Miracle-dz/UniFAR-FTUs huggingface dataset:
Model-Free Adaptive Integral Sliding Mode Control for 4-DOF Tower Crane Systems

An adaptive integral sliding mode control (AISMC) method with payload sway reduction is presented for 4-DOF tower cranes in this paper. The designed controller consists of three parts: the integral sliding mode control (ISMC) is used to provide the robust behavior; the adaptive control is utilized to present the adaptive performance; the swing-damping term is added to suppress and eliminate the payload swing angles. Different from existing sliding mode control (SMC) methods, the proposed AISMC method doesn’t include the switching action, therefore, it is continuous and chattering free. Moreover, a prior knowledge of the system parameters including the payload mass, the trolley mass, the cable length, the moment of inertia of the jib, the friction-related coefficients is not required for the designed controller thanks to the adaptive control. Lyapunov-based analysis and LaSalle’s invariance principle are employed to support the theoretical derivations without linearizing the nonlinear dynamics. Experimental results are illustrated to show the superior control performance of the designed controller.
Relative Pose Estimation of Visual SLAM Based on Convolutional Neural Networks

Pose estimation is the core module of the visual SLAM system, which is usually calculated by the front-end visual odometry. The calculated pose gives the initial estimation of the camera pose that is the basis for later map construction and path planning. The target of pose estimation is to calculate the relative motion between two frames. The absolute pose of the latter frame can be calculated according to the absolute pose of the previous frame and the relative transformation matrix of the two frames. The main methods for calculating the relative motion of two frames are feature-based method and direct method, and both of them have been successfully applied in various visual SLAM systems. But they all have the disadvantage of needing to carefully adjust the parameters, and sensitive to environment and illumination changes, image blur and other factors. With the great success of deep learning in the field of computer vision, researchers began to explore the use of deep learning methods to solve pose estimation problems. This paper proposes a neural network model for calculating the relative motion of two frames. The training and testing are carried out on a public dataset. The experimental results show that the model shows good performance for known scenes and has certain generalization ability for unknown environments, proving that deep learning methods have the potential to solve the problem of pose estimation.
Energy Regeneration during Deceleration of Direct Torque Control of Induction Motor Drive for Electric Vehicles

In this paper, an energy regeneration during deceleration of direct torque control based induction motor drive for a heavy electric vehicle is presented. Charging and discharging of supercapacitor by bidirectional converter during acceleration and deceleration is studied. DC bus voltage observed during deceleration or braking. The energy recovered in supercapacitor in every deceleration is fed back through bidirectional converter during a transient increase of loading or during acceleration. Energy recovery during deceleration and braking makes energy efficient direct torque controlled induction motor drive.
A Novel Feedback Mechanism-Based Stereo Visual-Inertial SLAM

Simultaneous Localization and Mapping (SLAM) combining visual and inertial measurements has achieved significant attention in the community of Robotics and Computer Vision. However, it is still a challenge to balance real-time requirements and accuracy. Therefore, this paper proposes a feedback mechanism for stereo Visual-Inertial SLAM (VISLAM) to provide accurate and real-time motion estimation and map reconstruction. The key idea of the feedback mechanism is that the frontend and backend in the VISLAM system can promote each other. The results of the backend optimization are fed back to the Kalman Filter (KF)-based frontend to reduce the motion estimate error caused by the well-known linearization of the KF estimator. Conversely, this more accurate motion estimate of the frontend can accelerate the backend optimization since it provides a more accurate initial state for the backend. In addition, we design a relocalization and continued SLAM framework with the feedback mechanism for the application of autonomous robot navigation or continuing SLAM. We evaluated the performance of the proposed VISLAM system through experiments on public EuRoC dataset and real-world environments. The experimental results demonstrate that our system is a promising VISLAM system compared with other state-of-the-art VISLAM systems in terms of both computing cost and accuracy.
The caring child: an evaluative case study of the Cornwall Young Carers project

This paper reports on an evaluation of the Cornwall Young Carers project (jointly funded by social services and the Cornwall and Isles of Scilly Health Action Zone), conducted during the period 2000–2002. A diverse range of methodological approaches were adopted (comparative national statistics, local pilot study, monthly and quarterly data capturing instruments, and a service user focus group) in monitoring and evaluating the project's high level statements/outcomes. Four evaluative themes are discussed, namely: identification of young carers; assessment of young carers' needs; direct service provision; and partnership working. Recommendations and action steps are proposed with a view of informing future planning and service delivery, as well as developing an ethos of integrating evaluation into everyday practice for the Cornwall Young Carers Project. Overall, the project has made significant progress in meeting and exceeding its original targets. The fact that the project has been able to identify 202 new young carers over this period is a key milestone in demonstrating the critical need for the project's services. It is suggested that existing referrals are only the ‘tip of the iceberg’ for work with young carers in Cornwall.
Unit-cell design for antenna arrays efficiently matched to uni-travelling-carrier photodiodes

