Miracle-dz/UniFAR-FTUs huggingface dataset:
CNN-SVM Spectrum Sensing in Cognitive Radio Based on Signal Covariance Matrix

Among the basic components of cognitive radio technology is spectrum sensing technology. Cooperative spectrum sensing is also a crucial factor research directions of spectrum sensing. Due to the problems of channel fading and hidden nodes, the spectrum sensing based on a single node cannot meet the requirements of the cognitive radio system for the primary user (PU) detection performance. A spectrum sensing algorithm is based on CNN-SVM and covariance matrix. In this algorithm, the covariance matrix of the signal is input into the convolutional neural network for feature extraction. Secondly, the result of the fully connected layer is taken as the input of the support vector machine for training to obtain a classifier, and finally, spectrum sensing is performed. The simulation outcomes demonstrate that the algorithm suggested in this research works outperform other algorithms in this paper. When the signal-to-noise ratio is -14 dB, the false alarm probability is 0.1, the number of secondary users is 32, and the detection probability reaches 0.81.
Emission factor for emission reduction estimation in reduced impact logging

Indonesia has a forest area of 125.92 million hectares and Production Forest has the largest area, which is approximately 55% of the total forest area of Indonesia. To ensure sustainability of forest management, the use of a logging system that provides minimal damage is highly desirable. Reduced Impact Logging (RIL) as one of the logging systems can minimize the impact of damage due to logging. It is important to provide the emission factor for emission reduction estimation in RIL to support achievement of Indonesian NDC target for net sinker in 2030. This research aims to estimate the emission factor of stands stock in forest concession areas that have applied conventional logging systems and RIL. The circular plot was used to conduct a vegetation survey in concessionaries areas in Central Kalimantan Province and the data collected were then analyzed further for carbon stock and emission factor for emission reduction. The results of the research in study sites showed that emission factor for emission reduction estimation in RIL compare to conventional logging (CL) amounted 30.03 tonC/ha and equivalent 110.2 tonCO2eq/ha or 55.71%. Thus, this value can be used as emission factor to estimate the emission reduction locally in RIL compare to the CL in Central Kalimantan.
Enhancing High-School Students' Computational Thinking with Educational Robotics Learning

In recent years, engineering education has become one of the challenging issues in Thailand education. By teaching and learning subjects independently, students are limited to subject-oriented problems, which are not in reality. To address these flaws, integrating knowledge across disciplines becomes significantly necessary. In the past years, STEM has been accepted as one of the effective strategies to bridge the difference in nature of each field to construct more practical projects and innovations. Moreover, the educational robot can be employed as a learning tool in that strategy since it not only provides challenging learning missions but also promotes computational thinking for the students. Therefore, this research study proposed STEM learning activities on three-day workshop to lay out the foundation to the high-school science-and-technology students who are becoming the engineering students. The workshop comprises of eight phases to facilitate their learning inquiry process with hands-on experience; moreover, the activities were designed in consideration of promoting computational thinking with challenging learning missions. The findings of this study showed that the proposed workshop activities are beneficial for the students who outperformed on robotics with higher computational thinking; meanwhile, they could provide more relevant responses regarding the proposed learning activities.
Pulsar-candidate Selection Using a Generative Adversarial Network and ResNeXt

Pulsar research has been a hot topic in the area of astronomy since they were first discovered. Pulsar discovery is fundamental for pulsar research. While pulsars are now visible across the electromagnetic spectrum, pulsar searches with modern radio telescopes are most promising. As the performance of astronomical instruments improves, the number of pulsar candidates detected by modern radio telescopes grows at an exponential rate. The application of artificial intelligence to the field of pulsar-candidate identification can automatically and efficiently address the identification problem with enormous amounts of data. However, there are still significant challenges in enhancing the accuracy of deep-learning-based pulsar-candidate identification. These problems result primarily from the fact that real pulsar data is scarce: the number of candidates that can be successfully identified as real pulsars (positive samples) is much smaller than those candidates that turn out to not be pulsars but instead radio-frequency interference or noise (negative samples). This makes it difficult to train a machine-learning model that can accurately select those candidates that are real pulsars. Therefore a novel pulsar-candidate identification framework is proposed that combines a deep convolutional generative adversarial neural network (DCGAN) and a deep aggregation residual network (ResNeXt). To overcome sample imbalance, the DCGAN is utilized to generate images that approximate real pulsars, while observed and generated candidates are employed together to train the pulsar-candidate identification model ResNeXt. Experiments on the HTRU Medlat data set back up the framework’s performance. The precision, recall, and F1-score of the framework are 100%.
Study for Identity Losses in Image-to-Image Domain Translation with Cycle-Consistent Generative Adversarial Network

