Miracle-dz/UniFAR-FTUs huggingface dataset:
Influence of Organic Carbon Sources on Growth and Lipid Content of Marine Green Alga Dunaliella tertiolecta

This study investigated the potential use of various organic carbon sources (glucose, glycerol and acetate) and different concentrations of CO₂ for culturing marine microalga Dunaliella tertiolecta. Cell growth and lipid production were monitored under heterotrophic, mixotrophic and photoautotrophic modes of cultivation. D. tertiolecta showed the ability to grow under mixotrophic (acetate and glucose), heterotrophic (glucose) and photoautotrophic condition under high CO₂ concentration (15%). With all the organic carbon sources (glucose, glycerol and acetate) tested in this study, 1~5% acetate enhanced cell growth rate and lipid content, while higher concentrations of acetate (10% and 15%) were inhibitory and resulted in cell death.
Advancements in Microgrid Voltage Control Schemes

Due to the intermittent behaviour of the renewable energy sources, containing the voltage rise issues and maintaining voltage regulation throughout the microgrids is of paramount importance. This paper presents a detailed literature survey on the available voltage control schemes. A unique approach for classifying and analyzing the voltage control techniques of active distribution system and/ or microgrid, on the basis of their time scale, is proposed as short term voltage control and long term voltage control. Various architectures of their field implementation as decentralized, centralized and distributed are reviewed and discussed. Furthermore, the future trends in the voltage control are also reported.
A Review on 5G Technological Intervention in Smart Grid

After progressing through the fog and edge computing, and Software Defined Networking (SDN), 5G is leading the way to the next paradigm shift in the networking industry. Several pioneering technologies such as network virtualization and softwarization, and network slicing are blossoming in the networking domain. The integrated intervention of these upcoming 5G technologies is leading to advancements in the Internet of Things (IoT) and Information and Communications Technology (ICT) infrastructure. The smart grid is one of the major vertical which will get a revamp in its communication network infrastructure. The 5G network slicing technology can well address challenges to the communication networks raised by the smart grid services such as orchestration flexibility, reliable isolated networks, security, and ultra-low latency networks. The technological intervention of the 5G network slicing to the smart grid, along with the service management and security aspects, leveraging blockchain network architecture, is reviewed in this paper. A novel conjoint architecture integrating all the network roadmap modernization is also presented. Moreover, technical resource allocation approaches for dynamic allocation of the network slices in the proposed architecture are analyzed.
Path Planning for Unmanned Surface Vehicle with Dubins Curve based on GA

Path planning is a common method for unmanned surface vehicle (USV) to carry out multi-task navigation. The appropriate path can effectively ensure the safety and stability of unmanned ship in the navigation process, and reduce the cost loss. In this paper, the task execution process of the USV is considered as a TSP problem, and combined with its trajectory characteristics, the navigation time and distance are minimized. Its trajectory that affected by many factors must be analyzed. By Combining Dubins curve with USV's trajectory, the relationship model between trajectory angle and navigation speed is established, and genetic algorithm is used to solve the task execution sequence and path in the navigation process according to the navigation time and distance. In addition, this article also considers the possible obstacles in USV task planning, and solves the problem by adding new task points. The paper proposes a method to solve the path planning problem in the execution of multi-task for USV, and overcomes the problems of trajectory discontinuity and inconvenient navigation in common methods. At the same time, considering the obstacles in the navigation process, the path planning results are more in line with the actual navigation needs of ships.
Performance Analysis of LFV Soft Landing Guidance Method Based on Convex Optimization

This paper analyzed the lunar soft landing guidance performance of lunar maneuvering flying vehicle (LFV) based on convex optimization. To this end, a fuel optimization model for the lunar soft landing phase is firstly established. By using convex optimization theory, it is then transformed into a second-order cone optimization problem, which can be sovled via mature convex optimization tools. Based on this, various factors including landing time, throttle range and initial error, are analyzed to investigate their impacts on the guidance performance. The simulation results indicate that the guidance method based on convex optimization is able to meet mission requirements with any landing accuracy even in large landing time range, various throttle range and large initial error, and thus hold good mission adaptability.
Synthesis and characterization of ternary zinc-antimony-transition metal spinels

