Miracle-dz/UniFAR-FTUs huggingface dataset:
Detection of Lung Nodules from Temporal Subtraction Image Using Deep Learning

In recent years, the number of death due to lung cancer is increasing year by year worldwide. Early detection and early treatment of lung cancer are important. Especially, early detection of the abnormalities on thoracic MDCT images detection of small nodules is required in visual screening. Although a CT apparatus is used for the examination, the burden on the image interpretation doctor is large due to the high performance of the CT, so the diagnostic accuracy may be reduced. In this paper, we propose an image analysis method to detect abnormal shadows from chest CT images automatically. The initial lesion candidate areas are extracted by using temporal subtraction technique that emphasizes temporal change by subtracting from a current image to previous one which is obtained same subject. The image of the area is given as input and classification is performed by CNN (Convolutional Neural Network). In the discrimination experiment based on our proposed method, 90.26 [%] of true positive rates and 13.58 [%] of false positive rates are obtained from the 49 clinical cases.
Non-Deterministic Behavior Analysis for Embedded Software Based on Probabilistic Model Checking

The real-time interaction between embedded software and its external environment is conducted through the interrupt mechanism. Since the interrupt request is random and responds according to priority, the execution of embedded software is non-sequential, which leads to the non-deterministic software behaviors. If these non-deterministic behaviors can be quantitatively pre-analyzed during the software design phase, the reliability of embedded software can be improved effectively. In this paper, we first provide an embedded software behavior model based on extended deterministic and stochastic Petri nets (EDSPN). Through EDSPN, the interrupt behavior of embedded software can be effectively modeled. Then we put forward a probabilistic model checking method of Continuous Stochastic Logic (CSL) for EDSPN to analyze embedded software behavior. For alleviating the state explosion problem, the above method uses the bounded model checking (BMC) technique. We present the model checking methods and the probability metric calculation methods for CSL operators under bounded semantics. Finally, by analyzing the EDSPN model of embedded software with multiple interrupts, we compare the analytical capabilities of BMC method and non-BMC method. The experiment shows that when the state space of EDSPN is large and is hard to calculate, the bounded checking algorithm can be used to approximate the software behavior. The conclusions obtained are helpful to understand the properties to be verified.
Zero Operating Cost Renewables and Shadow Bidding in Wholesale Electricity Markets

Electricity market mechanisms are expected to deliver efficient outcomes for consumers. This paper examines the ways in which new entrants into energy-only wholesale markets, such as wind and photovoltaic power plants, may be incentivised to engage in false bidding activities. The presence of these generators with near-zero short-run marginal operating costs is seen to highlight and exacerbate existing design flaws in the incentive structures of reverse auction energy dispatch mechanisms. Specifically, a scenario of false ‘shadow’ bidding is explored for generators with near-zero short-run marginal operating costs. This bidding strategy appears to run counter to the core expectations of energy-only wholesale markets in terms of efficient energy pricing.
A Tri-level Defense Model Against Load Redistribution Attacks

The modern power system evolves into a complex cyber-physical system with advanced sensors and communication network. However, power systems are also increasingly vulnerable to cyber attacks. It is of great practical significance to model cyber attacks and propose effective defense measures. This paper proposes a tri-level robust defense model to defend against the load redistribution attack for power systems. The proposed model considers the risk of both the load shedding of load buses and the overloading of transmission lines caused by the load redistribution attack. In the proposed tri-level model, the upper level optimizes the defense strategy with the least cost; the middle level determines the most serious load redistribution attack scheme based on the defense strategy determined by the upper level; the system loss is minimized by optimal operation in the lower level. Then the proposed tri-level model is solved by Genetic algorithm coordinated with linear programming. Finally, case studies based on the modified IEEE 14-bus test system demonstrate the effectiveness of the proposed model.
Robust Event Detection using Synchrophasor Assisted Forecasting-Aided State Estimation

Identification of various power system events such as fault and power swing is important for its reliable and secure operation. Multiple sequential events may make the event identification algorithm complex. By taking advantage of The high reporting rate of Phasor Measurement Units (PMUs), this paper proposes a method to identify the power system events in the network. Synchronized voltage and current phasor measurements are utilized along with the forecasting-aided state estimator to extract the features of the event. The proposed method can detect an invalid PMU data and can correct it to avoid wrong identification of an event. The method is capable of detecting events like fault and power swing. The proposed event detection algorithm is validated on WSCC 9-bus system using the Real Time Digital Simulator (RTDS).
Efficient Graph Learning From Noisy and Incomplete Data