The effect of identity loss in image-to-image domain translation using generative adversarial networks (GAN) remains largely uncertain given its wide application in many successful GAN architectures, e.g., CycleGAN. In this study, negative identity regularization and idempotence regularization are identified from several related studies, and a novel loss, positive identity loss which limits the generative model’s output on the input of positive samples from the source domain, is defined. Results from identity regularized GANs and those without are compared and analyzed. This study concludes that: 1) negative identity loss is helpful in regularizing CycleGAN’s tendency to apply inversible but non-identity transform on non-features such as coloration and background landscapes; 2) positive identity loss significantly increases resolution, details, and affinity with the original image on top of negative identity loss; 3) idempotence loss has positive but limited impacts on output image quality.
Identifying management strategies for the development of wildlife-based ecotourism: A study case of Sulawesi bear cuscus (Ailurops ursinus) inhabited the Educational Forest of Hasanuddin University, Maros

The educational forest of Hasanuddin University is intended to be developed as ecotourism spot with various physical and biological tourism attractions including bear cuscus (Ailurops ursinus). However, the concrete management strategies required have not been fully explored and examined. This research aimed to identify and formulate management strategies of wildlife-based tourism for bear cuscus in the area by examining bio-ecological of the species and analyzing community’s perceptions and attitudes towards bear cuscus and its conservation in the area. Data on the biology and ecology of A. ursinus and its habitat were gained from literatures while the perceptions were obtained directly from questionnaire distribution to all relevant stakeholders as respondents. SWOT analysis was used and further analyzed by using AHP (Analysis Hierarchy Process) to determine quadrants and priority levels. There were 11 strengths and 4 weaknesses found as internal factors and 5 opportunities and 3 threats for the external. The IFAS matrix score were 1.82 with the weighted total value of 3.29 and the weakness’ score of 1.47. For the EFAS matrix, the total opportunities score was 2.95 and the total threats score of 1.00. The high priority strategy to be implemented in the context of ecotourism management was located in Quadrant I which indicates that the opportunity for the bear cuscus' ecotourism is favorable.
Research on Road Condition Sensing Technology based on Vision and Radar Information

In the process of intelligent vehicle driving, due to the complexity of the environment, a single sensor or multiple homogeneous sensors cannot completely perceive the traffic environment around the intelligent vehicle. Therefore, it is necessary to study the information fusion scheme of different sensors and make use of the advantages of each sensor to make up for the deficiency of a single sensor, so as to realize the function of cooperation and mutual compensation between multiple sensors. In this paper, millimeter-wave radar and camera are selected as sensors for an intelligent vehicle to sense the environment ahead. Firstly, in the case of time and space synchronization of the two types of signals, fusion based on the cross-ratio of detection frames is used to find the region of common interest of radar and camera for fusion. Then the fusion based on data association is adopted. The experimental results show that the method based on radar and camera data can effectively realize accurate target recognition and location, and can meet the requirements of real-time accuracy in the application of intelligent vehicles.
Study on Simulation Model of Power Electronic Transformer (PET)

The thesis has completed the following research work: 1. After reviewing the development process, research background, and application field of PET, the advantages of PET are summarized, and then the research object of this paper is AC/DC/AC including DC port and the key technical problems that need to be solved. 2. This thesis has established the mathematical model of PET at all levels of AC/DC/AC type structure. The input stage is composed of a PWM rectifier circuit, and the circuit working principle, mathematical model, and control strategy are analyzed in detail. The isolation stage theoretically analyzes the working modes of the circuit topology in each time period. The output stage consists of a unipolar inverter circuit and an LC filter. 3. In the simulation environment of Matlab/Simulink, according to the design parameters, the simulation model of the main circuit of the PET rectifier stage, isolation stage, and inverter stage, as well as the simulation model of the whole machine system are built under the 1kv voltage level.
Neural Network for Solving Nonlinear Parabolic Differential Equations

We develop a cell-average-based neural network (CANN) method to compute nonlinear differential equations. Using feedforward networks, we can train average solutions from t0 + Δt with initial values. In order to find the optimal parameters for the network, in combination with supervised training, we use a BP algorithm. By the trained network, we may compute the approximate solutions at the time t n+1 with the ones at time tn . Numerical results show CANN method permits a very large time step size for solution evolution.
Research on path planning and scheduling of multi AGVs based on a two-way dual path map

With the development of intralogistics technology in factories, multi-AGV scheduling is still an important problem that needs to be solved by the control system. Aiming at the path planning and scheduling problem of automated guided vehicle (AGV) in the factory loading and unloading work, this paper designs a two-way dual path map construction method suitable for the factory, introduces a two-way different weight map construction method suitable for the right path, and uses “simulated road conditions” algorithm to calculate the shorter path, takes the shortest driving distance as the optimization goal to carry out the task scheduling analyzed possible conflicts and gives corresponding solutions. To verify the correctness of the map construction and algorithm, the multi-AGV simulation is carried out by using a robot operating system (ROS). The results show that the proposed method can realize the stable operation of multi-AGV in the factory environment without conflict and collision.
Pair-sphere trajectories in finite-Reynolds-number shear flow