We present an antenna array with a backing reflector that allows one to obtain efficient matching to integrated sources or loads with low input resistance. In the infinite array limit, it is possible to describe the proposed unit-cell as an equivalent network with closed-form expressions for its different constituents. This analytic approach enables the preliminary design of arrays with improved matching efficiency for optimum power transmission/reception. The proposed solution has enabled an improved matching to a uni-travelling-carrier photodiode with a maximum improvement of 3 dB in the radiated power with respect to a $72-\Omega$ antenna, and featuring a 50% bandwidth.
High-Efficiency High-Repetition-Rate Gain-Switched Operation Around 3 μm in Cr2+:CdSe Single-Crystal Laser Pumped by Fiber-Laser-Pumped Ho3+:YAG Laser at 2.1 μm

The expansion of laser sources operating near 3 μm is motivated in large by their strong absorption in several gases, liquid water, and plastics, critical for applications in remote sensing, soft and hard tissue surgery, and materials processing [1,2]. Both CW and pulsed operations were demonstrated in Cr2+:CdSe lasers with wide tunability range spanning from 2 μm to 3.6 μm. Different pump sources were used, such as Tm3+- and Tm3+,Ho3+-doped crystal lasers, diodes and Tm-fiber lasers [3,4]. The relatively short lifetime of the upper laser level and low damage threshold of Cr2+:CdSe crystal surface limit maximum pulse energy and average power in the repetitively-pulsed Cr2+:CdSe oscillators [2–4]. The highest average power achieved so far in the Cr2+:CdSe lasers was less than 1 W at kHz pulse repetition rate (PRR). In this report, we present an efficient high-repetition-rate Cr2+:CdSe single-crystal laser operating around 3 μm with the output power of up to 6 W and pumped at 2.1 μm by a fiber-laser-pumped Ho3+:YAG laser.
Unstructured Lane Identification Based on Hough Transform and Improved Region Growing

With the continuous development of intelligent driving technology, vision-based navigation technology is becoming increasingly significant. Lane detection is the key part of the navigation technology. At present, most of the researches focus on the identification of structured lane, such as highways. However, study on the detection of unstructured lane is relatively few, mainly because unstructured lanes have no obvious marks, which brings difficulties in identification. Therefore, unstructured lane detection still faces serious challenges now. In this paper, region of interest (ROI) of original RGB image is first selected. Next step is RGB image conversion to a grayscale. Then, Gaussian filter and mean filter are used to smooth the structured lane image and unstructured lane image, respectively. Finally, applying edge detection and Hough transform (HT) to extract the unstructured straight lane. Meanwhile, improved region growing and least square (LS) are used to extract unstructured curved lane. The experiment results on the roads of a campus show that the presented algorithms are effective, which can accurately extract the road area and road boundary.
ATOMOS: An ATomistic MOdelling Solver for dissipative DFT transport in ultra-scaled HfS2 and Black phosphorus MOSFETs

A state-of-the-art DFT-NEGF based ATOmistic - MOdelling Solver (ATOMOS) was developed and used to assess the physics and fundamental-performance potential of various scaled mono-layer transition-metal-dichalcogenides and blackphosphorus (BP) MOSFETs down to a gate length of 5 nm, including the effect of electron-phonon scattering. Our study highlights the good scalability and drive-current potential of HfS2 and the impact of optical-phonon scattering for BP.
Analysis and Control of Energy Storage Systems for Power System Stability Enhancement

Utility-scale energy storage systems (ESSs) have been widely used in power system to facilitate its secure and economic operation. This paper investigates the transient influence of ESSs on bulk power systems. ESSs are treated as a controllable current source, whose dynamic response is represented by a first order transfer function. The influence of ESSs on the system dynamics is analyzed on a single machine infinite bus (SMIB) system with a ESS connected at its transmission line. The analytical result indicates that the deviated electrical power of synchronous machine (SG) caused by ESS is linear to both the d-axis and q-axis current of ESS. Based on this result, a control strategy of ESS’s reference current is proposed to improve the system damping. Case studies are performed on both SMIB system and the 2-Area 4-Generator system to validate the correctness of the analysis and the effectiveness of proposed ESS control strategy.
An Intelligent System for the Classification of Lung Cancer Based on Deep Learning Strategy