We consider the problem of learning a graph from a given set of smooth graph signals. Our graph learning approach is formulated as a constrained quadratic program in the edge weights. We provide an implicit characterization of the optimal solution and propose a tailored ADMM algorithm to solve this problem efficiently. Several nearest neighbor and smoothness based graph learning methods are shown to be special cases of our approach. Specifically, our algorithm yields an efficient but extremely accurate approximation to $b$-matched graphs. We then propose a generalization of our scheme that can deal with noisy and incomplete data via joint graph learning and signal inpainting. We compare the performance of our approach with state-of-the art methods on synthetic data and on real-world data from the Austrian National Council.
Human Activity Recognition Using Multichannel Convolutional Neural Network

Human Activity Recognition (HAR) simply refers to the capacity of a machine to perceive human actions. HAR is a prominent application of advanced Machine Learning and Artificial Intelligence techniques that utilize computer vision to understand the semantic meanings of heterogeneous human actions. This paper describes a supervised learning method that can distinguish human actions based on data collected from practical human movements. The primary challenge while working with HAR is to overcome the difficulties that come with the cyclostationary nature of the activity signals. This study proposes a HAR classification model based on a two-channel Convolutional Neural Network (CNN) that makes use of the frequency and power features of the collected human action signals. The model was tested on the UCI HAR dataset, which resulted in a 95.25% classification accuracy. This approach will help others to conduct further researches on the recognition of human activities based on their biomedical signals.
Anti-Disturbance Output Feedback Tracking Control for Switched Stochastic Systems With Multiple Disturbances via Mode-Dependent Average Time Method

The anti-disturbance output feedback tracking control problem is addressed for switched stochastic systems with multiple disturbances in this article. To deal with unknown disturbances and unmeasurable states, composite disturbance observer and state observer are designed simultaneously. Then, an output feedback tracking controller is designed based on outputs of two observers. By employing multiple Lyapunov function method, some sufficient conditions are given to analyse the mean square exponential stability with weighted $H_{\infty }$ performance for the closed-loop system. Furthermore, the controller and observer gains are solved by linear matrix inequalities. At last, the validity of the presented scheme is demonstrated via two examples.
Deep CNN and Equivalent Channel Based Hybrid Precoding for mmWave Massive MIMO Systems

Millimeter wave (mmWave) system tends to have a large number of antenna elements to compensate for the high channel path loss. The immense number of BS antennas incurs high system costs, power, and interconnect bandwidth. To circumvent these obstacles, two-step hybrid precoding algorithms that enable the use of fewer RF chains have been proposed. However, the precoding schemes already in place are either too complex or not performing well enough. In this study, an equivalent channel hybrid precoding was proposed. The part from the transmitter RF chain to the receiver RF chain is regarded as equivalent channel. By reducing the dimension of channel matrix to the level of RF link number, baseband pre-coder is simply calculated from decomposing the equivalent channel matrix $\mathbf {H}_{equ}$ , which greatly reduces the complexity. Based on this novel precoding approach and convolutional neural network (CNN), a novel combiner neural network architecture was also proposed, which can be trained to learn how to optimize the combiner for maximizing the spectral efficiency with hardware limitation and imperfect CSI. Simulation results show that the proposed approaches achieve significant performance improvement.
An Approach for Classifying Skin Diseases Using Texture Analysis

Early diagnosis of skin diseases, mostly in developing countries, is necessary since it has serious impact on people’s quality of life. In this study, an automated model is proposed to classify skin diseases for dermoscopic images using texture analysis. There are some challenges for automating the classification of skin lesions such as existence of hair in the lesion, dark and highlight regions in dermoscopic image and different types of skin tone. In this study, these challenges are addressed with class imbalance issue, an internecine and common problem of skin disease image datasets. By combining different techniques or algorithms an effective approach is introduced to allay those challenges. The performance of the proposed approach is tested for two dermatological skin conditions viz. Solar Lentigo, Lentigo Simplex using k-th nearest neighbor (kNN) and support vector machine (SVM) as classifier.
Coordinated Wholesale and Retail Market Mechanism for Providing Demand-side Flexibility

With the trend of electricity market deregulation for recent decades, the consumers at demand-side are progressively encouraged to trade the power in competitive market environment. Conventionally, only large-scale consumers participate directly in the wholesale market that adopts spot energy pricing. Most small-scale consumers are involved in the retail market that offers non-varying tariff predefined by retailers or utility companies. In this work, we investigate a framework of coordinated wholesale and retail market structure that enables consumers in the retail market to reflect the spot price and improve the power system flexibility for the high penetration of renewables. Then, market behaviors of demand-side participants with the proposed market mechanism and some related business models are discussed. The following participation modes for consumers could be opted including: direct trade, trade agented by retailers or organized in virtual power plant (VPP). For a pool-based trade at demand-side, a bi-level market clearing mechanism at transmission and distribution levels is explained. This mechanism seeks to optimize the global demand profile for hosting renewables while solving the operating constraint of loads and local networks. This study can offer constructive advises to promote further development of China’s electricity market.
Super-Resolution Reconstruction Method of Remote Sensing Image Based on Multi-Feature Fusion