The pair trajectories of neutrally buoyant rigid spheres immersed in finite-inertia simple-shear flow are described. The trajectories are obtained using the lattice-Boltzmann method to solve the fluid motion, with Newtonian dynamics describing the sphere motions. The inertia is characterized by the shear-flow Reynolds number ${\it Re} \,{=}\,\rho\dot{\gamma}a^2/\mu$, where μ and ρ are the viscosity and density of the fluid respectively, $\dot{\gamma}$ is the shear rate and a is the radius of the larger of the pair of spheres in the case of unequal sizes; the majority of results presented are for pairs of equal radii. Reynolds numbers of 0 ≤ Re ≤ 1 are considered with a focus on inertia at Re = O(0.1). At finite inertia, the topology of the pair trajectories is altered from that predicted at Re = 0, as closed trajectories found in Stokes flow vanish and two new forms of trajectories are observed. These include spiralling and reversing trajectories in addition to largely undisturbed open trajectories. For Re = O(0.1), the limits of the various regions in pair space yielding open, reversing and spiralling trajectories are roughly defined.
Fuzzy Direct Torque Control of Surface Permanent Magnet Synchronous Motor

To solve the problems of large torque and flux ripple in direct torque control (DTC) of permanent magnet synchronous motors (PMSM), a fuzzy controller is used to replace hysteresis comparators and switching tables in the DTC. Different from the conventional fuzzy controller which only outputs the angle or amplitude of the voltage vector, the controller adjusts the angle and value of the voltage vector at the same time in the surface PMSM DTC system is proposed. The inputs and output of the fuzzy controller are: stator flux, torque error, angle and amplitude of voltage vector in stator flux coordinate system. The simulation results of proposed fuzzy control system work normally. Compared with conventional fuzzy control systems, stator flux and torque ripples are decreased and only when motor speed is negative, torque ripple is a little higher. To suppress torque ripple at negative speed, two membership functions of torque error are designed and the fuzzy control system can select the proper membership function according to motor speed. Simulation results show adaptive fuzzy control system can effectively reduce torque and flux ripple in four quadrants.
Autonomy-Based Creative Learning: Equip Creativity and Concern for Prospective Physics Teachers in Wetland Environments

Creativity and environmental care in wetlands have received wide attention from the government and researchers in various countries, especially in the industrial era of 4.0. However, both competencies are less familiar in physics learning. Therefore, this study aims to analyze the effectiveness of Autonomy-Based Creative Learning (ABCL) in improving prospective physics teachers' creativity and caring attitude in wetland environments. This research includes development research using the ADDIE model. The implementation test was conducted on 59 physics education students in South Kalimantan (Indonesia), which were divided into three groups. Before and after learning, prospective physics teachers take a creativity test in designing ABCL, then fill out environmental care questionnaires to determine their attitude on preserving the environment’s energy-saving behaviour, managing waste, loving the environment program, and health protocols. The results show increased students' creativity and caring attitude in the wetland environment. N-gain creativity in the medium criteria, while n-gain care for the environment in the standard serial. This creativity is because students initially have a good understanding of environmental care. Thus, this learning is effectively used to equip the creativity and concern of prospective physics teachers in the wetland environment.
Longest common subsequences of two random sequences

Given two random k-ary sequences of length n, what is f(n,k), the expected length of their longest common subsequence? This problem arises in the study of molecular evolution. We calculate f(n,k) for all k, where n $\leq$ 5 , and f(n,2) where n $\leq$ 10. We study the limiting behavior of $n^{-1}$f(n,k) and derive upper and lower bounds on these limits for all k. Finally we estimate by Monte-Carlo methods f(100,k), f(1000,2) and f(5000,2).
On the interpretation of the spectrum of FU Orionis

Several difficulties with the accretion disk interpretation of the spectra of FU Orionis objects are point out. The central contention of the paper is that the double absorption lines observed in FU Ori and other members of the group, which have been claimed to be the signature of a self-luminous Keplerian disk, can be produced equally well by a single rotating star. Direct support for this assertion comes from observations of similar line structure in several high-luminosity stars (ρ Cas and RW Cep in particular) in which there is no reason to think that a disk exists. The properties of the absorption-line spectrum of FU Ori as observed at a resolution of about 5 × 10 4 are described and compared to simple models of a single rotator with a stratified atmosphere
Path Planning for AUVs with Energy-Optimal Velocity in Current Fields

