Mallard Detection Using Microphone Arrays Combined with Delay-and-Sum Beamforming for Smart and Remote Rice–Duck Farming

This paper presents an estimation method for a sound source of pre-recorded mallard calls from acoustic information using two microphone arrays combined with delay-and-sum beamforming. Rice farming using mallards saves labor because mallards work instead of farmers. Nevertheless, the number of mallards declines when they are preyed upon by natural enemies such as crows, kites, and weasels. We consider that efficient management can be achieved by locating and identifying the locations of mallards and their natural enemies using acoustic information that can be widely sensed in a paddy field. For this study, we developed a prototype system that comprises two sets of microphone arrays. We used 64 microphones in all installed on our originally designed and assembled sensor mounts. We obtained three acoustic datasets in an outdoor environment for our benchmark evaluation. The experimentally obtained results demonstrated that the proposed system provides adequate accuracy for application to rice–duck farming.

Benchmark Evaluation of Protein–Protein Interaction Prediction Algorithms

Protein–protein interactions (PPIs) perform various functions and regulate processes throughout cells. Knowledge of the full network of PPIs is vital to biomedical research, but most of the PPIs are still unknown. As it is infeasible to discover all of them experimentally due to technical and resource limitations, computational prediction of PPIs is essential and accurately assessing the performance of algorithms is required before further application or translation. However, many published methods compose their evaluation datasets incorrectly, using a higher proportion of positive class data than occuring naturally, leading to exaggerated performance. We re-implemented various published algorithms and evaluated them on datasets with realistic data compositions and found that their performance is overstated in original publications; with several methods outperformed by our control models built on ‘illogical’ and random number features. We conclude that these methods are influenced by an over-characterization of some proteins in the literature and due to scale-free nature of PPI network and that they fail when tested on all possible protein pairs. Additionally, we found that sequence-only-based algorithms performed worse than those that employ functional and expression features. We present a benchmark evaluation of many published algorithms for PPI prediction. The source code of our implementations and the benchmark datasets created here are made available in open source.

Complex-InversE: Improving bilinear knowledge graph embeddings by mapping into complex space

Knowledge graph link prediction uses known fact links to infer the missing link information in the knowledge graph, which is of great significance to the completion of the knowledge graph. Generating low-dimensional embeddings of entities and relations which are used to make inferences is a popular way for such link prediction problems. This paper proposes a knowledge graph link prediction method called Complex-InversE in the complex space, which maps entities and relations into the complex space. The composition of complex embeddings can handle a large variety of binary relations, among them symmetric and antisymmetric relations. The Complex-InversE effectively captures the antisymmetric relations and introduces Dropout and Early-Stopping technologies into deal with the problem of small numbers of relationships and entities, thus effectively alleviates the model’s overfitting. The results of comparison experiment on the public knowledge graph datasets show that the Complex-InversE achieves good results on multiple benchmark evaluation indicators and outperforms previous methods. Complex-InversE’s code is available on GitHub at https://github.com/ZeyuMiao97/Complex-InversE.

A Benchmark Evaluation of Adaptive Image Compression for Multi Picture Object Stereoscopic Images

A stereopair consists of two pictures related to the same subject taken by two different points of view. Since the two images contain a high amount of redundant information, new compression approaches and data formats are continuously proposed, which aim to reduce the space needed to store a stereoscopic image while preserving its quality. A standard for multi-picture image encoding is represented by the MPO format (Multi-Picture Object). The classic stereoscopic image compression approaches compute a disparity map between the two views, which is stored with one of the two views together with a residual image. An alternative approach, named adaptive stereoscopic image compression, encodes just the two views independently with different quality factors. Then, the redundancy between the two views is exploited to enhance the low quality image. In this paper, the problem of stereoscopic image compression is presented, with a focus on the adaptive stereoscopic compression approach, which allows us to obtain a standardized format of the compressed data. The paper presents a benchmark evaluation on large and standardized datasets including 60 stereopairs that differ by resolution and acquisition technique. The method is evaluated by varying the amount of compression, as well as the matching and optimization methods resulting in 16 different settings. The adaptive approach is also compared with other MPO-compliant methods. The paper also presents an Human Visual System (HVS)-based assessment experiment which involved 116 people in order to verify the perceived quality of the decoded images.

