Miracle-dz/UniFAR-FTUs huggingface dataset:
Research on object detection algorithm based on deep learning for mobile Terminal

In order to improve object detection ability of robot, this study introduces an object detection algorithm which is based on deep learning. Firstly, a neural network that contains Convolution Layer, Pooling Layer and Fullly Connection Layer is designed to recognize whether the target object in image. Secondly, the Faster-RCNN, as a typical object detection algorithm, is used to detect the position of target object in image. Finally, the algorithm is transplanted on Raspberry Pi for deployment and testing. Results show that this algorithm can work well on Raspberry PI, and the mAP (mean average precision) and FPS (frames per second) reach 97% and 3. Meanwhile, the position of target object is accurately marked in image. Therefore, this study implies that the object detection algorithm based on deep learning can be implemented on mobile terminal and is helpful to improve object detection ability of mobile terminal.
DEA Modelling Effectiveness of Building Envelopes

Today, insulated building envelopes must not only meet thermal-technical, mechanical, and economic requirements but must also be environmentally friendly. These requirements are often contradictory, and it is not clear what the effective option is for a particular case. The aim of the paper is to compare twenty different variants of building envelopes, not only in terms of thermal-technical properties but also in terms of environmental parameters and time and financial demands. The result of the paper is the determination of effective variants by a suitable mathematical method with consideration of all the above-mentioned influences.
Energy demand prediction with machine learning supported by auto-tuning: a case study

The operational energy of buildings is making up one of the highest proportions of life-cycle carbon emissions. A more efficient operation of facilities would result in significant energy savings but necessitates computational models to predict a building’s future energy demands with high precision. To this end, various machine learning models have been proposed in recent years. These models’ prediction accuracies, however, strongly depend on their internal structure and hyperparameters. The time demand and expertise required for their finetuning call for a more efficient solution. In the context of a case study, this paper describes the relationship between a machine learning model’s prediction accuracy and its hyperparameters. Based on time-stamped recordings of outdoor temperatures and electricity demands of a hospital in Japan, recorded every 30 minutes for more than four years, using a deep neural network (DNN) ensemble model, electricity demands were predicted for sixty time steps to follow. Specifically, we used automatic hyperparameter tuning methods, such as grid search, random search, and Bayesian optimization. A single time step ahead, all tuning methods reduced the RSME to less than 50%, compared to non-optimized tuning. The results attest to machine learning models’ reliance on hyperparameters and the effectiveness of their automatic tuning.
Frequency tuning of piezoelectric energy harvesters by magnetic force

A piezoelectric energy harvester is an electromechanical device that converts ambient mechanical vibration into electric power. Most existing vibration energy harvesting devices operate effectively at a single frequency only, dictated by the design of the device. This frequency must match the frequency of the host structure vibration. However, real world structural vibrations rarely have a specific constant frequency. Therefore, piezoelectric harvesters that generate usable power across a range of exciting frequencies are required to make this technology commercially viable. Currently known harvester tuning techniques have many limitations, in particular they miss the ability to work during harvester operation and most often cannot perform a precise tuning. This paper describes the design and testing of a vibration energy harvester with tunable resonance frequency, wherein the tuning is accomplished by changing the attraction force between two permanent magnets by adjusting the distance between the magnets. This tuning technique allows the natural frequency to be manipulated before and during operation of the harvester. Furthermore the paper presents a physical description of the frequency tuning effect. The experimental results achieved with a piezoelectric bimorph fit the calculated results very well. The calculation and experimental results show that using this tuning technique the natural frequency of the harvester can be varied efficiently within a wide range: in the test setup, the natural frequency of the piezoelectric bimorph could be increased by more than 70%.
Research on Optimization of E-commerce Supply Chain Management Process Based on Internet of Things Technology

With the development of information network technology, online shopping has become more and more accepted and chosen by people, the demand for logistics and express delivery industry is also increasing. In today’s e-commerce era, the global logistics industry has a new development trend, called modern logistics. In modern logistics, the requirements for the important link of warehouse logistics and distribution are also increasing. The goal of supply chain management is to achieve maximum operational efficiency while minimizing capital investment, which is of great significance for e-logistics distribution. Based on this, this paper explores the application of Internet of Things technology in the supply chain management of e-logistics.
Wind-Induced Vibration of an Irregular Pentagon Lamella

