Miracle-dz/UniFAR-FTUs huggingface dataset:
Sistem Kontrol Pemberian Nutrisi pada Budi Daya Tanaman Aeroponik Berbasis Fuzzy Logic

Metode aeroponik merupakan salah satu cara budi daya tanaman tanpa menggunakan tanah. Aeroponik umumnya digunakan untuk budi daya tanaman jenis sayuran seperti kangkung. Pada metode ini, akar tanaman ditempatkan dalam posisi menggantung. Nutrisi diberikan dalam bentuk ke bagian akar tanaman dengan teknik pengkabutan dengan menggunakan sprayer yang terhubung dengan pompa. Biasanya, pemberian nutrisi diberikan dalam rentang waktu tertentu (timer). Namun dalam sistem ini pemberian nutrisi tidak terlalu efektif. Oleh karena itu, dibuat sistem yang dapat mengontrol pemberian nutrisi sesuai kebutuhan tanaman. Pada sistem ini, yang menjadi parameter yaitu kelembapan dan suhu. Nilai kelembapan dan suhu dari sensor DHT22 akan menjadi input untuk Fuzzy logic, kemudian akan diproses oleh mikrokontroler untuk menghasilkan keluaran sesuai dengan input yang diterima. Nilai output dari mikrokontroler berupa waktu penyalaan akan mengaktifkan pompa yang akan menyuplai larutan nutrisi ke bagian akar. Sistem ini juga dilengkapi dengan data logger, sehingga data dari mikrokontroler dapat disimpan. Alat mampu bekerja menyuplai larutan nutrisi untuk tanaman kangkung dengan set point suhu 33°C sedangkan error suhu yaitu 3,32%-5,28%.
First observation of the charmless beauty decay Λ⁰/b→pKη'

A search is performed for the decay Λb → pKη′ using pp collision data collected with the LHCb detector at centre-of-mass energies of √ s = 7 TeV and √ s = 8 TeV, corresponding to an integrated luminosity of 3 fb−1. The search is performed in two decay channels; the η′ is reconstructed through the decays η′→ π+π−γ and η′→ π+π−η . In the η′→ π+π−γ decay channel 117 ± 15(stat.) ± 10(sys.) signal events are observed and 45 ± 8(stat.) ± 2(sys.) signal events are observed in the η′→ π+π−η decay channel. The combined statistical significance of these signals is 12. 0σ, therefore this is the first observation of the decay Λb→ pKη′ . The branching fraction of the decay Λb→ pKη′ is measured relative to the decay B+→ K+η′ . The ratio of branching fractions is measured to be B(Λb→ pKη′) B(B+→ K+η′) = 0.120± 0.013(stat.)± 0.013(sys.). Using the world average value for the branching fraction of the decay B+→ K+η′ [1], the branching fraction of Λb→ pKη′ is measured to be B(Λb→ pKη′) = 8.48± 0.88(stat.)± 0.97(sys.)× 10−6. This is the first observation of a b-baryon decaying to an η′.
CENTRAL PROCESSING UNIT-GRAPHICS PROCESSING UNIT COMPUTING SCHEME FOR MULTI-OBJECT TRACKING IN SURVEILLANCE

This research work presents a novel central processing unit-graphics processing unit (CPU-GPU) computing scheme for multiple object trackingduring a surveillance operation. This facilitates nonlinear computational jobs to avail completion of computation in minimal processing time forÂ tracking function. The work is divided into two essential objectives. First is to dynamically divide the processing operations into parallel units, andÂ second is to reduce the communication between CPU-GPU processing units.
Electrical properties of an arbitrary 2 × n order fan network

This paper deals with the electrical properties (branch current, node voltage, and equivalent resistance) of arbitrary 2 × n order Fan resistor network by means of the recursion-transform method. Taking the branch current as a variable, the matrix equation model and the boundary condition equation model are firstly established by applying Kirchhoff’s laws. Then the general solution and special solution of the matrix difference equation are studied by the matrix transformation. After that, the branch current formula of 2 × n order Fan network, the voltage formula of arbitrary node, and the equivalent resistance formula between any two nodes are all derived. Finally, special cases in the conclusions are analyzed and discussed.
Electrification mechanism of corona charged organic electrets