Abstract Four series of ternary Zn x Me y Sb z O 4 spinels, Me = Cr, Mn, Co, or Ni, y ≤ 0.778, have been prepared by solid state reaction from appropriate oxides. The spinels were characterized by the X-ray powder diffraction method. The efficiency of Me ions to form spinel structure measured by the temperature needed to obtain the pure spinel phase decreases in the order Cr ≈ Mn > Co > Ni. The reaction temperature also becomes lower with increasing content of Me ions. The spinel lattice constants decrease linearly with increasing Me content, while the slope of curves depends on the ionic radii of Me ions. These results allow the lattice constant of more complex spinels to be estimated. Crystal structures of (Zn 1.000 )[Zn 0.633 Co 0.700 Sb 0.667 ]O 4 , a = 0.85822(1) nm, and (Zn 1.000 )[Zn 0.816 Cr 0.778 Sb 0.407 ]O 4 , a = 0.84924(1) nm, spinels, where ( ) denotes tetrahedral and [ ] octahedral sites, are determined by Rietveld's method from X-ray powder diffraction data. The results show that Co(II), Cr(III), and Sb(V) are situated in the octahedral site. The tetrahedral site is occupied exclusively by Zn ions. The same cation distribution was postulated for all spinels.
Redundant Dictionary based Pulse Wave Compressed Sensing

With the development of sensor technology and wireless communication technology, pulse wave based medical or healthcare instruments have become smaller, portable, and more intelligent. However, energy efficiency is still one of the obstacles for such devices in long term real-time use. Data compression can reduce energy wasting in communication. It is an efficient method for prolonging the lifetime of wireless pulse wave devices. Here, we present a redundant Gaussian dictionary (RGD) based compressed sensing (CS) framework for pulse signals compression. The dictionary is composed of atoms computed as samples of Gaussian function, and a set of parameters are given out for RGD generation. To verify the advantage of the proposed method, we compare the proposed RGD with discrete cosine transformation (DCT), wavelet transform (WT), fast Fourier transform (FFT), and Gabor dictionary. Meanwhile, experiments are done for the determination of the optimal combination of the proposed dictionary with the measurement matrix and reconstruction algorithm. Numerical experiments demonstrate that the RGD can increase the pulse wave reconstruction quality under a higher compression ratio combined with 1-bit measurement matrix and SL0 reconstruction algorithm, it achieves the best performance in MIMIC database testing, the pulse signals is well reconstructed with up to 82% data reduction.
Modeling of Proton Exchange Membrane Fuel Cell Based on LSTM Neural Network

The proton exchange membrane fuel cell(PEMFC) is a complex nonlinear dynamic system coupled with multiple physical fields. To analyze the external characteristics of the fuel cell system, it is regarded as a black box and the neural network structure is used to identify the fuel cell model. This paper uses long and short-term memory(LSTM) neural network to model the PEMFC system, the model’s input is the cell current, and the model’s output is the cell voltage. First, fit the PEMFC voltage and current characteristic curve under certain operation conditions according to the empirical formula, and train the LSTM neural network through the data obtained above, then continuously optimize the relevant parameters of the LSTM neural network to make the LSTM neural network model output and experience after the training is completed the formula fitting output is the same, and the result shows that the LSTM neural network model can effectively reflect the output characteristics of PEMFC.
Knowledge Induced Deep Q-Network for Robot Push and Grasp Manipulation Skills Learning

In the process of robotic push and grasp manipulation skills learning, the prior knowledge of object shape and environmental constraints can be employed to make corresponding manipulation policies. In this paper, a DQN decision model is proposed for learning multi-step push and grasp manipulation of different types of objects. The key of the proposed method is that the prior knowledge of object shape and environmental constraints is introduced into the reward system by a SVM classification model. Therefore, the reward system will continuously motivate the robot to approach to the target manipulation during training process. Simulated and actual verification of the trained model is carried out. The experimental results show that the trained model has a high success rate in grasping cube objects, and it can also successfully complete the task of pushing cylindrical objects to the constraint boundary.
Domain Adaptation from Public Dataset to Robotic Perception Based on Deep Neural Network

A robot needs to understand the environment in a human-like manner in order to provide good service for us. However, the working environment changes from time to time for reasons such as light and layout. As a result, the robot needs to adapt existing knowledge to fit current environment. Deep neural network has shown its advantages in feature extraction which could be used in later processing. In this paper, we use a framework of deep neural network, Mask R-CNN, to endow a robot with the capability of adapting knowledge from public dataset to current environment instead of training from scratch. The effectiveness of our proposed adaptive method is evaluated on an actual humanoid NAO robot’s environment, where the robot could intelligently perceive the classification and localization of objects, as well as their spatial layout even they are placed randomly.
Crystallization Kinetics and Mechanism of Low-Expansion Lithium Aluminosilicate Glass-Ceramics by Dilatometry

The crystallization kinetics and mechanism of a precursor glass of lithium aluminosilicate (LAS)-based commercial low-expansion glass-ceramics were investigated using a dilatometer. The isothermal crystallization behavior of β-quartz solid solution (ss) was found to obey the Avrami equation. Nonisothermal crystallization data were analyzed by the Ozawa method and modified Kissinger equation. The value of the Avrami exponent (n) was ∼1.5, and the apparent activation energy (Ea) was ∼500 kJ/mol, which was close to that of the diffusion of silicon and aluminum ions as well as metal–oxygen bond strengths, suggesting a three-dimensional (3D) diffusion-controlled reaction mechanism.
The Application of IoT layer one Based Mobile Labs in Engineering, Science and Technology Education

