Application of a TID Controller for the LFC of a Multi Area System using HGS Algorithm

A Tilt Integral Derivative (TID) controller is designed in this paper for the Load Frequency Control (LFC) issue of a multi-area interconnected restructured power system. The suggested TID controller settings are fine-tuned using a novel optimization technique known as Hunger Games Search (HGS) algorithm. A multi-area interconnected power system with various generating units is used to test the performance of the proposed TID controller based on HGS. The suggested controller also takes into account system non-linearities such as Generation Rate Constraints (GRCs) and Governor Dead Band (GDB). The superiority of HGS's optimization over a range of other significant optimization techniques, such as the grey-wolf optimization algorithm, has been confirmed. The simulation results show that the proposed TID controller based on HGS improves system frequency stability significantly under a variety of load perturbation scenarios

Fail Over Strategy for Fault Tolerance in Cloud Computing Environment

YesCloud fault tolerance is an important issue in cloud computing platforms and applications. In the event of an unexpected system failure or malfunction, a robust fault-tolerant design may allow the cloud to continue functioning correctly possibly at a reduced level instead of failing completely. To ensure high availability of critical cloud services, the application execution and hardware performance, various fault tolerant techniques exist for building self-autonomous cloud systems. In comparison to current approaches, this paper proposes a more robust and reliable architecture using optimal checkpointing strategy to ensure high system availability and reduced system task service finish time. Using pass rates and virtualised mechanisms, the proposed Smart Failover Strategy (SFS) scheme uses components such as Cloud fault manager, Cloud controller, Cloud load balancer and a selection mechanism, providing fault tolerance via redundancy, optimized selection and checkpointing. In our approach, the Cloud fault manager repairs faults generated before the task time deadline is reached, blocking unrecoverable faulty nodes as well as their virtual nodes. This scheme is also able to remove temporary software faults from recoverable faulty nodes, thereby making them available for future request. We argue that the proposed SFS algorithm makes the system highly fault tolerant by considering forward and backward recovery using diverse software tools. Compared to existing approaches, preliminary experiment of the SFS algorithm indicate an increase in pass rates and a consequent decrease in failure rates, showing an overall good performance in task allocations. We present these results using experimental validation tools with comparison to other techniques, laying a foundation for a fully fault tolerant IaaS Cloud environment

A nationwide study of adults admitted to hospital with diabetic ketoacidosis or hyperosmolar hyperglycaemic state and COVID‐19

AimsTo investigate characteristics of people hospitalized with coronavirus-disease-2019 (COVID-19) and diabetic ketoacidosis (DKA) or hyperosmolar hyperglycaemic state (HHS), and to identify risk factors for mortality and intensive care admission.Materials and methodsRetrospective cohort study with anonymized data from the Association of British Clinical Diabetologists nationwide audit of hospital admissions with COVID-19 and diabetes, from start of pandemic to November 2021. The primary outcome was inpatient mortality. DKA and HHS were adjudicated against national criteria. Age-adjusted odds ratios were calculated using logistic regression.ResultsIn total, 85 confirmed DKA cases, and 20 HHS, occurred among 4073 people (211 type 1 diabetes, 3748 type 2 diabetes, 114 unknown type) hospitalized with COVID-19. Mean (SD) age was 60 (18.2) years in DKA and 74 (11.8) years in HHS (p < .001). A higher proportion of patients with HHS than with DKA were of non-White ethnicity (71.4% vs 39.0% p = .038). Mortality in DKA was 36.8% (n = 57) and 3.8% (n = 26) in type 2 and type 1 diabetes respectively. Among people with type 2 diabetes and DKA, mortality was lower in insulin users compared with non-users [21.4% vs. 52.2%; age-adjusted odds ratio 0.13 (95% CI 0.03-0.60)]. Crude mortality was lower in DKA than HHS (25.9% vs. 65.0%, p = .001) and in statin users versus non-users (36.4% vs. 100%; p = .035) but these were not statistically significant after age adjustment.ConclusionsHospitalization with COVID-19 and adjudicated DKA is four times more common than HHS but both associate with substantial mortality. There is a strong association of previous insulin therapy with survival in type 2 diabetes-associated DKA

A FUZZY BASED DIVIDE AND CONQUER ALGORITHM FOR FEATURE SELECTION IN KDD INTRUSION DETECTION DATASET

This paper provides a fuzzy logic based divide and conquer algorithm for feature selection and reduction among large feature set of KDD intrusion detection data set, since a reduced feature set will help to evolve better mining rules.This algorithm introduces a fuzzy idea of dividing the normal record by attacks records or vice-versa, and then considers the feature sets for every attack type separately. Actually, this algorithm is applied on KDD CUP 99 dataset having 37 attack types and selecting important feature among 41 feature of KDD dataset. The selected features are used in TANAGRA [11, 12] data mining tool to classify the dataset (i.e. KDD 99) for every attack vs. normal using various classification algorithms [5, 6]. The result for feature selection and classification shows a reduced set and maximized classification rate respectively

ASSOCIATION RULE MINING FOR KDD INTRUSION DETECTION DATA SET

Network intrusion detection includes a set of malicious actions that compromise the integrity, confidentiality and availability of information resources. Several techniques for mining rules from KDD intrusion detection dataset [10] enables to identify attacks in the network. But little research has been done to determine the association patterns that exist between the attributes in the dataset. This paper focuses on the association rule mining in KDD intrusion dataset. Since the dataset constitutes different kinds of data like binary, discrete & continuous data, same technique cannot be applied to determine the association patterns. Hence, this paper uses varying techniques for each type of data. The proposed method is used to generate attack rules that will detect the attacks in network audit data using anomaly detection. Rules are formed depending upon various attack types. For binary data, Apriori approach is used to eliminate the non-frequent item set from the rules and for discrete and continuous value the proposed techniques are used. The paper concludes with experimental results

Selective epidemic broadcast algorithm to suppress broadcast storm in vehicular ad hoc networks

Broadcasting in Vehicular Ad Hoc Networks is the best way to spread emergency messages all over the network. With the dynamic nature of vehicular ad hoc networks, simple broadcast or flooding faces the problem called as Broadcast Storm Problem (BSP). The issue of the BSP will degrade the performance of a message broadcasting process like increased overhead, collision and dissemination delay. The paper is motivated to solve the problems in the existing Broadcast Strom Suppression Algorithms (BSSAs) like p-Persistence, TLO, VSPB, G-SAB and SIR. This paper proposes to suppress the Broadcast Storm Problem and to improve the Emergency Safety message dissemination rate through a new BSSA based on Selective Epidemic Broadcast Algorithm (SEB). The simulation results clearly show that the SEB outperforms the existing algorithms in terms of ESM Delivery Ratio, Message Overhead, Collision Ratio, Broadcast Storm Ratio and Redundant Rebroadcast Ratio with decreased Dissemination Delay