This paper developed a comprehensive model which can fully explain the surface potential behavior of corona charged organic dielectric electrets. Compared to previous studies, both sides of the corona charged films were measured while they were grounded or free-standing. All films showed surface potential with the same magnitude, but opposite polarity on each side while they were measured with the other side grounded. This indicates that in contrast to the previous incomplete model, believing only the corona side containing injected charges, both sides should contain injected charges with the same magnitude but opposite polarity. This dipolar charge distribution is relatively stable and leads to a constant and controllable potential difference ΔVf between each side of the free-standing measured film. Interestingly, free-standing surface potential varied dramatically for films charged with the same parameters. This large variation is caused by the minor number of free charges, which is only ~0.1% of the corona induced dipolar charges. Therefore, in this model, dipolar charges control the surface potential difference between each side and the minor charges control the absolute value on a certain side. By manipulating them together, the surface potential on both sides can be precisely controlled.
Feature Extraction and Selection for Parsimonious Classifiers With Multiobjective Genetic Programming

The objectives of this paper are to investigate the capability of genetic programming to select and extract linearly separable features when the evolutionary process is guided to achieve the same and to propose an integrated system for that. We decompose a <inline-formula> <tex-math notation="LaTeX">$c$ </tex-math></inline-formula>-class problem into <inline-formula> <tex-math notation="LaTeX">$c$ </tex-math></inline-formula> binary classification problems and evolve <inline-formula> <tex-math notation="LaTeX">$c$ </tex-math></inline-formula> sets of binary classifiers employing a steady-state multiobjective genetic programming with three minimizing objectives. Each binary classifier is composed of a binary tree and a linear support vector machine (SVM). The features extracted by the feature nodes and some of the function nodes of the tree are used to train the SVM. The decision made by the SVM is considered the decision of the corresponding classifier. During crossover and mutation, the SVM-weights are used to determine the usefulness of the corresponding nodes. We also use a fitness function based on Golub’s index to select useful features. To discard less frequently used features, we employ unfitness functions for the feature nodes. We compare our method with 34 classification systems using 18 datasets. The performance of the proposed method is found to be better than 432 out of 570, i.e., 75.79% of comparing cases. Our results confirm that the proposed method is capable of achieving our objectives.
Coherence Gated Laser Ray Tracing Based on a High Speed FPGA Platform

eld, rendering them incapable of differentiating light returned from targets with many layers (such as the human retina). Instead, the measured wavefront is the superposition of the wavefronts returned from each layer. By combining principles from low-coherence interferometry and wavefront sensing, a depth-resolved wavefront sensor may be realised. This allows only light from within the coherence-gate of the interferometer to be measured by the wavefront sensing device. By adjusting the axial position of the coherence-gate, wavefronts from distinct layers of a multi-layer object may be measured. This method has been demonstrated for the Shack-Hartmann wavefront sensor but requires an external PC for image processing and wavefront reconstruction. This dissertation presents, for the first time, a depth-resolved laser ray tracing wavefront sensor. Results are shown, in the form of Zernike modes, which demonstrate the ability of the instrument to resolve wavefronts from a multi-layer target (two stacked microscope slides and a mirror). Also, an FPGA based embedded system was developed for all command, control, image processing and wavefront reconstruction functions. This highly specialised system is able to perform these operations in real-time, limited only by the frame rate of the available camera. Specfic attention is given to the portion of the system focused on wavefront reconstruction. Zernike modes are commonly used in adaptive optics systems to represent optical wavefronts. However, real-time calculation of Zernike modes is time consuming due to two factors: the large factorial components in the radial polynomials used to define them, and the large inverse matrix calculation needed for the linear t. This dissertation presents an efficient parallel method for calculating Zernike coefficients from phase gradients and its real-time implementation using an FPGA by pre-calculation and storage of subsections of the large inverse matrix. The architecture exploits symmetries within the Zernike modes to achieve a significant reduction in memory requirements and a speed-up of 2.9 when compared to published results utilising a 2D-FFT method for a grid size of 8 x 8. Analysis of the processor element's internal word length requirements show that 24-bit precision in pre-calculated values of the Zernike mode partial derivatives ensures less than 0.5% error per Zernike coefficient and an overall error of less than 1%. The design has been synthesized on a X
Sensitivity analysis and optimization in low order thermoacoustic models

