Miracle-dz/UniFAR-FTUs huggingface dataset:
Research on energy consumption of fiber-reinforced fluidic soft actuators

Fluidic soft actuators have drawn more and more research interest in recent years, but it is not clear on their behaviors of dynamical energy consumption, which is critical to power system design. To solve this problem, this work develops an analytical model of required energy including elastic potential energy and dissipated energy for fiber-reinforced fluidic soft actuators based on the theory of viscoelasticity. Since the parameters of soft material are difficult to obtain, the key parameters in the model are identified by experimental data and genetic algorithm. To verify the general performance of the developed model, a series of fiber-reinforced fluidic soft actuators with different dimensions are fabricated and tested. The average errors of the experimental value and the predicted value are equal to 0.045 J, 0.003 J, 0.001 J, and 0.011 J, which are less than 15% of the nominal values. The experimental results show that the model can capture the energy characteristics of the soft actuators under various operating conditions with expected accuracy. Finally, the energy consumption of a robotic arm composed of three soft actuators is also evaluated. This work can promote the understanding on the energy requirement of soft robots and provide further reference to the optimization design of power system.
Automatic Defect Segmentation in X-Ray Images Based on Deep Learning

X-ray imaging has been broadly adopted as a nondestructive testing method for product quality inspection. Deep learning has demonstrated powerful image scene understanding capabilities. In this article, U-Net with resnet101 is taken as the baseline for defect segmentation. First, there exist gray inhomogeneous and low-contrast regions in X-ray images, which can hardly be segmented. Contrast-limited adaptive histogram equalization (CLAHE) could be used to improve the contrast and consistency of the X-ray image. A two-stream convolutional neural network (CNN) is proposed that takes the original image and CLAHE processed image as inputs to address this issue. And then in CNN, low-level feature maps are lacking semantic information, which may lead to worse results. A gated multilayer fusion module is proposed to adaptively fuse the high-level features into low-level features. Furthermore, loss functions (such as cross entropy) in semantic segmentation are usually pixel level, ignoring the regional information. A weighted intersection over union (IOU) loss function is proposed to introduce IOU information to guide the model to focus on the objects that are easy to mine. The experimental results prove that the three proposed methods have better performance than the baseline for our dataset, achieving 42.2 in mIoU, 59.2 in Dice, and 54.5%, 74.9%, and 86.3% in small, middle, and large object recall rate, respectively.
Pre-Harvest Finger Rot of Cavendish Banana (Musa acuminate) Reported from Moneragala and Badulla Districts, Sri Lanka: Identification and Confirmation of Pathogenicity of the Causal Agent

The present study was conducted to identify and confirm the pathogenicity of the causal agent of a wet finger rot of Cavendish banana, a newly reported pre-harvest disease in commercial plantations in Moneragala and Badulla districts of Sri Lanka. Bacterial species were isolated from banana fingers at three stages of symptom development: initial, middle, and late. Isolated bacterial colonies were inoculated to healthy and mature banana fingers by two methods, namely peel and pulp inoculation, to confirm the pathogenicity by Koch’s postulates. Re-isolated bacterial isolates from inoculated banana fingers showing typical symptoms were identified by biochemical tests and subjected to PCR amplification using universal primers of bacteria, B27F/1492R. Subsequent DNA sequencing of the PCR products and homology search identified the pathogen as a strain of Dickeya dadantii. Phylogenetic analysis with selected Dickeya spp. sequences available in databases identified that the D. dadantii strain of the present study groups with D. dadantii 3937 (CP002038.1), of which the complete genome has been sequenced. Cross infection studies revealed that D. dadantii isolate is more virulent on banana variety Anamalu than on Cavendish banana in terms of days taken to develop the symptoms. The findings identified the causal agent of wet finger rot of banana in the present study as a strain of D. dadantii and confirmed its pathogenicity. The present study was conducted to identify and confirm the pathogenicity of the causal agent of a wet finger rot of Cavendish banana, a newly reported pre-harvest disease in commercial plantations in Moneragala and Badulla districts of Sri Lanka. Bacterial species were isolated from banana fingers at three stages of symptom development: initial, middle, and late. Isolated bacterial colonies were inoculated to healthy and mature banana fingers by two methods, namely peel and pulp inoculation, to confirm the pathogenicity by Koch’s postulates. Re-isolated bacterial isolates from inoculated banana fingers showing typical symptoms were identified by biochemical tests and subjected to PCR amplification using universal primers of bacteria, B27F/1492R. Subsequent DNA sequencing of the PCR products and homology search identified the pathogen as a strain of Dickeya dadantii. Phylogenetic analysis with selected Dickeya spp. sequences available in databases identified that the D. dadantii strain of the present study groups with D. dadantii 3937 (CP002038.1), of which the complete genome has been sequenced. Cross infection studies revealed that D. dadantii isolate is more virulent on banana variety Anamalu than on Cavendish banana in terms of days taken to develop the symptoms. The findings identified the causal agent of wet finger rot of banana in the present study as a strain of D. dadantii and confirmed its pathogenicity.
Suppression of Range Ambiguity in Spaceborne SAR With Elevation Beam Pattern Mask Design