This study intends to develop an easy-to-use software using deep learning algorithms classification of benign and malignant lung nodules with high accuracy. In modelling phase Keras library and convolutional neural networks model are used. To construct user interface, shiny web framework is utilized. Experimental findings showed that the convolutional neural network model has high classification performance metrics of benign and malignant lung nodules on computed tomography images. As future research, it will be aimed to develop new software for medical images of other types of definitely diagnosed diseases by using appropriate deep learning models. The developed software is available at: biostatapps.inonu. edu.tr/AKSY/
Towards combining a neocortex model with entorhinal grid cells for mobile robot localization

Motion and navigation are fundamental abilities of all terrestrial animals. It is essential for foraging, reproduction, and more generally for survival. There are a couple of strategies to conduct navigation from simpler visual homing in ants to more complex and cognitive demanding techniques in mammals. Many species of mammals use several specialized cell types in the hippocampus and the entorhinal cortex to represent space in the brain like head direction cells to encode their orientation and grid cells to keep track of their position. In our recent work, we presented MCN - an algorithm that is inspired by working principles of the human neocortex for the navigational subtask visual place recognition. MCN makes decisions based merely on camera data without odometry about whether or not a currently visited place has been seen in the past. In this work, we intend to answer the question if we can combine our neocortex-inspired model with entorhinal cortex cells for space representation to exploit additional metric data like odometry in our system. We believe that the combination of bio-inspired techniques could help someday to create a biologically plausible and more robust navigation system like in animals. In this paper, we give an introduction to our neocortex-inspired algorithm MCN and to two cell types of the entorhinal cortex, answer how these concepts can be combined to perform visual place recognition, and provide proof-of-concept experiments with a mobile robot to show the performance of the proposed system.
MPPT Control of a Photovoltaic System based on Sliding Mode Control

In this paper, we introduce a PV system consisting of a photovoltaic panel and a boost converter. The output of photovoltaic systems is not fixed. It varies according to the temperature of the solar cells and the solar radiation. Generally, the temperature and solar radiation is not constant and thus affects the output of our panel. Furthermore, we proposed a sliding mode control for our chopper, which will leads to the maximum power point tracking (MPPT). Therefore, we performed simulation tests under different environmental conditions and discussed the results to confirm that slip control was effective to the Perturb and Observe (P&O) method.
Genomic Mechanisms of THz-Induced Cancer Dysregulation in Human Skin

Intense pulses of THz radiation significantly alter gene expression and dysregulate phenotypic endpoints of cancer-related signaling pathways, including activation of an inflammatory response and suppression of pro-mitotic signaling in human skin. Of 1681 potential candidates, the predicted cancer-related dysregulation is nearly entirely localized to a subset of only 42 key driver genes belonging to 7 gene families/superfamilies. The individual and concerted regulatory roles of these genes within the relevant cancer-related pathways are elucidated and discussed in the context of gene-specific response to THz exposure and potential for clinical application.
Development of CNN architecture for Honey Bees Disease Condition

Honey bees are one of the most important pollinators for a wide range of products in the food chain. Today, with the help of developing technology, observing bees healthy controls is a very important field of study. In this study, the images taken in the natural environment of the bees were processed with Convolutional Neural Network (CNN) architecture and the health status of the bees were classified. The results obtained were promising for the studies to be carried out in this area. In addition, the structure of the CNN architectures was studied and the CNN architectures with different types and number of layers were compared with each other. As a result of the comparison, using the ideal number of convolution layers instead of using a great number of convolution layers for CNN architectures, increases the success. In addition, the use of normalization layers that serve as supporters in CNN architectures has been found to be very important for increasing success. In this study, 5 different CNN architectures were developed and the classification results obtained with these architectures were analyzed. Among the architectures developed, KM_1 network architecture has achieved the best results with a success rate of 92,42.
Part of Speech Tagging for Indonesian Language using Bidirectional Long Short-Term Memory

Part of Speech (POS) is a label to distinguish a word based on its grammatical and morphological form. By providing the POS label, we can get the contextual meaning. This label can be used as contextual features for several computational linguistic research – for example, word sense disambiguation, chunking, machine translation, and sequence classification. Our work is done by using bidirectional long short-term memory to do the part of speech tagging task for Bahasa Indonesia. We use deep learning model for Indonesia language POS tagging because deep learning can achieve excellent performance on it. We could reach 96.92% of F1 Score based on our approach.
Research on Gravity Compensation of Robot Arm Based on Model Learning*

