysis and Instrument Selection Using a Web-Based Virtual Experiment

A methodology has been developed and successfully implemented for transforming physical experiments in an undergraduate thermo-fluids laboratory at Old Dominion University (ODU), a doctoral university, into web-based virtual experiments while the Mechanical Engineering (ME) faculty at Western Kentucky University (WKU), an undergraduate university, have developed and implemented a Design of Experiments (DOE) Plan to assure that graduates of their program have acquired the skills necessary to design and conduct experiments and analyze experimental results. This paper presents details about a web-based virtual experiment designed to teach students about selection of instruments based on the uncertainty estimated from the virtual experiment. The web-based virtual experiment, involves the measurement of frictional losses in fluid flowing in a pipe at various flow rates. In this virtual module, the student experimenter can adjust the flow rate in the pipe with a virtual flow control valve and measure both the flow rate and the pressure drop by selecting different measuring instruments. The selected instruments have corresponding measurement uncertainties and the student is tasked through various activities in the virtual experiment to evaluate which instrument is the best fit for the particular experimental design situation. The web-based virtual module has been tested at ODU and an assessment of its effectiveness in student learning is provided. Student learning gains achieved through the web-based virtual module were measured by comparing the performance of a Control group (no access to the module) and an Experimental group with access to the web-based virtual module. Both groups were administered an identical multiple choice quiz and the quiz scores were analyzed to gage the effectiveness of the module in teaching students about instrument selection, and uncertainty and errors in experiments. Students in the Experimental group were also surveyed to get their feedback on the effectiveness of the module in aiding their learning of these skills

Indica rice genome assembly, annotation and mining of blast disease resistance genes

Sequence data size for rice cultivars. (XLSX 15 kb

Open Arms: Open-Source Arms, Hands & Control

Open Arms is a novel open-source platform of realistic human-like robotic hands and arms hardware with 28 Degree-of-Freedom (DoF), designed to extend the capabilities and accessibility of humanoid robotic grasping and manipulation. The Open Arms framework includes an open SDK and development environment, simulation tools, and application development tools to build and operate Open Arms. This paper describes these hands controls, sensing, mechanisms, aesthetic design, and manufacturing and their real-world applications with a teleoperated nursing robot. From 2015 to 2022, the authors have designed and established the manufacturing of Open Arms as a low-cost, high functionality robotic arms hardware and software framework to serve both humanoid robot applications and the urgent demand for low-cost prosthetics, as part of the Hanson Robotics Sophia Robot platform. Using the techniques of consumer product manufacturing, we set out to define modular, low-cost techniques for approximating the dexterity and sensitivity of human hands. To demonstrate the dexterity and control of our hands, we present a Generative Grasping Residual CNN (GGR-CNN) model that can generate robust antipodal grasps from input images of various objects in real-time speeds (22ms). We achieved state-of-the-art accuracy of 92.4% using our model architecture on a standard Cornell Grasping Dataset, which contains a diverse set of household objects.Comment: Submitted to 36th Conference on Neural Information Processing Systems (NeurIPS 2022

Diverse and tissue-enriched small RNAs in the plant pathogenic fungus, Magnaporthe oryzae

<p>Abstract</p> <p>Background</p> <p>Emerging knowledge of the impact of small RNAs as important cellular regulators has prompted an explosion of small transcriptome sequencing projects. Although significant progress has been made towards small RNA discovery and biogenesis in higher eukaryotes and other model organisms, knowledge in simple eukaryotes such as filamentous fungi remains limited.</p> <p>Results</p> <p>Here, we used 454 pyrosequencing to present a detailed analysis of the small RNA transcriptome (~ 15 - 40 nucleotides in length) from mycelia and appressoria tissues of the rice blast fungal pathogen, <it>Magnaporthe oryzae</it>. Small RNAs mapped to numerous nuclear and mitochondrial genomic features including repetitive elements, tRNA loci, rRNAs, protein coding genes, snRNAs and intergenic regions. For most elements, small RNAs mapped primarily to the sense strand with the exception of repetitive elements to which small RNAs mapped in the sense and antisense orientation in near equal proportions. Inspection of the small RNAs revealed a preference for U and suppression of C at position 1, particularly for antisense mapping small RNAs. In the mycelia library, small RNAs of the size 18 - 23 nt were enriched for intergenic regions and repetitive elements. Small RNAs mapping to LTR retrotransposons were classified as LTR retrotransposon-siRNAs (LTR-siRNAs). Conversely, the appressoria library had a greater proportion of 28 - 35 nt small RNAs mapping to tRNA loci, and were classified as tRNA-derived RNA fragments (tRFs). LTR-siRNAs and tRFs were independently validated by 3' RACE PCR and northern blots, respectively.</p> <p>Conclusions</p> <p>Our findings suggest <it>M. oryzae </it>small RNAs differentially accumulate in vegetative and specialized-infection tissues and may play an active role in genome integrity and regulating growth and development.</p