Lean combustion technologies in gas turbines reduce the generation of NOx but increase the susceptibility to thermoacoustic oscillations. These oscillations can produce structural damage and need to be eliminated. The stability of a given configuration can be examined with a thermoacoustic model. In this thesis a wave-based network model is used. Using adjoint methods the gradients of the eigenvalue with respect to system parameters can be obtained at a low computational cost. This information is used as an input to an optimization routine to find stable thermoacoustic configurations. In this thesis thermocaoustic oscillations are analysed using a linear low order network model. This modelling approach is used to predict the unstable modes of five different configurations: a Rijke tube, a choked combustor, a longitudinal combustor, a generic lean premix prevaporized annular combustor and the laboratory scale annular combustor built in Cambridge University Engineering Department. The continuous and discrete adjoint equations for the low order network model are derived. Using the adjoint equations the sensitivities of the eigenvalues to changes in base state parameters such as time delays, areas, lengths and mean radii are computed. Similarly, the sensitivity of the eigenvalues to the introduction of a feedback device such as a drag mesh or a secondary heat source is investigated. By fitting experimental data to a low Mach number model of the Rijke tube, the predictions of the growth rate and frequency shifts due to the presence of these mechanisms are improved. Finally, using the sensitivity information, two different optimization algorithms are developed to stabilize the thermoacoustic systems. Different stabilization scenarios are presented, showing the changes required in each section of the configurations to eliminate thermoacoustic oscillations. The techniques presented as part of this thesis are readily scalable to more complex models and geometries and the inclusion of further constraints. This demonstrates that adjoint-based sensitivity analysis and optimization could become an indispensable tool for the design of thermoacoustically-stable combustors.
A Trench LDMOS Improved by Quasi Vertical Super Junction and Resistive Field Plate

An improved trench lateral double-diffused MOSFET (T-LDMOS) is proposed. It has a quasi vertical super junction (QVSJ) drift region and adopts a resistive field plate (RFP) to help QVSJ satisfy charge-balance. The realization of RFP barely complicates the device fabrication, but it motivates QVSJ to significantly improve the relationship between breakdown voltage (BV) and specific on-state resistance (<inline-formula> <tex-math notation="LaTeX">${R} _{\mathrm{ON,SP}}$ </tex-math></inline-formula>). The simulation results show that compared with the conventional QVSJ T-LDMOS, the proposed one gains the <inline-formula> <tex-math notation="LaTeX">${R} _{\mathrm{ON,SP}}$ </tex-math></inline-formula> reduced by about 79% under the same BV requirement of about 500 V. It therefore presents an excellent figure of merit (FOM) (FOM = BV<sup>2</sup>/<inline-formula> <tex-math notation="LaTeX">${R} _{\mathrm{ON,SP}}$ </tex-math></inline-formula>, Baliga’s FOM) up to 29.8 MW/cm<sup>2</sup>, which is superior to the prior art and exhibits a bright prospect of saving energy.
Kinerja Penyuluh Pertanian Lapangan (PPL) dan Kaitannya terhadap Pendapatan Usahatani Padi di Kabupaten Banyuasin