At present, there are increasingly encountering the use of lamellar structures, for example on the roofs of buildings, which, in addition to their visual function, also fulfil the function of reducing the flow of wind into the roof space. These structures are often designed as long and subtle structures and therefore their very common problem is unwanted vibration. In this article, the main focus is to show the methodology of the determination of the effects of wind on the lamella of the shape of an irregular pentagon. A real-size model made of steel with a total length of 2 m and a weight of 7.4 kg was used. Its size and shape were influenced by several factors which are specified in more detail in the paper. In the wind tunnel experiment, it was very important to ensure the exact position of the model and also to secure both ends of the model against shifting (to replicate fixed ends). Dynamic response of the structure in two directions together with wind speed were measured simultaneously. To investigate the wind effects by numerical analysis, fluid-structure interaction software simulation (FSI) on a full-size model was used. The main pitfall of the software solution was to get as close as possible to the conditions of the wind tunnel. The actual wind speed measured under laboratory conditions was used as the input wind speed for FSI simulation. The material of the model and the shape of the model was set in software simulation to be as close as possible to the real structure. Subsequently, other boundary conditions were set and the solution process was executed. The biggest problem, especially in terms of comparing the results of both approaches which greatly affected the results, was the very high stiffness of the model. Due to the extent and interconnectedness of results, findings are presented in more detail in the conclusions of the paper. The methodology of setting up a relatively complex FSI simulation, its results, as well as new findings that we came up with if the measurement of the dynamic effects of wind is the matter of interest are presented in this paper.
Evaluation of the vacuum infusion process objectives at the early stages of computer simulation

Increasing the quality and reliable reproducibility of large-size composite structures molding using the vacuum infusion method, which is gaining popularity in various industries, is achieved in practice through numerous tests by try and errors that require significant costs and time. The purpose of these tests is to determine the layout of the ports for the resin injection and vacuum supply, as well as the temperature regime that ensures the absence of isolated non-impregnated zones, the minimum porosity and the required reinforcement volume fraction in the composite. The proposed approach removes the simplifying assumptions used in commercial software for modeling the process, which reduce the accuracy of reconstruction of its dynamics and the sensitivity to the formation of unrepairable defects such as dry spots. It involves multiphysics modeling of resin filling in a porous preform by describing the resin front dynamics by the phase field equation, pressure distribution in an unsaturated porous medium by the Richards equation, the evolution of the degree of cure by the convection / diffusion / thermokinetics equation, and thermal processes by the heat transfer equation using modified models of viscosity, the diffusion coefficient of the degree of cure, the boundary condition for the vacuum port. To reduce the finite element computation time of the investigated variants of the process, which is necessary for its computer optimization, the predictive partial sub-criteria were used, which give a reliable prediction before the beginning of the resin gel and solidification. Due to this, a gain in computation time is 30-50% with a significant prediction accuracy of quality objectives and the presence of possible defects.
A Study on Different Bandwidths and Rim Decorations’ Influence on the Mechanical Properties of Graphene Nanoribbon

This paper structured the Graphene Nanoribbon with different bandwidths and rim decorations and obtained the ideal strength and modulus of elasticity based on the calculation under the First Principle. It can be known that the mechanical properties of Graphene Nanoribbon are close to that of graphene, which have less changes with different bandwidth. However, the mechanical properties would be influenced by different decorations which may change the electronic connection state of edge carbon atoms. The results found in this paper can provide some reference for researchers to study the mechanical properties of graphene nanoribbons in the future.
Case study of fungal growth on newly cast concrete floors

In several cases, the Danish Technological Institute has experienced widespread fungal growth on newly cast concrete floors, with a moisture barrier and floating wooden flooring. The reason for fungal growth is usually due to an inadequate drying period. Existing recommendations require that the relative humidity (RH) of air in equilibrium with the concrete, measured in the middle of the concrete, should not exceed 85-90% RH. In this study, six randomly picked apartments in a newly built apartment complex, were chosen for a case study of fungal growth and moisture on newly cast concrete. The study demonstrates that at least some pecies of fungi can grow very well on newly cast concrete if the surface is dusty and moist. The study also demonstrates that a few samples on the surface will often be representative for the whole floor. The study finds that there is a need to revise the existing guidelines for acceptable moisture content in the concrete before mounting the floor. This might have an impact on the entire building process and/or the design of the floor construction. The study also finds that there is a need for a guideline for measuring moisture and fungal growth on newly cast concrete floors.
Reconciliation of Owner and Contractor Views in Heavy Construction Projects

