Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimization and Research of Suspicious Object Detection Algorithm in X-ray Image

At present, X-ray imaging technology is widely used in the fields of security inspection and medical treatment, and plays a vital role in the safety of people’s lives and property. This paper mainly does in-depth research on the detection of suspicious objects in the field of security inspection by X-ray, which has made a great contribution to improving the efficiency and accuracy of security inspection. In recent years, the convolutional neural network has made great breakthroughs in the field of computer vision, making it possible to detect objects in real-time X-ray images. However, due to the overlap and occlusion of objects in X-ray images compared with natural images, the convolutional neural network does not achieve good training results. In this paper, we propose an object detection algorithm based on the SSD model, using the improved HarDNet network as the backbone network, and introducing a multi-scale feature fusion mechanism and an attention mechanism to improve the detection accuracy. The multi-scale feature fusion module combines the feature maps of different levels in the backbone network, and fuses the low-level texture information and high-level semantic information to obtain more effective feature maps. In addition, the attention module constructs two sub-modules: the spatial attention module and the channel attention module, respectively summarize the attention information of the space and the channel, and synthesize the information to obtain more comprehensive and reliable attention information. We evaluate our method extensively on HiXray and SIXray and the results demonstrate that it outperforms SOTA detection methods.
An Improved RBPF-SLAM Algorithm for Indoor Robot

Aiming at the problems of inaccurate proposal distribution and particle degradation of traditional RPF-SLAM algorithm in robot indoor mapping, an improved RPF-SLAM algorithm was proposed. Firstly, the motion model and observation model were combined as the mixed suggestion distribution. Annealing parameters were used to optimize the mixed suggestion distribution and adjust the proportion of the two in the suggestion distribution to make the mixed suggestion distribution more accurate. Secondly, a Markov chain Monte Carlo (MCMC) resampling method is used to generate reasonable samples from the target distribution by constructing Markov chains, so that particles can move to different positions in each iteration to avoid particle degradation. Experimental results show that compared with the traditional RBPF-SLAM algorithm, the improved algorithm has lower root mean square error and calculation time, higher accuracy and better effect of map construction.
An Adaptive Control Scheme for Distributed Generators in Systems connected via Underground Cables

Stringent constraints on space and reliability in urban active distribution networks demand the need for routing power cables underground (UG). Such lines are predominantly capacitive in nature unlike the overhead cables (which are inductive). This work focuses on the use of an adaptive droop coefficient based control scheme with the L, C a nd R C droops applied to inverter based distributed generators (DGs) which are interconnected through UG cables. The focus is on improved proportional power sharing among the sources with different types of loads such as balanced, unbalanced, non-linear and constant power loads. The strategies have been validated using simulations in MATLAB/Simulink.
RBFNNBA Control of Electromechanical Servo System Based on the LuGre Model

To reduce the adverse effects of friction nonlinearity existing in electromechanical servo systems, an adaptive control method is proposed in this paper. First, the electromechanical servo system model is presented, followed by the introduction of the concept of virtual control quantity using the backstepping control method. The system's control law is designed by recursively selecting the Lyapunov function, and it is shown that system position tracking error is convergent. Because control law has unknown parameters, it is difficult to obtain accurate values for these parameters in practical engineering. Therefore, the universal approximation ability of a radial-basis-function (RBF) neural network is employed to approach the unknown parameters online. The system's adaptive law is designed, an RBF neural network backstepping adaptive controller based on state feedback is constructed, and the controller's stability is analyzed. Finally, a comparison is made between the simulation results of the proportional-integral-derivative (PID) controller, the RBF neural network (RBFNN) controller, and the RBF neural network backstepping adaptive (RBFNNBA) controller. Results show that performance of all three types of controller is gradually improved, which verifies the feasibility of the proposed control strategy.
RT-SLAM:Real-Time Visual Dynamic Object Tracking SLAM

Simultaneous Localization and Mapping(SLAM) and dynamic object tracking are mutually beneficial. Local localization for the environment and object tracking are fundamentally the same problem, and solving them simultaneously can reduce the ambiguity of scenes and improve localization in the highly dynamic scenarios. This paper presents RT SLAM, a real-time robust visual dynamic object tracking SLAM system that uses semantic information to achieve accurate motion estimation and tracking of dynamic rigid objects without prior knowledge of the shape or geometric model of the object. We use optical flow networks and adaptive-coupled solution to increase the operation speed and enhance the system performance in real-time while maintaining system accuracy. Finally, we demonstrate the performance of the proposed system on Virtual KITTI datasets and show the accuracy and FPS of RT-SLAM.
Anti-Money Laundering Risk Identification of Financial Institutions based on Aspect-Level Graph Neural Networks