Most technical courses mainly in engineering, science and technology, have always been taught using very expensive equipment usually stationed in a particular lab setup in the university with students accessing these labs in groups. The problem is even compounded further in the case where the number of students is high in a class. This paper discusses the use of a mobile lab being a simple, inexpensive and portable kit to undertake practicals in the fields of engineering, science and technology a case study of digital electronics, computer organization, networking and operating systems that are done by students in these fields. The study involved the development of IoT layer one based kit in terms of hardware and software support together with its actual use in a classroom setup. For full implementation, a laptop or a smartphone are also required. The first group of students, who were requested to do an evaluation of the kit at the end of the semester, was undertaking computer science. The results were quite positive showing the link between IoT and a classroom setup making it easy for the students to relate what they learn in class and the many IoT related actual real life applications.
Research on relative positioning system of UAVs Swarm based on distributed UWB

Multi-UAVs swarm is the focus of current research. UWB (ultra-wide band) is very feasible to be applied to UAV(unmanned areial vehicle) relative positioning. The ranging accuracy, refresh frequency and positioning accuracy of UWB are very important to the formation flight of UAV. The paper mainly studies the applicability and main influencing factors of UWB distributed relative positioning for UAV cluster in tight formation flight. First of all, we analyzed the current cluster relative positioning method and UWB application. Meanwhile, we designed a distributed relative measurement system. Then, the measurement error of UWB point to point is studied, the distributed positioning algorithm is designed and the error analysis is carried out. Finally, the influence of relative speed and positioning result of UAV is emphatically studied and analyzed through experiments.
Multi-model data fusion for cervical precancerous lesions detection

Cervical cancer ranks fourth among women in the world, seriously affecting women’s health. Colposcopy is one of the main methods to find the location of cervical precancerous lesions, but it requires a lot of doctor resources and requires doctors to have rich experience.In the state of art, the lesion information can usually be recognized by deep learning on colposcopy images stained with acetic acid. However, the colposcopy image contains two stained images(called acetic images and iodine images), use only one of them will prone to false positive regions. Using deep learning to fuse patient acetic images and iodine images helps to improve the detection speed and accuracy of cervical precancerous lesions. To this end, we propose a novel detection framework. In our framework, first, we use target detection algorithm to detect the location of cervical cancer that may exist, secondly, we propose a new method to determine the location of cervical cancer to exclude false positive areas. In order to ensure that the target detection algorithm better learns the characteristics of cervical cancer lesions, we improved the Faster-RCNN algorithm by using methods such as average pooling and retention of decimals generated in the image calculation process. The experimental results show the performance of the proposed HLDNet, which achieves the average accuracy of 86%(sensitivity 82% and specificity 90%) in the test set including 200 real patients.
Pose Measurement of Large Cabin Based on Point Cloud in Multi-robot Assembly

For multi-robot assembly of two large cabins, the pose measurement of cabin is indispensable. Traditionally, this is achieved by installing targets or markers on the cabins, making the process inefficient. To address this, a pose measurement method based on point cloud which can be readily collected by 3D vision sensor is proposed in this paper. The cylinder model of the cabin is firstly established according to its geometry. The point clouds of cabins are segmented from the point cloud scene using RANSAC model fitting. Then the relative pose between two cabins can be calculated using the segmented point clouds and model coefficients. It is validated in the experiment that the proposed method is accurate and robust for pose measurement of large cabins.
Improved Particle Swarm Optimization-based GRU Networks for Short-time Traffic Flow Prediction

Short time traffic flow forecasting is the heart of matter in intelligent transportation system (ITS). Accurate traffic flow prediction can help people to choose trip mode and trip time. Although gated recurrent unit (GRU) has outstanding performance in traffic flow forecasting, but determines the hyperparameters of the GRU rely by experience reduces the predictive effect of the model. This study uses the adaptive learning strategy improved particle swarm optimization (IPSO) algorithm to optimize the hyperparameters of GRU model. The characteristics of traffic data with network topology are matched by this algorithm, so the accuracy of traffic flow prediction can be improved. To verify the reliability of this algorithm, this study construct IPSO-GRU model by the traffic flow data from California department of transportation and compare IPSO-GRU model with other traffic flow forecasting models. The experimental results shows that, the IPSO-GRU model achieves the lowest mean square error (MSE), Mean Absolute Percentage Error (MAPE) and Mean Absolute Error (MAE) compared to conventional GRU model.
Classification of Airborne Laser Bathymetry Data Using Artificial Neural Networks