Alamsyah et al, 2019. Performance of the Field Agriculture Intructors (PPL) and the Relationship to Farming Income in Banyuasin District. pp. 313-321. The performance of extension workers in this study was by the level of satisfaction of farmers who received agricultural extension services. This research was carried out in Banyuasin Regency. This reasearch aimed was to analyze performance of the field agriculture intructors (PPL) and the relationship to farming income in Banyuasin District. Primary and secondary data collection is carried out in May to July 2018. Implementation of activities includes collection of secondary data and primary data. The sample withdrawal method is done with a simple random sampling method. The results showed that the performance of PPL in Banyuasin District had a score of 22.48 which means that the performance of PPL was good enough, PPl performance was seen from 4 indicators, namely planning, implementation and evaluation. Planning has a score of 5.43 which means that it is not good, when the farmer makes his farming plan for the next planting season PPL does not accompany the farmer, so that the farmers lack guidance in planning their farming, the implementation score has a score of 8.48 which means the performance of the instructor Field agriculture is quite good, because during the growing season PPL visits farmers and provides guidance, and for evaluation indicators that is equal to 8.58, which means that the performance of PPL in conducting supervision and evaluation of farmer farming is good enough. The results of the data analysis there is an unreal relationship between PPL performance and income.
A System-Level Framework for Analytical and Empirical Reliability Exploration of STT-MRAM Caches

Spin-transfer torque magnetic RAM (STT-MRAM) is known as the most promising replacement for static random access memory (SRAM) technology in large last-level cache memories (LLC). Despite its high density, nonvolatility, near-zero leakage power, and immunity to radiation as the major advantages, STT-MRAM-based cache memory suffers from high error rates mainly due to retention failure (RF), read disturbance, and write failure. Existing studies are limited to estimate the rate of only one or two of these error types for STT-MRAM cache. However, the overall vulnerability of STT-MRAM caches, whose estimation is a must to design cost-efficient reliable caches, has not been studied previously. In this paper, we propose a system-level framework for reliability exploration and characterization of errors’ behavior in STT-MRAM caches. To this end, we formulate the cache vulnerability considering the intercorrelation of the error types including RF, read disturbance, and write failure as well as the dependency of error rates to workloads’ behavior and process variations (PVs). Our analysis reveals that STT-MRAM cache vulnerability is highly workload-dependent and varies by orders of magnitude in different cache access patterns. Our analytical study also shows that this vulnerability divergence significantly increases by PVs in STT-MRAM cells. To take the effects of system workloads and PVs into account, we implement the error types in gem5 full-system simulator. The experimental results using a comprehensive set of multiprogrammed workloads from SPEC CPU2006 benchmark suite on a quad-core processor show that the total error rate in a shared STT-MRAM LLC varies by 32.0× for different workloads. A further 6.5× vulnerability variation is observed when considering PVs in the STT-MRAM cells. In addition, the contribution of each error type in total LLC vulnerability highly varies in different cache access patterns and moreover, error rates are differently affected by PVs. The proposed analytical and empirical studies can significantly help system architects for efficient utilization of error mitigation techniques and designing highly reliable and low-cost STT-MRAM LLCs.
Towards real-time human participation in virtual evacuation through a validated simulation tool

The analysis of the ship layout from the point of view of safe and orderly evacuation represents an important step in ship design, which can be carried out through agent-based evacuation simulation tools, typically run in batch mode. Introducing the possibility for humans to interactively participate in a simulated evacuation process together with computer-controlled agents can open a series of interesting possibilities for design, research and development. To this aim, this article presents the development of a validated agent-based evacuation simulation tool which allows real-time human participation through immersive virtual reality. The main characteristics of the underlying social-force-based modelling technique are described. The tool is verified and validated by making reference to International Maritime Organization test cases, experimental data and FDS + Evac simulations. The first approach for supporting real-time human participation is then presented. An initial experiment embedding immersive virtual reality human participation is described, together with outcomes regarding comparisons between human-controlled avatars and computer-controlled agents. Results from this initial testing are encouraging in pursuing the use of virtual reality as a tool to obtain information on human behaviour during evacuation.
A Correction Algorithm of QR Code on Cylindrical Surface