The contemporary financial industry is a highly information-based industry. The digital system can establish a complete information system around various attributes and behaviors of bank accounts. In the core business system, most of this information is constantly changing and recorded in real time. Therefore, we can achieve the goal of monitoring the money laundering risk of the account by analyzing the relevant element data and specific characteristics of the account. The risk assessment and customer classification indicator system for accounts is composed of four basic elements: customer characteristics, location, business development and industry conditions. Account money laundering risk indicators are composed of various basic elements and their risk sub-items. We propose an aspect-based (aspect-level) graph convolutional neural network, starting from different perspectives, to quantify the risk of money laundering in financial institutions.
Voltage and Frequency Control of a PV-Battery-Diesel Generator based Standalone Hybrid System

An effective controller is proposed for the “Photovoltaic(PV) – Battery - Diesel Generator(DG)” based hybrid standalone system in this article. A bidirectional DC to DC device which can allow power in both directions is integrated among the DC-link and battery bank to maintain contact voltage by maintaining power balance in the system. Further, a bidirectional inverter is also considered between point of common coupling (PCC) and DC-link for providing supply to AC loads as well as charging the battery from diesel generator based on requirement. However, the phenomenon of partial shading condition (PSC) commonly occurring on PV modules and also generated power from PV always fluctuates according to changes in weather. To achieve a superior performance of the system, a Takagi-Sugeno Fuzzy (TSF) based controllers are implemented in proposed control schemes. The novel control schemes are implemented on a bidirectional DC to DC converter as well as three phase bidirectional inverter. However, the proper deloading process of the PV system should be incorporated into the controller of the DC to DC converter to achieve power balance during light load conditions. Balanced quality voltages (3-phase) at PCC are achieved by inverter control which forces to maintain balanced currents generated by DG during the operation of unbalanced loads. Maintaining balanced DG currents can help to reduce the oscillations on the torque of the shaft which increases the fatigue life. Further the reactive power demanded at PCC is compensated by the inverter control, hence DG need not to provide it which results can reduce the consumption of diesel. Various case studies are examined by using MATLAB to present results and Real Time Digital Simulator (RTDS) based results are also provided in this paper.
Non-cooperative Game Theory based Electric Vehicle Charge Scheduling

The desire to join green transportation to gain emission-free alternatives has never been more pronounced. Electric vehicle (EV) adoption is completely dependent on available charging infrastructure, its management and control. EV charge scheduling is one of the management technique. This technique addresses several issues like load curve flattening as well as reducing EV operational costs. This paper presents a novel game-theory based algorithm in order to proficiently solve the problem of EV charging schedules. Cost optimization along with customer satisfaction is achieved by simulating a practical scenario considering load curve data of a distribution point in Pune and the results are discussed.
Domain decomposition FDTD algorithm for the analysis of a new type of E-plane sectorial horn with aperture field distribution optimization

In this paper, a universal and efficient approach of domain decomposition finite-difference time-domain FDTD (DD-FDTD) is presented for the analysis of a new type of horn antenna-E-plane sectorial horn with field amplitude taper and phase correction in the aperture. The power fed into the horn is redistributed to achieve the optimal field amplitude distribution in the aperture, and meanwhile the field phase is corrected by metal lens. Compared with conventional E-plane sectorial horns, the new horn antenna takes the advantages of low sidelobe level, short physical length and wide flare angle etc. Moreover, the most important property of this horn is the weak coupling with each other when it is used as the element of a phased array antenna. The field analysis of such a horn antenna is an extremely complicated three-dimensional EM boundary value problem. The domain decomposition FDTD method is presented in this paper to break through the drawback. The whole horn is decomposed into several subdomains and the meshes are created in local coordinates. In the iteration procedure of FDTD, the data are exchanged between adjacent subdomains with overlapped meshes. The aperture field distribution, voltage standing-wave ratio and pattern calculated by the DD-FDTD method are in good agreement with the experimental data.
Categorical Databases for Mathematical Formalization of AC Optimal Power Flow

It has been decades since category theory was applied to databases. In spite of their mathematical elegance, categorical models have traditionally had difficulty representing factual data, such as integers or strings. This paper proposes a categorical dataset for power system computational models, which is used for AC Optimal Power Flows (ACOPF). In addition, categorical databases incorporate factual data using multi-sorted algebraic theories (also known as Lawvere theories) based on the set-valued functor model. In the advanced metering infrastructure of power systems, this approach is capable of handling missing information efficiently. This methodology enables constraints and queries to employ operations on data, such as multiplicative and comparative processes, thereby facilitating the integration between conventional databases and programming languages like Julia and Python’s Pypower. The demonstration illustrates how all elements of the model, including schemas, instances, and functors, can modify the schema in ACOPF instances.
Improved Enhanced-Boost Quasi-Z-Source Inverter