The current development of technology allows for extensive use of active remote sensing systems in water environment research. The data obtained using airborne bathymetric scanning are mainly used to build digital elevation models of the seabed. As a result, their information potential is largely untapped, because full-waveform data have considerably more information that can be used for classification with different machine learning algorithms. This article presents the process of classification and detection of objects on the seabed using multilayer perceptron neural networks. The features of full waveform and point cloud geometry were considered for network training. The results obtained allow for almost 100% correct classification of water surface and seabed. The seabed object points were also classified with an accuracy of over 80%. The obtained results increase the effectiveness of object detection compared to the other well-known classification algorithms.
A Comparative Approach For Clickbait Detection Using Deep Learning

The use of Online Clickbait in different social media platforms have increased momentarily. Basically, click baits are the eye-catching titles or headlines which exaggerate the facts and make the user to “click” on it. These clickbaits comes in many forms like images, videos also through advertisements. This links will lead you to anonymous websites which contains very little information and create nuisance on the internet. In social media clickbaits are very commonly used and Detection of Clickbait is a very crucial process. This paper proposes a method using a deep learning algorithm namely Convolution Neural Network (CNN) for detecting the clickbaits on the social media platforms. The used method focuses on the textual features which consider the word sequence information and also learns the word meanings from entire dataset. Our Results obtained a high accuracy of 0.82% comparatively better than different Machine Learning algorithms. We also did comparative analysis with the classification algorithm called Random Forest (RF).
Transient in-cylinder air flow estimation of gasoline engine using MAP learning approach

Accurate in-cylinder air mass flow estimation is extremely important for gasoline engines to reduce emissions and increase fuel economy. Since the operating conditions of gasoline engines are changeable because of the complex traffic conditions, it will lead to the precision of air mass flow estimation decreases during transient conditions compared to steady-state conditions. In order to improve the estimation accuracy of in-cylinder air mass flow, the method based on MAP learning algorithm is presented in this paper mainly considering the transient conditions. The proposed method is designed to identify and correct the volumetric efficiency MAP real time during the engine operation, which aims at increasing the estimation accuracy of volumetric efficiency under different operating conditions to improve the transient inlet flow estimation result indirectly. At last, an engine bench test is conducted to verify the estimation effect of the proposed method.
Unsupervised Learning of Radio Map from Imbalanced Crowd-sourced Trajectories

The indoor localization based on WiFi fingerprints has attracted great attention, where the most important but challenging work is the construction of the radio map without the need for floor maps. The rapid popularization of mobile and wearable devices makes the collection of massive trajectory data by crowd-sourcing convenient and cost-effective. However, the crowd-sourced trajectory data are with low quality, which usually have an imbalanced distribution due to the presence of hot spots and restricted zones in the building and the random movements of participants. To address this challenge, in this paper, we propose the Unsupervised Learning Radio Map from Imbalanced Crowd-sourced Trajectories scheme (MapICT) to build radio map. First, the Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) algorithm is suggested to solve the problem of imbalanced distribution of trajectory data. Second, smoothing algorithm is proposed to optimize the clustering result. The MapICT scheme is proven feasible and effective through the simulation experiments and experiments in actual environment without the floor maps, where the locating accuracy of radio map constructed is 9.86% higher than existing methods.
Data-Driven Modeling & Analysis of Dynamic Wake for Wind Farm Control: A Comparison Study

For the study of wind farm and wake effect, the steady-state wake models like FLORIS were proposed and used during wind farm operations to achieve higher wind power utilization and conversion. However, the dynamic performance of the wake should also be involved in favor of better optimizations. In this paper, a data-driven analysis and modeling method, dynamic mode decomposition (DMD), is used to construct dynamic flow model with high-fidelity flow data. Two DMD-derived models are constructed based on flow data in three-dimensional and two-dimensional spaces, respectively. The obtained models and respective flows are compared in the time and frequency domain. Results show that both models have apparent differences in the frequency domain, but the dominant wake characteristics’ consistency is maintained.
Hardware Security for eXtended Merkle Signature Scheme Using SRAM-based PUFs and TRNGs

Due to the expansion of the Internet of Things (IoT), there is an increasing number of interconnected devices around us. Integrity, authentication and non-repudiation of data exchanged between them is becoming a must. This can be achieved by means of digital signatures. In recent years, the eXtended Merkle Signature Scheme (XMSS) has gained popularity in embedded systems because of its simple implementation, post-quantum security, and minimal security assumptions. From a hardware point of view, the security of digital signatures strongly depends on how the private keys are generated and stored. In this work, we propose the use of SRAMs as True Random Generators (TRNGs) and Physically Unclonable Functions (PUFs) to generate and reconstruct XMSS keys in a trusted way. We achieve a low-cost solution that only adds lightweight operations to the signature itself, such as repetition decoding and XORing, and does not require additional hardware (like secure non-volatile memories) since the manufacturing variations of the SRAM inside the IoT device are exploited. As a proof of concept, the solution was implemented in an IoT board based on the ESP32 microcontroller.