Miracle-dz/UniFAR-FTUs huggingface dataset:
Latest Advances in Inductive Logic Programming

This book represents a selection of papers presented at the Inductive Logic Programming (ILP) workshop held at Cumberland Lodge, Great Windsor Park. The collection marks two decades since the first ILP workshop in 1991. During this period the area has developed into the main forum for work on logic-based machine learning. The chapters cover a wide variety of topics, ranging from theory and ILP implementations to state-of-the-art applications in real-world domains. The international contributors represent leaders in the field from prestigious institutions in Europe, North America and Asia. Graduate students and researchers in this field will find this book highly useful as it provides an up-to-date insight into the key sub-areas of implementation and theory of ILP. For academics and researchers in the field of artificial intelligence and natural sciences, the book demonstrates how ILP is being used in areas as diverse as the learning of game strategies, robotics, natural language understanding, query search, drug design and protein modelling.Readership: Graduate students and researchers in the field of ILP, and academics and researchers in the fields of artificial intelligence and natural sciences.
On the evaluation of analytic energy derivatives for correlated wave functions

It is shown that to obtain the (2n)th and (2n+1)th energy gradients, it is only necessary to solve equations of the difficulty of the nth order coupled perturbed equations for the orbital and configuration interaction (CI) parameters. For example, to find analytic second and third energy derivatives for CI wave functions, it is only necessary to solve the first order coupled perturbed equations and some related equations, for the effects of orbital rotations. Similar results apply for gradients of energies derived using perturbation theory.
A Risk-Aware Architecture for Autonomous Vehicle Operation Under Uncertainty

A significant barrier to deploying autonomous vehicles (AVs) on a massive scale is safety assurance. Several technical challenges arise due to the uncertain environment in which AVs operate, such as road and weather conditions, errors in perception and sensory data, and model inaccuracy. This paper proposes a system architecture for risk-aware AVs capable of reasoning about uncertainty and deliberately bounding collision risk below a given threshold. The system comprises of three main subsystems. First, a perception subsystem that detects objects within a scene and quantifies the uncertainty arising from different sensing and communication modalities. Second, an intention recognition subsystem that predicts the driving-style and the intention of agent vehicles and pedestrians. Third, a planning subsystem that takes into account the aggregate uncertainty, from perception, intention recognition, and tracking error, and outputs control policies that explicitly bound the probability of collision. We deliberate further on the planner and show, in simulation, that tuning a risk parameter can significantly alter driving behavior. We believe that such a white-box approach is crucial for safe and explainable autonomous driving and the public adoption of AVs.
LUT-based Circuit Approximation with Targeted Error Guarantees

Approximate circuits are widely gaining popularity in various fields where error tolerance is applicable. However, striking the right balance between error tolerance and the output quality is a challenging step in the overall design of approximate systems. We propose a systematic approach utilizing Look-Up Table (LUT)-based netlist transformations to achieve approximation while targeting specific error guarantees. Specifically, we employ a SAT-based property checking technique to accommodate worst-case error constraints acting as error guarantees. The proposed methodology involves the formulation of templates to enable the reusability of the technique for different design choices. The analysis comprises of fitness function evaluation based on layout area or the considered error guarantees. We analyze the impact of different parameters on the quality of the output of the resulting approximation and the time taken to obtain them.
Abstract of "STABILIZATION OF A SINGLE-PHASE NATURAL CIRCULATION LOOP BY PRESSURE DROPS"

Single-phase natural circulation is of interest in various energy systems, including solar heaters, nuclear reactors, geothermal power production, engine and computer cooling. The present paper deals with an experimental study on the influence of pressure drops on the behavior of a single-phase natural circulation loop. At DITEC two rectangular loops have been constructed and operated, differing in the inner diameter of the tubes and other geometric dimensions, but maintaining the same length/diameter ratio. In this paper the experimental results obtained using the larger loop are reported. Several series of tests have been already carried out in this loop, utilizing smooth pipes and varying the input power from 500 to 3400 W. The loop exhibited unstable behavior; hence, to stabilize it, two localized pressure drops (orifices of different diameters) were located in the vertical legs. The experimental data are analyzed and compared with results for smooth pipes, demonstrating the stabilizing effect of the pressure drops on the overall behavior of the loop.
Mirror Full Adder SET Susceptibility on 7nm FinFET Technology