This paper presents the energy-optimal velocity to solve the problem of energy-optimal path planning for autonomous underwater vehicles (AUVs) in current fields. In the proposed scheme, a sequence of thrust velocities and steering directions is predicted to enable vehicles to utilize/avoid currents and minimize their energy consumption. Each thrust velocity and steering direction are deduced through a vector analysis method under the expected displacement and corresponding current vector, which is the energy-optimal velocity. The proposed energy-optimal velocity combines the global optimization technique and guarantees the minimization of a vehicle’s energy consumption. Several experiments are performed on strong and turbulent current fields to validate the effectiveness of the proposed scheme.
Discretization Methods for Nonconvex Optimal Control Problems with State Constraints

ABSTRACT We consider an optimal control problem described by nonlinear ordinary differential equations, with control and state constraints, including pointwise state constraints. Because no convexity assumptions are made, the problem may have no classical solutions, and it is reformulated in relaxed form. The relaxed control problem is then discretized by using the implicit midpoint scheme, while the controls are approximated by piecewise constant relaxed controls. We first study the behavior in the limit of properties of discrete relaxed optimality, and of discrete relaxed admissibility and extremality. We then apply a penalized conditional descent method to each discrete relaxed problem, and also a corresponding discrete method to the continuous relaxed problem that progressively refines the discretization during the iterations, thus reducing computing time and memory. We prove that accumulation points of sequences generated by these methods are admissible and extremal for the discrete or the continuous problem. Finally, numerical examples are given.
Research on behaviour planning for power substation inspection robot based on fuzzy cognitive map

A fuzzy cognitive map based behavior planning method for mobile robot is proposed in this paper. Aiming at the intelligentialize advanced application problems of power substation equipment inspection robot, a logical structure of inspection tasks planning based on hierarchical hybrid complex system is presented, and the operating principle of task decomposition and task planning are given in detail. The dependent events in inspecting tasks are considered as concept nodes to present the connection weights between nodes and establish the fuzzy cognitive map model of task and behavior planning, so as to realize task decomposition and generate the action logic sequences. Meanwhile, the action sequences can be adjusted in real time with the sensor information. The proposed method is simple without introducing complicated calculations and much training. Simulation results verify the feasibility and effectiveness.
An Isospectral Deformation on an Infranil-Orbifold

Abstract We construct a Laplace isospectral deformation of metrics on an orbifold quotient of a nilmanifold. Each orbifold in the deformation contains singular points with order two isotropy. Isospectrality is obtained by modifying a generalization of Sunada's theorem due to DeTurck and Gordon.
Point cloud registration method based on Particle Swarm Optimization algorithm and improved ICP

In order to achieve accurate wheel gripping by truss manipulators, accurate point cloud alignment of train wheels is required. In this paper, the wheels are detected by binocular laser displacement sensors. And accurate point cloud alignment is achieved by using the point cloud denoising and streamlining algorithm, particle swarm-based algorithm, and improved ICP algorithm for 3D point cloud data. The experimental results show that reducing noise and data volume can improve the stitching effect and alignment efficiency of 3D point cloud models. The algorithm of this paper is experimentally verified to be 43% better in error accuracy and 93% faster in time-consuming than the traditional ICP algorithm, which verifies the effectiveness of its method.
The Simplest Thing That Can Possibly Work: Pseudo-Relevance Feedback Using Text Classification

Motivated by recent commentary that has questioned today's pursuit of ever-more complex models and mathematical formalisms in applied machine learning and whether meaningful empirical progress is actually being made, this paper tries to tackle the decades-old problem of pseudo-relevance feedback with "the simplest thing that can possibly work". I present a technique based on training a document relevance classifier for each information need using pseudo-labels from an initial ranked list and then applying the classifier to rerank the retrieved documents. Experiments demonstrate significant improvements across a number of newswire collections, with initial rankings supplied by "bag of words" BM25 as well as from a well-tuned query expansion model. While this simple technique draws elements from several well-known threads in the literature, to my knowledge this exact combination has not previously been proposed and evaluated.
Unselected quantum information as an effective tool for quantum cryptography

A novel quantum key distribution (QKD) protocol, based on all unselected states of a quantum system, which set the alphabet with continuous set of letters, is proposed. Employing all states of the Hilbert space leads to the maximal quantum uncertainty of transmitted states and therefore an eavesdropper receives the minimal amount of information. For the case of two-dimensional Hilbert space, our protocol allows secure transmission at the error rate higher than that one for the BB84-protocol and comparable with the characteristics of the best known QKD-protocols. However, with increasing the dimensionality of the Hilbert space the critical error rate for our protocol increases and in the limit of infinite-dimensional space the protocol becomes non-threshold.