In this letter, we present a method to design an elevation beam pattern mask for spaceborne synthetic aperture radar (SAR) to reduce the range ambiguity. The proposed two-way elevation beam pattern mask, which requires relatively low sidelobe levels only in the direction of strong range ambiguous echoes, can be highly suitable for enhancing the range ambiguity to signal ratio (RASR). The power levels of the different ambiguous echoes are estimated based on the backscattering coefficient model and satellite geometry. To meet the two-way beam pattern mask, both transmit and receive beam patterns are synthesized simultaneously through the minimax algorithm. To confirm the effectiveness of this mask design method, the RASR performance is evaluated by using various swaths.
A novel Chinese herbal medicine classification approach based on EfficientNet

Chinese Herbal Medicine (CHM) classification is a promising research issue in Intelligent Medicine. However, the small available Chinese Herbal datasets and the traditional CHM classification model lead to huge challenge for obtaining the promising classification results. To tackle the above challenges, a novel large CHM classification (CHMC) dataset has been firstly established, which includes 100 classes with about 10,000 samples. This dataset contains a wide range of medicinal materials and natural background. Further, the promising EfficientNetB4 model is proposed to perform the CHM classification. EfficientNet can uniformly scales up the depth, width and resolution of the model, which will obtain better accuracy as it balance all dimensions of the network, including depth, width, and resolution, respectively. To validate the superiority of the EfficientNet and the effectiveness of CHMC dataset, extensive experiments have been conducted, verifying that the EfficientNetB4 is optimal for CHM classification, with 5% improvement of the existing model. In addition, this model has achieved state-of-the art CHM classification performance, with TOP-1 accuracy of 83.1%, and TOP-5 accuracy of 92.50%.
AKM3C: Adaptive K-Multiple-Means for Multi-View Clustering

With the popularity of cameras and sensors, massive data are captured from various view angles or modalities, which provide abundant complementary information and also bring great challenges for traditional clustering methods. In this article, we propose a novel Adaptive K-Multiple-Means for multi-view clustering method (AKM<sup>3</sup>C). Unlike traditional multi-view K-means methods by grouping samples into <inline-formula> <tex-math notation="LaTeX">$C$ </tex-math></inline-formula> clusters each with a cluster center in every view, the proposed AKM<sup>3</sup>C employs <inline-formula> <tex-math notation="LaTeX">$M (M>C)$ </tex-math></inline-formula> sub-cluster centers in each view to reveal the sub-cluster structure in the multi-view data thus enhances the clustering performance. Additionally, to distinguish the importance of different views, instead of using empirical weights, AKM<sup>3</sup>C exploits the multi-view combination weights strategy to assign a weight to each view automatically and thus fuses the complementary information of different views properly to get an optimally shared bipartite graph, on which the Laplacian rank constraint is executed and the final clusters are obtained by directly partitioning. An efficient optimization algorithm proposed with complexity and convergence analysis is used to solve the proposed AKM<sup>3</sup>C method. The extensive experimental results on eight public datasets show that the proposed AKM<sup>3</sup>C performs better than state-of-the-art multi-view clustering methods. The code can be downloaded at <uri>https://drive.google. com/file/d/1CQ0royrYxKFJdNLnbBQSbDrohtfH71di/view? usp=sharing</uri>.
Information Theoretic Weighted Fuzzy Clustering Ensemble