Delay and loss of productivity are the two main types of damage experienced by the contractor when the owner issues a change order. Courts have recognized critical path method schedule analysis as the preferred method of identifying and quantifying critical delays. As for the inefficiency damages, there is no direct way of measuring inefficiency due to its qualitative nature and the difficulty of linking the cause of the productivity loss to the damage. Most of the scholarly work published in this area was based on data supplied by the contractors; and that explains why there are discrepancies between what the contractor asks for and what the owner believes the contractor is entitled to. This study addresses the need for a statistical model to quantify the productivity loss from verifiable site data such as owner’s daily reports, change orders, drawings, and specifications, rather than rely solely on contractor surveys. A model is developed and validated to quantify the productivity loss in pipe work in r.
Study on Universal Parameter of Power System for Urban Bus in Cold Region Based on Driving Cycles

In northern China, temperature is below zero degrees Celsius more than six months in a year. The buses lacked power and temperature in winter. In order to study the universal parameter of power system, 40 bus lines were selected. The bus lines came from the cities of Changchun, Jilin, Tonghua, and Songyuan. Driving cycle's data was collected by an experiment with the real urban bus in two years. After defining the kinematics fragments and characteristic parameter, 13660 divided kinematics fragments and 13 selected kinematic eigenvalues composed a matrix. After reducing the matrix by principal component analysis, the matrix has been reconstructed including the kinematics fragments and the corresponding principal component score. The 13660 kinematics fragments were divided into three categories by fuzzy C-means clustering. On this basis, abnormal data were removed by Pauta criterion. Then according to the characteristic parameter of kinematic eigenvalues, a model on engine power and driving cycles was built, which included the heating power in the winter. Finally, three sets of power-weight ratios were calculated by the weight and volume of the traditional local bus. The buses which are configured with this method have enough power to provide warm carriage.
Interpretation of gas-in-oil analysis using new IEC publication 60599 and IEC TC 10 databases

IEC Publication 60599, concerning the interpretation of dissolved gas-in-oil analysis, was issued in 1999 by Working Group 13, as a result of the revision of IEC TC 10 of the previous IEC Publication 599, issued in 1978. The main body of Publication 60599 contains an in-depth description of the five main types of faults usually found in electrical equipment in service. The familiar gas ratios have been retained for the diagnoses, but with new code limits, while additional gas ratios are suggested for specific fault cases. More precise definitions of normal and alarm gas concentrations in service are indicated. In the Annexes, examples of typical (normal) gas concentration values observed in service are given for six different types of equipment. Extensive databases of faulty equipment inspected in service and of typical normal values related to various types and ages of equipment and types of faults have been used for the revision of Publication 60599. These databases are presented in this paper for the benefit of the maintenance personnel involved in the interpretation of DGA results in service.
Visually-guided Grip Selection for Soft-Hand Exoskeleton

This paper presents a visually-guided grip selection based on the combination of object recognition and tactile feedback of a soft-hand exoskeleton intended for hand rehabilitation. A pre-trained neural network is used to recognize the object in front of the hand exoskeleton, which is then mapped to a suitable grip type. With the object cue, it actively assists users in performing different grip movements without calibration. In a pilot experiment, one healthy user completed four different grasp-and-move tasks repeatedly. All trials were completed within 25 seconds and only one out of 20 trials failed. This shows that automated movement training can be achieved by visual guidance even without biomedical sensors. In particular, in the private setting at home without clinical supervision, it is a powerful tool for repetitive training of daily-living activities.
The Computer Intelligent Selection of Scientific Research Subjects Through Ensemble Learning for Large-Scale Data Sources and Deep Neural Network

Selecting a proper scientific research subject is critical for scientific researchers and managers. Scientific researching data are from massive sources and have various attributes. For the problem of subject selection, feature extraction and prediction model play important role in performance optimization. In the paper we introduce ensemble learning method to help find the best fit attributes describing data. Our ensemble learning models include random forests, support vector machine, Boltzmann machine and decision tree. Since the data are from many data sources, we adopt multiple models of deep neural network. An acceleration method is used to reduce the training time as well. Experiments shows that the proposed approach performs better than RNN algorithm both in accuracy ratio and recall ratio. The model selection module and acceleration method help optimize the time cost largely.
Remaining Useful Life Prediction of Aero-engines by Appropriate Utilization of Multi-sensor Signals