This work investigates the Mirror full adder circuit using a 7nm FinFET technology, considering the SET susceptibility and the robustness of all internal nodes of the circuit. This work aims to identify how this full adder topology behaves in a specific environment, observing the charge collected at each internal node. The devices involved in the processing of the carry-out function shown to be more robust than the Sum circuit and also presented a lower error rate. Furthermore, the input vector 000 proved to be the most critical one, among the input combinations, and the results show a dependence of the type of pulse in the generation of errors in the Mirror full adder.
A Thermal Failure Model for MOSFETs Under Repetitive Electromagnetic Pulses

A thermal failure model for MOSFETs under repetitive electromagnetic pulses is investigated in this paper. The analytic equation to analyze the relationship between the temperature rise and pulse parameters is given by a theoretical derivation. The electro-thermal process of a 180 nm MOSFET is simulated as an example. It shows that the model agrees well with the technology computer aided design (TCAD) results. Some discussions on the influence of the dissipation performance and on the electro-thermal coupled effect are given. Both the theoretical model and the TCAD results indicate that most of the failures occur in 1 or 2 cycles. Further increase in the pulse number does not change the failure probability. Influence of heat dissipation performance of the substrate is discussed. This work is useful for further failure analysis, and is also helpful in protection design for the MOSFET device and circuit under HPMs and other repetitive electromagnetic pulses.
Optimization of key parameters towards high performance perovskite solar cells

Here, we report important findings regarding underestimated parameters for the synthesis and fabrication of high-performance perovskite solar cells. These parameters include the effect of Fluorine-doped Tin Oxide (FTO) etching, FTO cleaning, the number of compact TiO2 (c-TiO2) layer, the number of mesoporous TiO2 (m-TiO2) layers and the aging time before Ag deposition. Our results demonstrated that etching of FTO substrate with Zn/HCl is an essential step and has a major effect on the solar cell's open circuit voltage (Voc), fill factor (FF) and power conversion efficiency (PCE). Furthermore, we demonstrate new and improved protocols for the complete cleaning of FTO substrates. Despite the use of sonication and plasma etching in previous cleaning techniques, SEM images clearly show black clouds in the samples, which may be due to residual Zn particles in the FTO grooves. Thus, a soft toothbrush was used with detergent before sonication to detach the remaining Zn particles. In addition, the optimum number of spin coated layers of compact and mesoporous TiO2 precursors was investigated. We found that one mesoporous and two compact TiO2 layers were required to obtain a homogenous pinhole-free compact layer. Consequently, we demonstrate that using these optimized device fabrication procedures, a high efficiency of 17.96% for 6 mol% Co3+-doped TiO2 solar cells can be obtained in comparison to 16.98% for pristine TiO2-based cells. Such cells are particularly important for wearable applications that require a small area and a high energy.
Network analysis of electrical activity in brain motor cortex during motor execution and motor imagery of elderly

We performed connectivity analysis of the brain motor-related electrical activity corresponding to the motor execution and motor imagery in the group of elderly subjects aged 55-76. The goal of the current study is to infer the the differences and similarities of brain motor cortex functional connectivity during executive and mental motor tasks. Particularly, we observed significant event-related desynchronization (ERD) in β-band (15-30 Hz) over the motor cortex during both motor execution and motor imagery tasks. We applied linear correlation analysis to explore functional connectivity and found that motor execution and motor imagery share the similar connectivity pattern in the β-band at the beginning of the motor-task. We conclude, that motor preparation preceding motor execution reflects the mechanisms of the motor imagery process.
Self-Tuning Vector Controlled PMSM Drive Using Particle Swarm Optimization