: In order to improve performance and robustness of clustering, it is proposed to generate and aggregate a number of primary clusters via clustering ensemble technique. Fuzzy clustering ensemble approaches attempt to improve the performance of fuzzy clustering tasks. However, in these approaches, cluster (or clustering) reliability has not paid much attention to. Ignoring cluster (or clustering) reliability makes these approaches weak in dealing with low-quality base clustering methods. In this paper, we have utilized cluster unreliability estimation and local weighting strategy to propose a new fuzzy clustering ensemble method which has introduced Reliability Based weighted co-association matrix Fuzzy C-Means number of data objects. Performance and robustness of the proposed method are experimentally evaluated for some benchmark datasets. The experimental results demonstrate efficiency and suitability of the proposed method.
Conceptual design of a novel Unmanned Ground Effect Vehicle

In this work, a methodology for the conceptual design of a combined Box-wing and Blended-Wing-Body unmanned aerial platform, which exploits the ground effect, is presented. Ground Effect Vehicles (GEVs) are aircrafts or aerial vehicles capable of flying very close to the surface of water areas, being a promising alternative to ships and seaplanes operating in closed seas. In order to exploit the air cushion that is formed underneath, GEVs have low Aspect Ratio wings. Thus, the Blended-Wing-Body (BWB) layout configuration is an appealing choice due to the large available body surface. On the other hand, a tailless vehicle having the BWB layout is facing challenging stability issues, especially in a turbulent and highly volatile environment as the one close to the sea level. Therefore, the addition of the Box wing configuration, namely a continuous-surface nonplanar wing formation with no wing tips, is deemed beneficiary as a way to overcome the stability challenges and eliminate the wingtip vortices. The combination of these two prominent platforms leads to improved aerodynamic performance and eventually, a reduced fuel consumption. The design methodology starts by the estimation of the most important design parameters, such as aspect ratio, sweep angle, and taper ratio, which are continuously refined in an iterative computational framework during the conceptual design phase. In an initial approach, the flight scenario with no ground effect interference is studied, with the use of both analytical calculations and computational fluid dynamics simulations. The results from the conceptual design phase indicate that the UGEV configuration has a considerable potential as an alternative to ships or seaplanes, based on its ability to carry larger payload than seaplanes and deliver it faster than ships.
Disjunct plant species in South American seasonally dry tropical forests responded differently to past climatic fluctuations

Seasonally dry tropical forests (STDFs) are a main component of open seasonally dry areas in South America and their biogeography is understudied compared to evergreen forests. In this work, we identify vascular plant species with long-distance disjunctions across STDF patches of South America based on information available in online repositories and on species taxonomy and distribution, to explore species’ biogeographic patterns. Specifically, we combine distribution data from the Brazilian Flora 2020 Project (BFG) and the Global Biodiversity Information Facility (GBIF) to identify species with a peri-Amazonian distribution, and then use species distribution models to discuss possible scenarios of peri-Amazonian distributions under Pleistocene climatic fluctuations. We identified 81 candidate species for peri-Amazonian distributions in STDFs, including shrubs, herbs, trees and lianas, and provided a summary of their main fruit dispersion syndrome based on the literature to identify prevalent dispersal patterns. The study species responded differently to Pleistocene climatic fluctuations, with both contractions and expansions through time in different rates and do not show consistent larger distributions during past climate conditions. Our results show that a peri-Amazonian distribution is also present in growth-forms other than trees. Also, the prevalence of species with long-distance dispersal strategies such as wind or vertebrate-dispersed can suggest, although biased for Neotropical taxa, an alternative scenario of long-distance dispersal, possibly using stepping-stones of azonal vegetation. We argue that such an alternative scenario, especially for species disjunct with long-dispersal abilities, should be considered to test if STDF disjunctions are relics of a past widespread distribution or not.
Molecularly Imprinted Polymers for Selective Adsorption of Lysozyme and Cytochrome c Using a PEG-Based Hydrogel: Selective Recognition for Different Conformations Due to pH Conditions

We report molecularly imprinted polymers (MIPs) for selective adsorption of proteins using a poly(ethylene glycol) (PEG)-based cross-linker with ionic monomers. To clarify the utility of the concept for preparation of the imprinted polymers using a PEG-based cross-linker, we employed lysozyme or cytochrome c as a template molecule. A few sulfonic functional monomers including sodium allylsulfonate, 2-acrylamido-2-methylpropanesulfonic acid, and sodium p-styrenesulfonate were utilized. After optimization of the concentration of the cross-linker and the washing process with a NaCl solution, we confirmed the complete eluting of the template lysozyme. The optimized lysozyme MIP showed the selective adsorption ability for lysozyme against the other proteins in higher salted condition, in which nonspecific ionic interaction due to the functional monomers were suppressed. Also, the imprinting factor toward the nonimprinted polymer was reached to over 8.6. Similar approaches were carried out for cytochrome c MIPs.
Comparative Analysis of Sleep Stage according to Number of EEG Channels

