Deep learning-based meta-learner strategy for electricity theft detection

Electricity theft damages power grid infrastructure and is also responsible for huge revenue losses for electric utilities. Integrating smart meters in traditional power grids enables real-time monitoring and collection of consumers’ electricity consumption (EC) data. Based on the collected data, it is possible to identify the normal and malicious behavior of consumers by analyzing the data using machine learning (ML) and deep learning methods. This paper proposes a deep learning-based meta-learner model to distinguish between normal and malicious patterns in EC data. The proposed model consists of two stages. In Fold-0, the ML classifiers extract diverse knowledge and learns based on EC data. In Fold-1, a multilayer perceptron is used as a meta-learner, which takes the prediction results of Fold-0 classifiers as input, automatically learns non-linear relationships among them, and extracts hidden complicated features to classify normal and malicious behaviors. Therefore, the proposed model controls the overfitting problem and achieves high accuracy. Moreover, extensive experiments are conducted to compare its performance with boosting, bagging, standalone conventional ML classifiers, and baseline models published in top-tier outlets. The proposed model is evaluated using a real EC dataset, which is provided by the Energy Informatics Group in Pakistan. The model achieves 0.910 ROC-AUC and 0.988 PR-AUC values on the test dataset, which are higher than those of the compared models

Line overload alleviations in wind energy integrated power systems using automatic generation control

Modern power systems are largely based on renewable energy sources, especially wind power. However, wind power, due to its intermittent nature and associated forecasting errors, requires an additional amount of balancing power provided through the automatic generation control (AGC) system. In normal operation, AGC dispatch is based on the fixed participation factor taking into account only the economic operation of generating units. However, large-scale injection of additional reserves results in large fluctuations of line power flows, which may overload the line and subsequently reduce the system security if AGC follows the fixed participation factor’s criteria. Therefore, to prevent the transmission line overloading, a dynamic dispatch strategy is required for the AGC system considering the capacities of the transmission lines along with the economic operation of generating units. This paper proposes a real-time dynamic AGC dispatch strategy, which protects the transmission line from overloading during the power dispatch process in an active power balancing operation. The proposed method optimizes the control of the AGC dispatch order to prevent power overflows in the transmission lines, which is achieved by considering how the output change of each generating unit affects the power flow in the associated bus system. Simulations are performed in Dig SILENT software by developing a 5 machine 8 bus Pakistan’s power system model integrating thermal power plant units, gas turbines, and wind power plant systems. Results show that the proposed AGC design efficiently avoids the transmission line congestions in highly wind-integrated power along with the economic operation of generating units

Object Oriented Model for Evaluation of On-Chip Networks

Abstract: The Network on Chip (NoC) paradigm is rapidly replacing bus based System on Chip (SoC) designs due to their inherent disadvantages such as non-scalability, saturation and congestion. Currently very few tools are available for the simulation and evaluation of on-chip architectures. This study proposes a generic object oriented model for performance evaluation of on-chip interconnect architectures and algorithms. The generic nature of the proposed model can help the researchers in evaluation of any kind of on-chip switching networks. The model was applied on 2D-Mesh and 2D-Diagonal-Mesh on-chip switching networks for verification and selection of best out of both the analyzed architectures. The results show the superiority of 2D-Diagonal-Mesh over 2D-Mesh in terms of average packet delay

Seasonality in Presentation of Acute Appendicitis

Background:. To assess the trends in incidence of appendicitis and pattern of variation with age, sex, and seasons of the year. Methods: In this cross-sectional  prospective study patients who underwent appendectomy for acute appendicitis were included. The demographic features, length of hospital stay, seasonal variation and post-operative outcome were assessed . The diagnosis of acute appendicitis was  established by history, examination and investigations in term of leukocyte count, urinalysis and ultrasound exam in many of these cases. In North Punjab region, the year is divided into two well-marked seasons with short transitional periods between the long hot rainless summer (May to October) and comparatively short cool winter (December to February).SPSS version 16 was used for all the statistical assessments and analysis Results: Out of 972 patients, 53% patients were males. Age range was from 5-70 years. All the patients treated surgically by open and laparoscopic means. Forty patients were found to have perforated appendix, 12 patients presented with abdominal mass and 3 patients presented with appendicular abscess. A significant seasonal effect was observed, with the rate of acute appendicitis being higher in the summer months. Conclusion: A seasonal pattern of appendicitis with a mostly predominant peak is seen during the summer months could be due to increased gastrointestinal infections in summer. The males have higher incidence of acute appendicitis with 11-20 years of age being most common age grou

Changes in Liver Fibrosis as Determined by FIB-4 Score Following Sofosbuvir-Based Treatment Regimes Without Interferon

Objective: To determine the mean change in liver fibrosis as evaluated using the FIB-4 score following Sofosbuvir based treatment regimens without interferon. Methodology: This prospective observational study was conducted at the Department of Medicine, Federal Government Services Hospital, Islamabad, from January 09, 2019 to January 03, 2020. A total of seventy (n=70) patients of either gender between age 18-75 years who were diagnosed with cases of HCV infection were enrolled in this study. All patients were treated with Sofosbuvir-based treatment regimens and were assessed for liver fibrosis using the FIB-4 score at baseline, at end of treatment (EOT) and 12 weeks after EOT. Results: The mean FIB-4 score at baseline was 2.45±0.42, at EOT was 1.0981±0.33 and at 12 weeks after EOT was 1.51±0.32.  As compared to the baseline, the mean FIB-4 score was significantly lesser at EOT (P=0.001) and at 12 weeks after EOT (P=0.001). A similar trend was observed across all stratified groups, i.e., age, gender, and type of patients (P<0.05 across all groups). Conclusion: The sofosbuvir-based treatment regimen significantly reduced liver fibrosis at EOT and 12 weeks after EOT, as evidenced by FIB-4 scores that were significantly lower than baseline at EOT and 12 weeks after EOT