This article presents the vector controlled drive of permanent magnet synchronous motor. The concept of vector control is used for decoupled control of flux and torque producing component of current. The PI controller's parameter of the drive system had been tuned with particle swarm optimization technique. The tuning of controller's parameter with particle swarm optimization had been greatly improved the performance of drive system for variable speed drive. The presented work and performance of the drive developed in MATLAB/SIMULINK environment with change in speed and load. It has been observed that from FFT analysis in stator current, develop output voltage and torque ripple in electromagnetic torque are greatly improved, Hence the proposed technique is acclaimed for best performances.
Divide-by-2 Injection-Locked Frequency Divider Using 3-path Transformer-Coupled Resonator

This article proposes a wide-band divide-by-2 injection-locked frequency divider (ILFD) in the 0.18 µm CMOS process. The ILFD uses transformer-coupled resonator and the secondary inductor can be turned on/off by a MOSFET switch. The free-running ILFD oscillation frequency is tuneable via switch gate bias, and the resonator looks like a resistively tuned resonator. The secondary is made of a three-path inductor and the primary is made of a symmetric three-turn single-path inductor. The single-band-like locking range is from 4.9 GHz to 12.71 GHz at the power consumption of 8.72 mW and the locking range is from 6.1 GHz to 12.1 GHz at the power consumption of 2.6 mW. The circuit also shows two non-overlapped locking ranges, which is attributed to the transformer-based 4th order resonator. Overlapped locking range technique is used to design wide-band transformer-based ILFD.
Optimal Placement and Sizing of PV as DG for Losses Minimization Using PSO Algorithm: a Case in Purworejo Area

The Total distribution system losses of PT PLN ULP Purworejo reach 10.467%as technical and non-technical losses which were very high compare to the maximum limit (5%). In this research, Photovoltaics (PV) Distributed Generation (DG) was installed as an active power injection to reduce the system’s losses. This research determined the hosting capacity of PV including the location and capacity of the PV. The problem was modeled into an optimization problem and was simulated using Particle Swarm Optimization (PSO) algorithm in MATLAB environment. The simulation was considered the voltage, power factor (pf), and branch current as constraints. The simulation was conducted on the existing system of Feeder 7 of PT PLN ULP Purworejo distribution system. Backward Forward Sweep (BFS) method was used for load flow analysis. As the load flow analysis result, the technical losses on a feeder 7 reach 2.676% of feeder 7 supply. The simulation results showed that by installing optimal distributed PV, the technical losses in feeder 7 were reduced to 1.351%. The overall losses of PT PLN ULP Purworejo become 10.045%.
Traffic Flow Forecasting with Spatial-Temporal Graph Convolutional Networks in Edge-Computing Systems

Traffic forecasting is one of important functions in Intelligent transportation systems (ITSs) and is of great significance to user experience and urban traffic control. Edge computing, has been recognized as a promising technique for real-time and accurate traffic flow forecasting. In this paper, we propose Spatial-Temporal Graph Convolutional Networks in an edge-computing system (STGCN-EC) for traffic flow forecasting. Firstly, we model the road network as a graph and partition it into multiple subgraph according to the spatial correlation of the area so that traffic flow forecasting can be individually performed by each edge node. Secondly, by taking the geographic information and temporal similarity of the traffic flow into account, we propose a spatial-temporal Graph convolutional network to efficiently capture the spatial-temporal features for traffic flow prediction in each subgraph. In addition, we adopt transfer learning to share models among different edge nodes to further improve training efficiency. Simulation results on real- world dataset demonstrate that the proposed approach is able to improve prediction accuracy and training efficiency in an edge-computing system.
Tests Estimate And Analysis on Losses of VSC Sub-modules