EEG(electroencephalogram) are measured to accurately determine the level of sleep in various sleep examinations. In general, measurements are more accurate as the number of sensor channels increases. EEG can interfere with sleep by attaching electrodes to the skin when measuring. It is necessary for self sleep care to select the minimum number of EEG channels that take into account both the user's discomfort and the accuracy of the measurement data. In this paper, we proposed a sleep stage analysis model based on machine learning and conducted experiments for using from one channel to four channels. We obtained estimation accuracy for sleep stage as following 82.28% for one channel, 85.77% for two channels, 80.33% for three channels and 68.87% for four channels. Although the measurement location is limited, the results of this study compare the accuracy according to the number of channels and provide information on the selection of channel numbers in the EEG sleep analysis. ■ keyword :∣Sleep Stage Analysis∣Sleep Care∣EEG Measurement∣EEG Channel Comparison∣Machine Learning∣ * This work was supported by the Technology development Program funded by the Ministry of SMEs and Startups(MSS, Korea) (Grants No. S2798371) 접수일자 : 2020년 09월 23일 수정일자 : 2020년 11월 20일 심사완료일 : 2020년 11월 23일 교신저자 : 이병문, e-mail : bmlee@gachon.ac. kr 뇌파 채널 개수 변화에 따른 수면단계 분석 비교 141 중요한 요소이다. 수면단계를 REM(Rapid Eye Movements)수면, 1단계, 2단계, 3단계, 4단계, 각성비수면 상태로 구분한다[5]. 적절한 수면이 사람의 회복 과 건강 상태에 영향을 미치기 때문에 수면상태를 이용 하여 건강상태를 파악 할 수 있다. 수면 클리닉과 같은 임상에서는 수면다원검사를 통 해 EEG를 측정할 때 10개에서 20개에 달하는 전극을 두피에 부착한다. 피부에 부착한 전극의 개수가 늘어나 면 정확도는 올라가지만 불편하므로 착용감이 감소하 는 문제가 있다. 일상생활에서 사용할 수면케어 시스템 은 많은 수의 전극을 부착하는 것이 오히려 사용자의 수면을 방해한다. 그러므로 의료용이 아니라 일상생활 가정에서 수면 케어용으로 사용하려면 채널의 개수가 적어야 한다. 그 래서 연구 사례들을 살펴보면 단일채널에서 정확도를 높이기 위해 다양한 기법이 도입되고 있다[6][7]. 측정 전극이 1개인 단일채널은 두피에 1개의 전극을 부착하 므로 덜 불편하지만 부착위치에서 조금만 벗어나도 측 정과 수면분석이 어려워지는 단점이 있다. 또 측정 위 치에 따라 뇌파의 파형이 달라지기 때문에 단일채널보 다 다중채널에서 더 다양한 정보를 획득할 수 있다[8]. 스마트 홈 헬스케어 환경이 고도화되면서 가정 환경 에서 다량의 의료 데이터를 처리할 수 있다[9]. 하지만 수면케어용 뇌파측정 시스템을 실용화하기 위해서는 사용자의 수면 방해 요소인 채널 개수를 최소화하면서 정확도에 대한 고려가 필요하다. 뇌파 채널을 1개부터 최대 4개까지 사용하여 수면단계를 분석하고 정확도를 비교하면, 가장 적합한 채널 개수를 선택하는데 도움이 된다. 또 다양한 위치에서 측정한 뇌파의 정확도를 비 교하면 뇌파분석 기반 수면케어 장치 설계에 활용할 수 있다. 따라서 본 논문에서는 채널에 따른 수면단계 분석을 위해 Physionet의 공개된 뇌파 데이터와 랜덤 포레스 트 알고리즘을 이용한 수면단계 분석 모델을 제시한다. 수면단계 분석 모델은 파이프라인 방식을 채택하여 전 처리 과정을 포함한 방식으로 1채널부터 4채널까지 하 나씩 채널을 추가하는 실험을 하나의 모델로 분석한다. 직접 뇌파를 측정하며 뇌파 채널의 부착위치를 수면다 원검사에서 사용하는 전극의 부착위치와 비슷하게 선 택한다. 실험의 결과로 수면케어 시스템에 적합한 뇌파 채널 개수를 확인하고자 한다. 본 연구에서는 뇌파 전극의 채널 개수와 부착 위치에 따른 수면단계 분석 정확도를 비교한다. 따라서 2장의 연구방법에서는 수면뇌파의 특성에 대해 살펴보고, 1채 널부터 4채널까지 뇌파를 측정하는 방법과 실험방법을 제안한다. 제3장에서는 실험한 결과로 수면단계를 분석 할때 각 채널별로 실험결과를 비교한다. 제4장에서는 실험결과를 해석하고 평가하여 최적의 수면분석용 채 널개수를 확인하며, 제5장에서는 결론을 맺는다.
Dual Battery Control System of Lead Acid and Lithium Ferro Phosphate with Switching Technique