In this paper, an algorithm based on model learning to calibrate gravity compensation parameters is proposed. This method can be used to solve the gravity term of the mechanical arm by linear regression method based only on the structure of the arm and the sampling data. The position of the gravity center and the geometric center of each mechanical arm link is not needed. It solves the problem that the model derived from the traditional dynamic modeling method produces a large deviation from the actual model. Theoretical analysis shows that the joint angle and joint driving torque can accurately regress the relationship between the barycentric coordinates of the link and the mass of the connecting rod in the Cartesian coordinate system, thus realizing the gravity compensation of the manipulator. The optimal solution on the optimization trade-off curve is presented and discussed.
BER Improvement Using the Better than Double-Jump 2 Pulse in OFDM Schemes Prone to Frequency Offset

Orthogonal frequency division multiplexing (OFDM) is highly sensitive to frequency offset since it induces inter-carrier interference (ICI). Fortunately, the pulse shaping filter method has proven its potential to reduce ICI without the cost of increasing the complexity of the OFDM modem. In order to improve the OFDM tolerance against the oscillator frequency detuning, we introduce the better than double jump 2 (BTDJ2) pulse. It is characterized by one additional design parameter apart from the roll-off factor. In addition, the improved filter is optimized following a representative system configuration. The BTDJ2 is compared with other novel pulses in terms of signal to noise ratio (SNR) and frequency offset requirements for a well known practical forward error correction (FEC) limit. Further, complexity evaluation of the BTDJ2 and other novel pulses is done in terms of the average elapsed convolution time. In general, the sub-optimum BTDJ2 outperforms other novel pulses in terms of the bit error rate (BER) and complexity.
An Improved Neighborhood-Based Recommendation Algorithm Optimization with Clustering Analysis and Latent Factor Model

With the development of Internet, information overload about products is pervasive. It is important for commercial platforms to predict users’ preferences and recommend information. Neighborhood-based recommendation algorithm is one of the most popular methods, which are used to predict the rating of items that have not yet been rated. However, neighborhood-based algorithm suffers from data sparsity in practice, causing low accuracy. A recommendation algorithm is proposed in this paper, in which auxiliary information of users and items are utilized. Specifically, clustering method is introduced to divide users into categories, which reduces the time complexity of the algorithm, and latent factor model approach is applied to predict user-item matrix, which improves the accuracy of neighborhood-based algorithm. Experiments on real-world dataset demonstrate that the proposed KC-LFMCF approach is superior to the conventional neighborhood-based algorithm in terms of recommendation accuracy and time complexity.
Doppler Effect Mitigation Using Spectral Temporal Average Estimation on V2V Channel with Moving Scatterer

The movement of the vehicle on the transmitter and receiver side of the V2V communication system produces a Doppler effect. The Doppler Effect increases when the scatterers around the transmitter and receiver vehicles also moves. The greater the value of the Doppler Effect will affect the Coherence Time parameter becomes smaller, so the communication channel change faster. This makes the estimation process on the receiver side more difficult to implement. The V2V channel model that accommodates the movement of the transmitter, receiver and scatterers vehicles around it produces a Doppler effect that has never been addressed before. This study uses the Spectral-Temporal Average (STA) estimation method to mitigate the Doppler Effect that appears in the modeling of the V2V channel used. The mitigation results show improved error estimation in the speed range 10–28 m/s and in the range 30–50 m/s. In addition, constant phase changes occur throughout the time of observation causing estimation errors to increase in the Long Training Symbol estimation and Symbol Data estimation.
ADRC vs LADRC for quadrotor UAV with wind disturbances

The wind is considered a strong disturbance for quadrotor aircraft. This paper presents two types of double closed-loop control scheme based on ADRC or LADRC for quadrotor aircrafts with wind disturbances, and the comparison and analysis are discussed. Firstly, the turbulent wind field is generated according to the Dryden model and a dynamic model is established via Newton-Euler. Secondly, double closed-loop control schemes based on ADRC or LADRC for quadrotor aircraft are presented respectively. Finally, we evaluate the performance and effectiveness of these control schemes in maintaining stability and tracking the target trajectory. The results of simulation show that the model proposed in the paper accurately reflects the dynamic performance of the quadrotor under the impact of turbulent wind, and the control schemes are effective for quadrotor and they can estimate and compensate for the wind disturbances.
Experimental Demonstration of a Non-Quantized Square-Root Topological Insulator using Photonic Aharonov-Bohm Cages

In many applications, topological effects can be characterized by a quantized topological invariant [1]. In this work, we investigate a new type of topological insulator that stands apart from this formalism, since it exhibits spectral bands with non-quantized topological properties. As it turns out, this system can nevertheless be reconciled with the existing paradigm, since a quantization can be established by squaring the Hamiltonian Fig. 1(b).