Cardiac Magnetic Resonance Images Superresolution via Multichannel Residual Attention Networks

The deep neural network has achieved good results in medical image superresolution. However, due to the medical equipment limitations and the complexity of the human body structure, it is difficult to reconstruct clear cardiac magnetic resonance (CMR) superresolution images. To reconstruct clearer CMR images, we propose a CMR image superresolution (SR) algorithm based on multichannel residual attention networks (MCRN), which uses the idea of residual learning to alleviate the difficulty of training and fully explore the feature information of the image and uses the back-projection learning mechanism to learn the interdependence between high-resolution images and low-resolution images. Furthermore, the MCRN model introduces an attention mechanism to dynamically allocate each feature map with different attention resources to discover more high-frequency information and learn the dependency between each channel of the feature map. Extensive benchmark evaluation shows that compared with state-of-the-art image SR methods, our MCRN algorithm not only improves the objective index significantly but also provides richer texture information for the reconstructed CMR images, and our MCRN algorithm is better than the Bicubic algorithm in evaluating the information entropy and average gradient of the reconstructed image quality.

Analysis of energy consumption status and development trend of cold chain logistics

As a new industry, cold chain logistics has attracted much attention. Under the strong support of national policies, the development of cold chain logistics has entered the fast lane. This study takes the energy consumption of cold chain logistics as the research object, uses the the energy consumption benchmark evaluation model of regression analysis to present the current situation of energy consumption of cold chain logistics from different dimensions, puts forward the contradictions and problems existing in the current development based on the SWOT (strengths, weaknesses, opportunities and threats) analysis method. Finally, the development trend of cold chain logistics energy consumption is predicted and relevant suggestions are put forward. The purpose of this paper is to find out the rule of effect of each factor on energy consumption in cold chain logistics, and to provide the direction for energy-saving measures in production.

OVERVIEW OF THE OECD-NEA EXPERT GROUP ON MULTI-PHYSICS EXPERIMENTAL DATA, BENCHMARKS AND VALIDATION

The OECD Nuclear Energy Agency (NEA) Nuclear Science Committee (NSC) established the Expert Group on Multi-physics Experimental Data, Benchmarks and Validation (EGMPEBV) in 2014 to bridge the gap between advanced, multi-physics simulation capabilities and the relatively low availability of dedicated, high-fidelity experimental data and benchmarks specifically for multi-physics modelling and simulation tools. The EGMPEBV was mandated to establish mechanisms for the certification of experimental data and benchmark models and to establish the processes and procedures for the validation of multi-physics modelling and simulation tools. The EGMPEBV oversees three task forces, covering (1) experimental data qualification and benchmark evaluation, (2) validation guidelines and needs and (3) example application of validation experiments. These have generated numerous reports surveying the state-of-the-art in multi-physics validation, challenge areas and recommendations for the evaluation of multi-physics benchmarks, while in parallel developing the specifications for multi-physics benchmarks. Three benchmark specifications are in active development, including a reactivity compensation scenario in the Rostov Unit 2 VVER-1000, multi-cycle depletion of the TVA Watts Bar Unit 1 and study of pellet cladding mechanical interaction within ramp tests performed at the Studsvik R2 reactor. We provide an overview of the recent progress in these areas and a summary of the future activities of the EGMPEBV in establishing international multi-physics benchmarks.

Application of mobile devices for the rapid assessment of masonry arch bridges – worked example and benchmarking

<p>Reassessment of masonry arches necessitates feasible methods for analysing the static behaviour of the persisting structure and an applicable way how to calculate the remaining load bearing capacity. Especially when a quick assessment of a structure is necessary, easily measurable characteristics, such as geometric properties have to be used. In this paper, the existing Application BRASSCO-NG is extended for masonry arch bridges by implementing the methodology described in paper [16] of this conference proceedings. Embedded into a worked example, the application of the tool is explained in detail from the first reconnaissance to the interpretation of the results. A subsequent benchmark evaluation to the static calculation and the empirical MEXE methodology compares the outcome to the techniques currently used in military and civil engineering. Even though the tool is mainly aimed for military application, the basic structure can easily be adopted for civil use, e.g. the rapid assessment of a structure after a natural disaster.</p>

ZERO POWER CRITICALITY BENCHMARK EVALUATION OF THE MSRE IN WIMS

This paper discusses work done to benchmark the deterministic code WIMS [1] against the Monte Carlo code Serpent [2] and experiment. Comparison is made against the Molten Salt Reactor Experiment at Oak Ridge National Laboratory as well as a Serpent model produced at the University of California, Berkeley. Producing a model for an MSR is possible thanks to the development of the next version of WIMS, WIMS11. The structure of the WIMS model built is discussed, and the final predicted criticality value for the MSR is given. This compares favourably with the Serpent model; however, both codes predict values considerably different to those expected. Potential reasons for this are suggested. However, it is concluded that WIMS has successfully been benchmarked against the current state of the art. This provides confirmation that this is a valid approach for molten salt reactor research analysis.