This paper presents a new impedance network inverter topology called an improved enhanced-boost quasi-Z-source inverter (improved-EBqZSI). The proposed improved-EBqZSI topology employs five capacitors, seven diodes, and five inductors in the impedance network to increase the boost factor. It provides continuous input current, lower voltage stress on capacitors and diodes, and lower current stress on inductors. Additionally, the improved-EBqZSI has higher gain at a lower duty ratio; it can operate at a higher modulation index, thus reducing stress across the inverter switches. The paper presents the operation of the inverter in shoot-through (ST), and non-shoot-through (NST) states, mathematical analysis, computer simulation, and experiment results. A laboratory prototype has been developed to validate the simulation results for the high gain capability.
Electrolytic Capacitor-less Minimal Power Processing Single-phase Active LED Driver with Constant output Current

In this paper, the concept of an LED driver based on minimal power processing is presented. By regulating the power flow using semiconductor switches, the required minimal power processing can be achieved, which in turn reduces storage capacitance and extends the lifespan of LED driver. In contrast to a two-stage LED driver, the DC-DC converter handles a part of the total power in the minimal power processing design. This will increase the overall performance of the system. In addition, the double line frequency power that is inevitable for any AC-DC conversion system is reduced. The DC output power can be match with the AC input ripple power using the correct switching timings of semiconductor switches. Thus, long life film capacitors can be used in place of electrolytic capacitors. To get the required minimal power processing, power factor correction, and constant output current increases the design and control complexity, and a number of components. A 38W simulation model is implemented using PSIM simulation. The results validate the performance of the system in terms of power factor, constant output current, and peak efficiency.
Bandwidth Based Approach for Mitigation of Delay-Cyberattacks of Load Frequency Control Via LADRC

Modern power systems are prone to cyberattacks due to sophisticated computer networks and open communication channels. Delay cyberattack is one of a few dangerous cyberattacks that can happen in power systems. It can quickly destabilize the whole power system's operation. Due to this, the mitigation of delay-based cyberattacks is an important topic. This paper proposes a heuristic linear active disturbance controller (LADRC) tuning approach for mitigation of time delay cyberattack. In this paper, first, the “bandwidth parameterization” method of tuning is explained using the controller bandwidth and observer bandwidth. Afterwards, a new linear active disturbance controller (LADRC) tuning approach is presented. Finally, proposed method of LADRC controller tuning is validated on the a single area load frequency control problem under a delay-based cyberattack. Simulations in MATLAB/Simulink are used to validate the proposed LADRC controller.
Improved QoS in Internet of Things (IoTs) through Short Messages Encryption Scheme for Wireless Sensor Communication

Global interest in Internet of Things (IoTs) can be accorded to the provisioning of Wireless Sensors Communication (WSC). Sensor data cannot be standardized due to diverse nature of measurements. However, the sensor's data needs to be converted into standard size for compatibility with encryption schemes. Therefore, IoTs face various processing and communication overhead issues for acceptable Quality of Service (QoS) in WSC. We proposed a communication model of the Short Messages Encryption Scheme (SMES) Model with the least additional padding in plain text. We performed a comparative analysis of various existing encryption modes and highlighted the limitations concerning compatibility with SMES. Our communication model utilizes Time of Day (TOD) based loose synchronization intervals in combination with Cipher Feed-Back (CFB) mode of AES encryption. Our evaluation of the proposed scheme shows least processing requirements and overheads in comparison with various other encryption modes. We concluded this paper by deliberating possible future research directions in SMES for IoTs.
Study of Isolated Single-stage Resonant AC-DC Converter

This paper presents study of Isolated single-stage resonant AC-DC converter. This converter combines the operation of the boost power factor correction (PFC) and the LLC resonant dc-dc converter in a single stage. In compare to two-stage topology this converter the PFC operation and generation of the high frequency pulsating voltage for LLC tank is done with a single H-bridge arrangement. This converter provides an isolated DC output voltage from AC mains with a high power factor through direct power conversion without using the full-wave diode bridge rectifier. Thus the conduction losses are reduced. The switching losses are also reduced by the zero-voltage switching (ZVS) and zero current switching (ZCS) operation of the power switches through the LLC resonance. The DC-link capacitor is not connected directly to the DC-DC stage so capacitance can reduce largely as it do not required to hold the dc voltage with less ripple. Thus we can use a small film capacitor instead of a large Electrolytic capacitor. This converter works in CCM mode with the interleaved operation so the current stress and input current THD are reduced. It has better Electromagnetic interference performance thus the input EMI filler can be eliminated.
Gait Classification of Parkinson’s Disease with Supervised Machine Learning Approach