The increase in electric vehicles needs to be supported by the existence of reliable energy storage devices. The battery, as an energy storage system, has its advantages and disadvantages. The combination of different battery types is chosen since the battery is one of the energy storage systems with mature technology and low life cycle cost. A solution that can be proposed to cover the weakness of each battery is the use of the Dual Battery System (DBS). In this project, a dual battery control system with a combination of Valve Regulated Lead Acid (VRLA) and Lithium Ferro Phosphate (LFP) batteries was developed using the switching method. Battery selection switching is determined by the specification and operational set point of the battery used. The experimental testing was carried out. The result of the research conducted showed that the current sensor accuracy was 83.75% and the voltage sensor accuracy was 94.25% while the current sensor precision value was 64.91% and the voltage sensor precision was 99.74%. The use of a dual battery system can save energy in a VLRA battery compare with a single VLRA battery by up to 68.62%, whereas in LFP battery by up to 29.48%. This means it gives the advantages of longer distances of traveling in electric vehicles.
Digital Learning Environment in Higher Education: New Global Issues

Research background: Living in a digitalized connected world changes the dynamic of the learning process. Recent issues in the world economy and society have increased the demand for online education drastically. But many enterprises in higher education find themselves unprepared to satisfy this demand effectively because digital learning is a relatively new and under-researched field especially regarding digital instruments and social aspects. For this study we adopted connectivism learning theory proposed by George Siemens in 2005 together with the “old” behaviourism, cognitivism, and constructivism theory put forward by Ertmer and Newby in 1993. Purpose of the article: The study intended to investigate elements of digital learning process using student feedback and to suggest ways to improve the quality of digital higher education based on learning theories applied to digital environment. Methods: A total of 2347 university students participated in this study. Data were collected through an online survey and were analysed with the use of SPSS software. Findings & Value added: The study revealed that students pinpoint three groups of problems in online learning: the process of getting feedback form tutor and peers; the process of concentration and self-management; the technical difficulties of staying connected. The paper studies the theoretical background of problematic areas and proposes tools for their improvement.
A COMPACT BROADBAND FOLDED DIPOLE ANTENNA ELEMENT WITH BALL GRID ARRAY PACKAGING FOR NEW 5G APPLICATION

Abstract—A compact broadband folded dipole antenna element with a ball grid array packaging is proposed in this letter. The compact antenna element is fabricated on a low-cost FR4 substrate consisting of only one dielectric layer. The solder balls are mounted on the square ground metal plane of the antenna element to form the ball grid array (BGA) packaging, which allows the antenna element to be surface mounted with other surface-mount devices (SMDs). Furthermore, ball grid array packaging has great potential for minimizing the size of antenna elements. The dimension of the proposed antenna element is only 6mm × 6mm × 1.6 mm. Parameter analysis shows that the solder balls have little effect on antenna performance. The proposed folded dipole antenna element is fed by a 50 Ω grounded coplanar waveguide (GCPW) transmission line on the evaluation board. The antenna prototype has been designed, analyzed, and manufactured. Measured results of the prototype show that the −10 dB impedance bandwidth is 45.4% (22.3–35.4 GHz), and the peak gain achieves 6.62 dBi at 35 GHz. The measurement results show that the proposed antenna element has great potential for the 5G millimeter wave application.