Anatomical Variation of Olfactory Fossa on Computed Tomography of Paranasal Sinuses

Objective: To determine the frequency of anatomical variation of olfactory fossa among the adult Pakistani population by Keros classification on computed tomography (CT) of paranasal sinuses. Study Design: Cross-sectional study Place and Duration of Study: Department of Radiology, Combined Military Hospital, Rawalpindi Pakistan, from May 2019 to Mar 2020. Methodology: A total of 65 patients of either gender were included. Patients with previous trauma or surgery of the skull base or paranasal sinuses, malignant diseases of the sinuses and congenital anomalies were excluded. All the included patients in the study underwent CT paranasal sinuses. Measurements of the olfactory fossae followed by grouping as per Kero’s classification were done, and CT findings were recorded. Results: The patients included in the study ranged from 18 to 65 years, with a mean age of 33.09±10.86 years and 72.3% of patients 18 to 40 years of age. Of 65 patients, 36(55.4%) were males, and 29(44.6%) were females. The mean CT depth of the olfactory fossa was 6.34 ±4.03mm. Type-I olfactory fossa by Keros classification was found in 17(26.2%), Type-II in 35(53.8%) and Type-III in 13(20%) of patients. Conclusion: This study concluded that Keros Type-II is the most common anatomical variation of olfactory fossa among the adult Pakistani population on CT of paranasal sinuses with an intermsediate risk of intracranial complications during endoscopic sinus surgery involving this region

Three-Pond Model with Fuzzy Inference System-Based Water Level Regulation Scheme for Run-of-River Hydropower Plant

Power generation from river hydropower plants depends mainly on river flow. Water fluctuations in the river make the yield process unpredictable. To reduce these fluctuations, building a small reservoir at the river flow of the hydropower plant is recommended. Conventionally, classic single-pond models are commonly used to design run-of-river hydropower plants. However, such models are associated with fluctuations, sagging, and irregular power fluctuations that lead to irregular water fluctuations. This research proposes a novel idea to replace the single-pond model with a three-pond model to increase the plant’s overall efficiency. The three-pond model is developed as a three-tank nonlinear hydraulic system that contains the same amount of water as a conventional single pond. It also has the advantage of minimizing the run-of-river power plant’s dependence on river flow and increasing efficiency by trapping swell and turbulence in the water. To further increase the efficiency, the developed model was tested for smooth and effective level control using fuzzy control.publishedVersionPeer reviewe

An Integrated Design for Classification and Localization of Diabetic Foot Ulcer based on CNN and YOLOv2-DFU Models

Diabetes is a chronic disease, if not treated in time may lead to many complications including diabetic foot ulcers (DFU). DFU is a dangerous disease, it needs regular treatment otherwise it may lead towards foot amputation. The DFU is classified into two categories such as infection (bacteria) and the ischaemia (inadequate supply of the blood). The DFU detection at an initial phase is a tough procedure. Therefore in this research work 16 layers convolutional neural network (CNN) for example 01 input, 03 convolutional, 03 batch-normalization, 01 average pooling, 01 skips convolutional, 03 ReLU, 01 add (element-wise addition of two inputs), fully connected, softmax and classification output layers for classification and YOLOv2-DFU for localization of infection/ischaemia models are proposed. In the classification phase, deep features are extracted and supplied to the number of classifiers such as KNN, DT, Ensemble, softmax, and NB to analyze the classification results for the selection of best classifiers. After the experimentation, we observed that DT and softmax achieved consistent results for the detection of ischaemia/infection in all performance metrics such as sensitivity, specificity, and accuracy as compared with other classifiers. In addition, after the classification, the Gradient-weighted class activation mapping (Grad-Cam) model is used to visualize the high-level features of the infected region for better understanding. The classified images are passed to the YOLOv2-DFU network for infected region localization. The Shuffle network is utilized as a mainstay of the YOLOv2 model in which bottleneck extracted features through ReLU node-199 layer and passed to the YOLOv2 model. The proposed method is validated on the newly developed DFU-Part (B) dataset and the results are compared with the latest published work using the same dataset

Deep Semantic Segmentation and Multi-Class Skin Lesion Classification Based on Convolutional Neural Network

Skin cancer is developed due to abnormal cell growth. These cells are grown rapidly and destroy the normal skin cells. However, it's curable at an initial stage to reduce the patient's mortality rate. In this article, the method is proposed for localization, segmentation and classification of the skin lesion at an early stage. The proposed method contains three phases. In phase I, different types of the skin lesion are localized using tinyYOLOv2 model in which open neural network (ONNX) and squeeze Net model are used as a backbone. The features are extracted from depthconcat7 layer of squeeze Net and passed as an input to the tinyYOLOv2. The propose model accurately localize the affected part of the skin. In Phase II, 13-layer 3D-semantic segmentation model (01 input, 04 convolutional, 03 batch-normalization, 03 ReLU, softmax and pixel classification) is used for segmentation. In the proposed segmentation model, pixel classification layer is used for computing the overlap region between the segmented and ground truth images. Later in Phase III, extract deep features using ResNet-18 model and optimized features are selected using ant colony optimization (ACO) method. The optimized features vector is passed to the classifiers such as optimized (O)-SVM and O-NB. The proposed method is evaluated on the top MICCAI ISIC challenging 2017, 2018 and 2019 datasets. The proposed method accurately localized, segmented and classified the skin lesion at an early stage.Qatar University [IRCC-2020-009]