The acquisition of remote sensing images is affected by imaging equipment and environmental conditions. Usually on lower performance devices, the resolution of the acquired images is also low. Among many methods, the super-resolution reconstruction method based on generative adversarial networks has obvious advantages over previous network models in reconstructing image texture details. However, it is found in experiments that not all of these reconstructed textures exist in the image itself. Aiming at the problem of whether the texture details of the reconstructed image are accurate and clear, we propose a super-resolution reconstruction method combining wavelet transform and generative adversarial network. Using wavelet multi-resolution analysis, training wavelet decomposition coefficients in the generative adversarial network can effectively improve the local detail information of the reconstructed image. Experimental results show that our method can effectively reconstruct more natural image textures and make the images more visually clear. In the remote sensing image test set, the four indicators of the algorithm, peak signal to noise ratio (PSNR), structural similarity (SSIM), Feature Similarity (FSIM) and Universal Image Quality (UIQ) are slightly better than the algorithms mentioned in the article.
Binary-Weighted Synaptic Circuit for Neuromorphic Learning System Using Stochastic Memristor SPICE Model

The memristive device is a nanoscale nonlinear passive two-terminal fourth fundamental circuit element in addition to the three previously known passive fundamental circuit elements namely resistor, capacitor, and inductor. However aside from its non-volatile memory nature, this memristor resistance/ memristance controlled in the circuit operation by the amount of charge applied between its terminals. The memristor device SPICE modeling is significant for memristive circuit and neuromorphic system design. Nowadays probabilistic switching behavior observed in many fabricated memristor devices that inspired stochastic learning rule for memristor-based neuromorphic learning system application. In this paper, a stochastic metastable switch memristor model (MSSs) is used for binary-weighted memristor-based artificial synapse circuitry presentation. Using this MSSs memristor SPICE model a binary-weighted memristor-based artificial synapse circuit presented. The presented circuit shows a binary response to the signal given to the memristor implemented in the binary synaptic circuit using a stochastic memristor device model. The authors left the implementation of the proposed binary synaptic circuit in a memristor-based artificial neural network that functions through the clipped perceptron (CP) learning algorithm as future work.
Design and Development of IoT based Supervisory Control and Data Acquisition System

Present industrial Supervisory Control and Data Acquisition (SCADA) systems use different protocols along with various modes and mediums of communication to transfer data in remote places. Most of the existing protocols work with the same transmission mode. Drawbacks of such communication protocols caused wireless communication schemes to flourish, facilitating the design of wireless monitoring and controlling systems. In this paper, a unique approach to monitor data and control devices using industrial website by the implementation of Internet of Things (IoT) technology is presented. A standard wireless system based on Internet is designed to connect individual devices without the dependency on their compatibility, overcoming the limitations of extant communication protocols by ensuring secure and reliable data transmission.
From logs to Stories: Human-Centred Data Mining for Cyber Threat Intelligence

An average medium-sized organisation logs approx. 10 to 500 mln events per day on the system. Only less than 5% of threat alerts are being investigated by the specialised staff, leaving the security hole open for potential attacks. Insufficient information in alert message produced in machine-friendly rather than human-friendly format causes cognitive overload on currently limited cybersecurity resources. In this paper, the model that generates the report in natural language by means of applying novel storytelling techniques from security logs is proposed. The solution caters for different levels of reader expertise and preference by providing adjustable templates, filled from both local and global knowledge base. The validation is performed on case study from Security Operations Centre (SOC) at educational institution. The report generated proves superior to existing approach in terms of comprehension (increased cognition) and completeness (enriched context). The evaluation demonstrates power of storytelling in potential threats interpretation in cybersecurity context.
Performance Study of Different PLL Schemes Under Unbalanced Grid Voltage

Phase locked loop (PLL) circuit tracks continuously, the fundamental angular frequency of the grid voltage. There are many PLL techniques introduced in the past decades. Some of the techniques only give good results in normal grid voltage, and some techniques give good results in both normal and abnormal grid voltage conditions. The performance of five different PLL techniques: an abc Reference Frame PLL, a Stationary Reference Frame PLL (αβ-PLL), a Synchronous Reference Frame PLL (dq-PLL), a Voltage Reforming Synchronous Reference Frame PLL (VRSR-PLL), a Decoupled Double Synchronous Reference Frame PLL (DDSRF-LL); are analyzed in this paper under three grid voltage conditions: a set of three-phase balanced voltages, a set of three-phase unbalanced voltages in magnitudes and a set of three-phase unbalanced voltages in magnitudes and phases. The work suggests some improvements in the basic PLL techniques so that the performance of the PLLs are improved. A comparison table is included for selection of the appropriate PLL scheme depending on the condition of the grid voltage.
Adaptive Fuzzy Low-pass Filter Based L1 Adaptive Controller Design for Twin Rotor MIMO System