Gait analysis is essential for diagnosis, assessment, monitoring purpose, and prediction of gait disorder. However, the objective analysis method is less feasible in hospital environments for treatment purposes due to limited coverage of sources. Thus, this study aims to develop a classification algorithm that can effectively classify subjects with relatively simplified temporal spatial gait data. This study employed several datasets acquired from PhysioNet containing subjects’ gait data of three classes. The training dataset contains a total of 48,318 instances of three target classes (young healthy adults, old healthy adults, and Parkinson’s disease patients). Two classification algorithms were developed: Support Vector Machine (SVM) classification algorithm and Artificial Neural Network (ANN). Preprocessing was performed to the original dataset which includes data cleaning, data normalization and new features generation. Next, fine-tuning on the manipulating hyperparameters was performed, and 10-fold cross validation was applied. The optimum configuration of SVM model can generate an accuracy of 93.01% and F1 score of 0.92 with 43 minutes of computational time. On the contrary, the optimum configuration ANN classifier generates an accuracy of 90.56% and F1 score of 0.89 with 112 minutes computational time. Conclusion: In conclusion, comparing both of the proposed classification algorithms, the SVM classifier is more effectively than ANN classifier as overall for the gait dataset used in this study. In addition, after compared with other state-of-the-arts of gait classification algorithms, our proposed classification algorithm produced comparable results with other state-of-arts using a smaller dataset with fewer training features. Clinical Relevance– This establishes the potential of apply machine learning algorithm on basic gait data obtained from the objective gait analysis method in classification of healthy adults, older adults, and Parkinson’s patient.
The effects on oceanic planetary waves of coupling with an atmospheric energy balance model

This paper shows the existence of a growing planetary-wave-like ocean mode, with a decadal period and growth rate, which appears when a stratified, diffusive ocean is coupled to a simple atmosphere via an energy balance model (EBM). Such modes are not found when simpler surface ocean conditions are applied. The mode is low order in the vertical and, because of its slow growth, is likely to be observed in Earth System Models using an EBM in place of a fuller set of atmospheric dynamics. There is no apparent physical energy source for such a mode, and therefore it should not be expected to arise in such a model. The mode is analysed through a hierarchy of simple models, which differ only through their surface boundary condition.
Seasonal Forecasting in the Pacific Using the Coupled Model POAMA-2

AbstractThe development of a dynamical model seasonal prediction service for island nations in the tropical South Pacific is described. The forecast model is the Australian Bureau of Meteorology's Predictive Ocean–Atmosphere Model for Australia (POAMA), a dynamical seasonal forecast system. Using a hindcast set for the period 1982–2006, POAMA is shown to provide skillful forecasts of El Nino and La Nina many months in advance and, because the model faithfully simulates the spatial and temporal variability of rainfall associated with displacements of the southern Pacific convergence zone (SPCZ) and ITCZ during La Nina and El Nino, it also provides good predictions of rainfall throughout the tropical Pacific region. The availability of seasonal forecasts from POAMA should be beneficial to Pacific island countries for the production of regional climate outlooks across the region.
A coded aperture for high-resolution nuclear medicine planar imaging with a conventional Anger camera: experimental results

Coded apertures have been investigated in the past for their promise of an improved signal-to-noise ratio (SNR) over pinhole and collimator systems. We describe a coded aperture camera designed for high-resolution (1.66-mm) imaging of low energy (140 keV) gamma-ray emitters with a conventional Anger camera. The aperture pattern was chosen to maximize the SNR and to allow a simple implementation of a new near-field artifact compensation technique based on the use of an antisymmetric array. Experimental results show that coded aperture imaging can produce good quality high-resolution planar images with high SNR while keeping exposure times and injected doses at reasonable levels.
Statistical linear approximation for environment compensation

The statistical linear approximation (SLA) method is proposed as a novel way to approximate a nonlinear function by a linearized model. In the proposed method, an optimization criterion for approximation is defined in terms of statistical expectation. The SLA is applied to environment compensation where the speech contamination rule appears as a highly nonlinear function of the relevant variables.
A method for predicting longitudinal stability derivatives of rigid and elastic airplanes

Using the concept of aerodynamic and structural influence coefficients, a method for estimating longitudinal stability derivatives for rigid and elastic airplanes is presented. The airplane is divided into a large number of surface panels. From elementary solutions to the classical small perturbation, steady flow equation a matrix of aerodynamic influence coefficients is generated which is used to compute pressure distribution, total forces and moments in subsonic and supersonic flow. The elastic properties of the airplane are represented by a flexibility influence coefficient matrix. Matrix algebra is used to obtain explicit expressions for longitudinal a-, u-, and q-stability derivatives. To validate this method, comparisons are shown with tunnel data for a- and w-derivatives obtained with a rigid model of the Boeing 707 and with rigid and elastic models of a variable sweep SST configuration. The agreement between theory and tunnel data is demonstrated to be quite good.