This paper presents the prediction of remaining useful life (RUL) with appropriate fusion of multi-sensor signals for the aero-engine, which is the heart of an aircraft. With the rapid development of information technology, health condition of one aero-engine is usually monitored with multiple sensors. To properly utilize these multi-sensor condition information for degradation modeling and RUL prediction is one of the key challenges for condition-based maintenance of the whole aircraft. Thus this paper proposes one statistical method based on health indicator (HI) construction and empirical parametric model for aero-engines RUL prediction. The method is validated with run-to-failure data sets of an aircraft gas turbine engine test-bed developed by NASA. Results show that the proposed method can effectively fuse multi-sensor signals to describe the degradation and predict RUL of the aero-engine.
Preliminary Validation of Upper Limb Musculoskeletal Model using Static Optimization

Musculoskeletal models are powerful analogues to simulate human motion through kinematic and dynamic analysis. When coupled with feature-rich software, musculoskeletal models form an attractive platform for the integration of machine learning for human motion analysis. Performing realistic simulations using these models provide an avenue to overcome constraints when collecting real-world data sets. This motivates the need to further investigate the validity, efficacy, and accuracy of each available model to ensure that the resultant simulations are transferable to real-world applications. Using the open-source software, OpenSim, the primary aim of this paper is to validate an upper limb musculoskeletal model widely used in research. Muscle activation results from static optimization are evaluated against real-world data. A secondary aim is to investigate the effects of two muscle force generation constraints when evaluating the model’s validity. Results show an agreement between the optimized muscle activation trends and real-world sEMG readings. However, it was found that static optimization of the musculoskeletal model is unable to identify voluntary co-contractions since the redundant model has more muscles than the system’s degrees of freedom. Thus, future work will look to utilize additional channels of information to incorporate this during analysis.
Anatomical Landmark Detection using Deep Appearance-Context Network

Accurate identification of anatomical landmarks is a crucial step in medical image analysis. While deep neural networks have shown impressive performance on computer vision tasks, they rely on a large amount of data, which is often not available. In this work, we propose an attention-driven end-to-end deep learning architecture, which learns the local appearance and global context separately that helps in stable training under limited data. The experiments conducted demonstrate the effectiveness of the proposed approach with impressive results in localizing landmarks when evaluated on cephalometric and spine X-ray image data. The predicted landmarks are further utilized in biomedical applications to demonstrate the impact.
Modified silk and chitosan scaffolds with collagen assembly for osteoporosis

Currently, many patients suffer from osteoporosis. Osteoporosis is a disease that leads to bone defect. Severe cases of bone defect from osteoporosis need an operation using a performance scaffold for bone tissue engineering. Therefore, to build a performance scaffold for bone defect from osteoporosis is the target of this research. Samples of silk fibroin and chitosan were fabricated into porous scaffolds before modification by coating with collagen selfassembly. The structure and morphology of the samples were characterized and observed by Fourier transform infrared spectroscopy, atomic force microscopy, and scanning electron microscopy. For biological functionality analysis, MC3T3-E1 osteoblasts were cultured on the samples. Afterward, biodegradation, cell proliferation, viability, and mineralization were analyzed. The results demonstrated that collagen organized into a fibril structure covering the pores of the scaffold. The modified scaffolds showed low degradability, high cell proliferation, viability, and mineralization. The results demonstrated that the modified scaffolds with a coating of mimicked collagen selfassembly had good performance and showed promise for bone tissue engineering in osteoporosis.
A visualization tool for assessment of spinal cord functional magnetic resonance imaging data quality

Functional magnetic resonance imaging (fMRI) is an extensively used neuroimaging technique to non-invasively detect neural activity. Data quality is highly variable, and fMRI analysis typically consists of a number of complex processing steps. It is crucial to visually assess images throughout analysis to ensure that data quality at each step is satisfactory. For fMRI analysis of the brain, there is a simple tool to visualize four-dimensional data on a two-dimensional plot for qualitative analysis. Despite the practicality of this method, it cannot be directly applied to fMRI data of the spinal cord, and a comparable approach does not exist for spinal cord fMRI analysis. The additional challenges encountered in spinal cord imaging, including the small size of the cord and the influence of physiological noise sources, drive the importance of developing a similar visualization technique for spinal cord fMRI. Here, we introduce a highly versatile image analysis tool to visualize spinal cord fMRI data as a simple heatmap and to co-visualize relevant traces such as physiological or motion timeseries. We present multiple variations of the plot, data features that can be identified with the heatmap, and examples of the useful qualitative analyses that can be performed using this method. The spinal cord plot can be easily integrated into an fMRI analysis pipeline and can streamline visual inspection and qualitative analysis of functional imaging data.Clinical Relevance— Implementation of this data visualization method is a simple addition to spinal cord fMRI analysis that could be used to identify normal vs. abnormal signal variation in pathologies that impact the cord, such as spinal cord injury or multiple sclerosis.