This VSC-HVDC (Voltage Sourced Converter) has been widely applied all over the world owing to its unique advantages. Converter valve is the key equipment of VSCs, its losses are significant to the economically operation of VSC stations. Analysis and accurate measurement of the losses can make great contribution to efficiency optimization, devices selection and thermal design, etc. The paper investigated the losses measurement technology of power electronics equipment and surveyed the academic research at home and abroad of the double pulse method, electric method and thermal method for losses measurement. It came up with three test schemes for measuring losses of VSC sub-modules respectively, the schemes include measuring method, test circuit design, equipment selection, software platform and its control method, test procedure, uncertainty and loss estimation method. In the physical test platform, tests based on three schemes are carried out, the uncertainty of tests results is analyzed. This paper provides reference for measuring losses of VSC sub-modules in practical projects.
Collaborative Online Caching with Freshness in the Internet of Things

In this paper, a Collaborative Online Caching Algorithm with Freshness is proposed to solve the data caching problem in the Internet of Things (IoT) among multiple small base stations (SBSs). In this algorithm, the ability of SBSs to cooperate with each other is related to three factors: the number of coordinated connections each SBS establishes with other SBSs, the distance between SBSs, and the number of served users in the coverage area of each SBS. On the basis of cooperative caching strategy, this algorithm considers online settings and introduces the term of "freshness" in the IoT to restrict the real-time degree of files cached in SBS, and on the premise that the freshness of files meets the user’s expectation, an optimization model is constructed to minimize the total cost paid by SBSs. We express the problem as an Integer Linear Program and prove its NP-completeness by mapping method of set covering problem, so that we can obtain the best scheme for SBSs to cache files. In addition, due to the limited cache capacity, we have improved the Least Recently Used Replacement Policy (LRU) to update cached files by combining freshness and the frequency each file has been requested. Finally, we calculate the time complexity of the algorithm. The simulation results manifest that, compared with the caching strategy without considering freshness, the algorithm in this paper greatly improves user satisfaction with a little increase in total costs.
TrackNetV2: Efficient Shuttlecock Tracking Network

TrackNet, a deep learning network, was proposed to track high-speed and tiny objects such as tennis balls and shuttlecocks from videos. To conquer low image quality issues such as blur, afterimage, and short-term occlusion, some number of consecutive images are input together to detect an flying object. In this work, TrackNetV2 is proposed to improve the performance of TrackNet from various aspects, especially processing speed, prediction accuracy, and GPU memory usage. First of all, the processing speed is improved from 2.6 FPS to 31.8 FPS. The performance boost is achieved by reducing the input image size and re-engineering the network from a Multiple-In Single-Out (MISO) design to a Multiple-In Multiple-Out (MIMO) design. Then, to improve the prediction accuracy, a comprehensive dataset from diverse badminton match videos is collected and labeled for training and testing. The dataset consists of 55563 frames from 18 badminton match videos. In addition, the network mechanisms are composed of not only VGG16 and upsampling layers but also U-net. Last, to reduce GPU memory usage, the data structure of the heatmap layer is remodeled from a pixel-wise one-hot encoding 3D array to a real-valued 2D array. To reflect the change of the heatmap representation, the loss function is redesigned from a RMSE-based function to a weighted cross-entropy based function. An overall validation shows that the accuracy, precision and recall of TrackNetV2 respectively reach 96.3%, 97.0% and 98.7% in the training phase and 85.2%, 97.2% and 85.4% in a test on a brand new match. The processing speed of the 3-in and 3-out version TrackNetV2 can reach 31.84 FPS. The dataset and source code of this work are available at https://nol.cs. nctu.edu. tw:234/open-source/TrackNetv2/.
A New Bounding Box based Pseudo Annotation Generation Method for Semantic Segmentation