In this paper a systematic approach of designing an adaptive fuzzy low-pass filter based L1 adaptive controller is proposed. Basic L1 adaptive controller uses high adaptation rate to obtain fast transient response by estimating and eliminating disturbances and uncertainties exist in the system. To eliminate high frequency content from control channel a low-pass filter is appended which cannot perform well enough when the tuned controller is subjected to unknown disturbances with different magnitudes and frequencies. To cope up with such problem in this paper the filter structure is designed utilizing fuzzy logic system which depends on the disturbances and uncertainties in the system. Adaptive structure of low-pass filter adjust itself according to the disturbance and hence can properly cancel out them. Lyapunov stability criteria formulates the adaptation law of fuzzy low-pass filter. The proposed method provides robustness with fast transient performance as well as guarantees efficient steady state tracking performance. To validate the effectiveness of the proposed method, it is employed on a twin rotor MIMO system (TRMS).
Real Time Indian License Plate Detection using Deep Neural Networks and Optical Character Recognition using LSTM Tesseract

Among the ranking of the largest road network in the world, India stood at third position. According to a survey held in 2016 the total number of vehicles in India were 260 million. Therefore, there is a necessity to develop Expert Automatic Number Plate Recognition (ANPR) systems in India because of the tremendous rise in the number of automobiles flying on the roads. It would help in proper tracking of the vehicles,expert traffic examining, tracing stolen vehicles, supervising parking toll and imposing strict actions against red light breaching. Implementing an ANPR expert system in real life seems to be a challenging task because of the variety of number plate (NP) formats,designs, shapes, color, scales, angles and non-uniform lightning situations during image accession. So, we implemented an ANPR system which acts more robustly in different challenging scenarios then the previous proposed ANPR systems.The goal of this paper,is to design a robust technique forLicense Plate Detection(LPD) in the images using deep neural networks, Pre-process the detected license platesand performLicense Plate Recognition (LPR) usingLSTMTesseract OCR Engine. According to our experimentalresults, we have successfully achieved robust results withLPD accuracy of 99% and LPR accuracy of 95%just like commercial ANPR systemsi.e. , Open-ALPRand Plate Recognizer.
Design and Performance Comparison of Si and SiC MOSFETs for Dual Tank LCL-Type Series Resonant Converter

This paper presents operation, analysis, design, simulation and experimental results for a fixed-frequency half-bridge dual tank high-frequency (HF) transformer isolated soft switching LCL-type series resonant dc-dc converter and its performance comparison when Si and SiC MOSFETs are used. Operation of the converter is presented with operating waveforms and equivalent circuits for different intervals. Approximate analysis approach is used to obtain design curves and design equations. A 300 W, 300 V output converter switching at 100 kHz is designed to illustrate the design procedure. PSIM simulation results are given to evaluate the performance of the designed converter. An experimental converter is built to test the performance of Si and SiC MOSFETs. It is shown that SiC MOSFETs operate with an efficiency of 97.1% at full load and at 93.7% at 20% load. This converter can be used in alternate energy applications, such as PV array, and can provide step-up operation with HF transformer isolation and high efficiency.
Predicting Water Quality for the Woronora Delivery Network with Sparse Samples

Monitoring drinking water quality in the entire delivery network, mainly indicated by total chlorine (TC), is a critical component of overall water supply management. However, it is extremely difficult to collect sufficient TC data from the network at customer sites, which makes it sparse for comprehensive modelling. This paper details an approach that provides TC prediction within the entire Woronora delivery network in Sydney in the next 24 hours. First, the hydraulic system is employed to capture the topology of the delivery network, so that the water travel time can be estimated using predicted water demand. The travel time links the upstream (reservoir) data to the downstream (resident) data. Then, a two-step strategy is proposed as a semi-parametric method to determine the crucial factors and build Bayesian model for TC decay to predict TC with the travel time. Lastly, the uncertainties of both data and the model are analysed to define the boundaries of prediction for better decision making. Several operational stages are involved when the approach is being deployed, including prediction interpretation, interactive tool development for water quality mapping and visualisation, and proactive optimisation. This has established a successful initiative to improve the overall water supply management for the entire Woronora delivery network.