Deep and comparative analysis of the mycelium and appressorium transcriptomes of Magnaporthe grisea using MPSS, RL-SAGE, and oligoarray methods

BACKGROUND: Rice blast, caused by the fungal pathogen Magnaporthe grisea, is a devastating disease causing tremendous yield loss in rice production. The public availability of the complete genome sequence of M. grisea provides ample opportunities to understand the molecular mechanism of its pathogenesis on rice plants at the transcriptome level. To identify all the expressed genes encoded in the fungal genome, we have analyzed the mycelium and appressorium transcriptomes using massively parallel signature sequencing (MPSS), robust-long serial analysis of gene expression (RL-SAGE) and oligoarray methods. RESULTS: The MPSS analyses identified 12,531 and 12,927 distinct significant tags from mycelia and appressoria, respectively, while the RL-SAGE analysis identified 16,580 distinct significant tags from the mycelial library. When matching these 12,531 mycelial and 12,927 appressorial significant tags to the annotated CDS, 500 bp upstream and 500 bp downstream of CDS, 6,735 unique genes in mycelia and 7,686 unique genes in appressoria were identified. A total of 7,135 mycelium-specific and 7,531 appressorium-specific significant MPSS tags were identified, which correspond to 2,088 and 1,784 annotated genes, respectively, when matching to the same set of reference sequences. Nearly 85% of the significant MPSS tags from mycelia and appressoria and 65% of the significant tags from the RL-SAGE mycelium library matched to the M. grisea genome. MPSS and RL-SAGE methods supported the expression of more than 9,000 genes, representing over 80% of the predicted genes in M. grisea. About 40% of the MPSS tags and 55% of the RL-SAGE tags represent novel transcripts since they had no matches in the existing M. grisea EST collections. Over 19% of the annotated genes were found to produce both sense and antisense tags in the protein-coding region. The oligoarray analysis identified the expression of 3,793 mycelium-specific and 4,652 appressorium-specific genes. A total of 2,430 mycelial genes and 1,886 appressorial genes were identified by both MPSS and oligoarray. CONCLUSION: The comprehensive and deep transcriptome analysis by MPSS and RL-SAGE methods identified many novel sense and antisense transcripts in the M. grisea genome at two important growth stages. The differentially expressed transcripts that were identified, especially those specifically expressed in appressoria, represent a genomic resource useful for gaining a better understanding of the molecular basis of M. grisea pathogenicity. Further analysis of the novel antisense transcripts will provide new insights into the regulation and function of these genes in fungal growth, development and pathogenesis in the host plants

Complete assembly of a dengue virus type 3 genome from a recent genotype III clade by metagenomic sequencing of serum.

Background: Mosquito-borne flaviviruses, such as dengue and Japanese encephalitis virus (JEV), cause life-threatening diseases, particularly in the tropics. Methods: Here we performed unbiased metagenomic sequencing of RNA extracted from the serum of four patients and the plasma of one patient, all hospitalized at a tertiary care centre in South India with severe or prolonged febrile illness, together with the serum from one healthy control, in 2014. Results: We identified and assembled a complete dengue virus type 3 sequence from a case of severe dengue fever. We also identified a small number of JEV sequences in the serum of two adults with febrile illness, including one with severe dengue. Phylogenetic analysis revealed that the dengue sequence belonged to genotype III. It has an estimated divergence time of 13.86 years from the most highly related Indian strains. In total, 11 amino acid substitutions were predicted for this strain in the antigenic envelope protein, when compared to the parent strain used for development of the first commercial dengue vaccine.  Conclusions: We demonstrate that both genome assembly and detection of a low number of viral sequences are possible through the unbiased sequencing of clinical material. These methods may help ascertain causal agents for febrile illnesses with no known cause