When capturing a QR code on a cylinder, there may be geometric distortions due to the angle of the camera and the cylindrical deformation, which makes the QR code difficult to recognize. To solve this problem, a correction algorithm for QR codes is proposed in this paper. First, the boundary of the QR code on the cylinder is extracted by a morphological algorithm and geometry calculations. Then, the feature points on the image are accurately located. Next, the standard size of the QR code is determined by using the cross-ratio method in descriptive geometry. Finally, the image is corrected by perspective projection. It is proven that the algorithm can correct the distortion of the cylinder image effectively, and it has the ability to properly identify cylindrical QR codes.
Circularly Polarized Fabry–Perot Antenna Using a Hybrid Leaky-Wave Mode

This paper presents the spectral Green’s function (GF) to analyze Fabry–Perot antennas to generate circular polarization (CP). The structure consists of a single cavity created between a partially reflective surface and a high impedance surface, fed by a low directive, linearly polarized source. Elements in the antenna are characterized by tensor impedances. It allows conceiving a circularly polarized antenna supporting a single hybrid leaky-wave mode. Residue analysis is used to demonstrate that a single hybrid leaky-wave mode is sufficient to generate CP. The tool is validated by an antenna design matched over a relative bandwidth of 28.5% with an axial ratio lower than 3 dB.
Fast ions and turbulent particle transport in tokamaks

The presence of highly energetic (fast) ions, be it fusion born alpha particles or ions accelerated by auxiliary heating schemes, can affect the heating and transport of all particles in the plasma through two main mechanisms. First, fast ions may excite fast ion driven instabilities. One such example is the toroidal Alfven eigenmode (TAE) that can be excited by super Alfvenic ions as they slow down due to friction-like collisions with the background plasma and eventually hit the wave-particle resonances. In addition, the fast ions may influence the turbulent transport, driven by temperature and density gradients in the background plasma. The turbulent particle transport, together with particle sources, determines the peaking of the density profile in fusion devices which in turn affect the fusion power generated. In this thesis we employ a simple one-dimensional electrostatic ``bump-on-tail'' model to qualitatively describe fast ion driven TAEs whose signals exhibit frequency sweeping. The frequency sweeping is tied to the formation and evolution of phase space structures known as holes and clumps in the non-thermal fast particle distribution. We have also made realistic simulations of particle transport driven by Ion Temperature Gradient (ITG) modes in JET plasmas heated by neutral beam injection (NBI). We study the turbulent transport using fluid, gyro-fluid and gyrokinetic simulations. Focus is on particle transport and the effect of the NBI generated fast ions on the density peaking and, finally, the influence of the NBI particle source on the density peaking.
Deep Learning applied to Learning Analytics and Educational Data Mining: A Systematic Literature Review

This work presents, to the extent of the authors’ knowledge, the first systematic literature review of the application of Deep Learning to Educational Data Mining and Learning Analytics. Previous literature reviews have documented several works in the areas of Educational Data Mining and Learning Analytics that used classical Artificial Neural Networks techniques. But none of them mentioned the new and much more powerful paradigm in Artificial Neural Networks: Deep Learning. This work surveys this new technique and identifies recent works in Learning Analytics and Educational Data Mining that have applied Deep Learning techniques.
Data‐based optimal Denial‐of‐Service attack scheduling against robust control based on Q‐learning

Attack optimization is an important issue in securing cyber‐physical systems. This paper investigates how an attacker should schedule its denial‐of‐service attacks to degrade the robust performance of a closed‐loop system. The measurements of system states are transmitted to a remote controller over a multichannel network. With limited resources, the attacker only has the capacity to jam sparse channels and to decide which channels should be attacked. Under an L2 framework, a data‐based optimal attack strategy that uses Q‐learning is proposed to maximize the effect on the closed‐loop system. The Q‐learning algorithm can adaptively learn the optimal attack using data sniffed over the wireless network without requiring a priori knowledge of system parameters. Simulation results sustain the performance of the proposed attack scenario.