This paper proposes a fusion-based method to generate pseudo-annotations from bounding boxes for semantic segmentation. The idea is to first generate diverse foreground masks by multiple bounding box segmentation methods, and then combine these masks to generate pseudo-annotations. Existing methods generate foreground masks from bounding boxes by classical segmentation methods driving by low-level features and own local information, which is hard to generate accurate and diverse results for the fusion. Different from the traditional methods, multiple class-agnostic models are modeled to learn the objectiveness cues by using existing labeled pixel-level annotations and then to fuse. Firstly, the classical Fully Convolutional Network (FCN) that densely predicts the pixels’ labels is used. Then, two new sparse prediction based class-agnostic models are proposed, which simplify the segmentation task as sparsely predicting the boundary points through predicting the distance from the bounding box border to the object boundary in Cartesian Coordinate System and the Polar Coordinate System, respectively. Finally, a voting-based strategy is proposed to combine these segmentation results to form better pseudo-annotations. We conduct experiments on PASCAL VOC 2012 dataset. The mIoU of the proposed method is 68.7%, which outperforms the state-of-the-art method by 1.9%.
NIR Palm Vein Pattern Recognition

The 850nm near-infrared (NIR) palm vein pattern imageries are considered and have been successfully recognized utilizing image processing in machine learning. The proposed method has well combined the ROI extraction, feature extraction to stipulate the desired response. The palm veins imageries are enhanced and denoised using a median filter. The Otsu Binarization is applied and finally, the 850nm NIR palm vein pattern collective image-enabled dataset has been successfully recognized. Thus, paper exhibits a novel near-infrared palm vein pattern recognition for personal authorization. The very famous CASIA database is used to introduce to recognize the palm vein pattern with a local binary pattern-based method. Various palm vein features extraction and correction are done to establish the quantification between the test data and the trained data. Hence, amalgamating the power of image processing in machine learning and Python programming, the proposed novel palm vein pattern for personal recognition method is designed which provides a user-friendly environment.
A Low-Power Injection-Locked Frequency Tripler in 90 nm CMOS Technology

This letter designs a low power CMOS LC-tank injection locked frequency tripler (ILFT) fabricated in 90 nm CMOS process. The differential-input and differential-output ILFT circuit is composed of a first-harmonic injection-locked oscillator (ILO) and a pMOSFET mixer-type frequency tripler to supply an injection current to the ILO. At the supply voltage of 0.4 V, the free-running oscillation frequency of the ILO is from 12.39 GHz to 12.86 GHz. The dc power consumption is 0.312 mW. At the incident power of 0 dBm, the input locking range is from the incident frequency 3.5 GHz to 5.2 GHz to provide an output signal source from the frequency 10.5 GHz to 15.6 GHz.
Autoclave Molding Artificial Intelligence (AI) Method and Apparatus System of Composite Materials for Aerospace Applications

In this research, smart meters and monitoring systems are set up for the aerospace industry hot-pressing furnace, and artificial intelligence (AI) and mechanical learning methods are used to find the process optimization steps of the autoclave. After the introduction of AI methods and smart devices, it can shorten the selection time for personnel seeking to optimize the process parameters of the autoclave, raise warnings for abnormal equipment, predict the maintenance of the hot pressing furnace equipment in advance, and reduce maintenance costs and equipment maintenance time.
Energy-Efficient Start-up Dickson Charge Pump for Batteryless Biomedical Implant Devices

This paper presents a power management concept for solar energy harvesting power management using an on-chip switched-capacitor (SC) DC-DC converter for biomedical implantable applications. This design eliminates potential reversion losses caused by the switching scheme. It also mitigates the bottom plate loss by employing the charge recycling technique. Moreover, instead of using a single step clock pulse, the two-step adiabatic charge sharing clock helps reduce the energy drawn from the PV cell by 65%. Furthermore, with the help of clock disabler scheme, the power dissipation has been further reduced by disabling the entire start-up charge pump once the desired reference output voltage was reached. However, due to additional circuitry for the clock disabler, there is a tradeoff between power efficiency and power dissipation. The proposed system was implemented and fabricated in a standard 0.18-µm TSMC RF CMOS technology. The proposed converter has achieved a maximum efficiency of 73%.