No abstract

With the remarkable enlargement of the usage of computers through the network and expansion in application running on several platform captures the consideration toward network security. This hypothesis exploits security susceptibilities on the entire computer systems that are technically challenging and expensive to resolve. Therefore, intrusion is employs as a key to conciliate reliability, availability and privacy/confidentiality of a computer resource. An Intrusion Detection System (IDS) participates a noteworthy responsibility in detecting anomalies and attacks over's network. In this research work, data mining conception is integrated with IDS to sort assured the relevant, concealed information of interest for the user efficiently and with fewer implementation times. Four concerns likely Classification of Data, Lack of Labeled Data, Extreme Level of Human Interaction and Effectiveness of D-DOS are being resolved by using the projected algorithms like EDADT algorithm, Semi-Supervised Approach, Hybrid IDS model and transforming HOPERAA Algorithm respectively. In this paper, proposes a SVM and KNN-ACO method for the intrusion detection and the analysis of this is perform using KDD1999 Cup dataset. This proposed algorithm shows improved precision and concentrated false alarm rate when matched with existing algorithms.

To address the issue of energy scarcity and to use solar photovoltaic energy as a renewable source, a three-phase grid-connected photovoltaic inverter system with uncertain system model parameters is investigated, which converts DC power into AC power, feeds it into the grid, and maintains the grid-connected part’s quality. An enhanced back-stepping approach is proposed to explore the control strategy of three-phase solar grid-connected inverter, which includes adaptive control, dissipative theory, sliding mode control, and rapid terminal sliding mode control, respectively. It is verified that the system is subjected to external interference F = 5 during 0.5 s–0.8 s. As per the simulation results, the improved adaptive terminal sliding mode control method can suppress the interference to a certain extent and has strong robustness to the external interference. When compared with the adaptive sliding mode control approach, the anti-interference ability is better, and the system stability is improved. Then, to improve the above approach, a high speed terminal sliding mode control method is proposed, which can swiftly converge in finite time and the estimated value of uncertain parameters is closer to the actual value, thereby boosting the system’s robustness.

An automated guided vehicle (AGV) is a portable robot that follows markers or wires in the floor, or uses vision, magnets, or lasers for navigation. They are most often used in industrial applications to move materials around a manufacturing facility or warehouse. Application of the automatic guided vehicle broadened during the late 20th century. The AGV can tow objects behind them in trailers to which they can autonomously attach. The trailers can be used to move raw materials or finished product. The first AGV was brought to market in the 1950s, by Barrett Electronics of Northbrook, Illinois, and at the time it was simply a tow truck that followed a wire in the floor instead of a rail. Out of this technology came a new type of AGV, which follows invisible UV markers on the floor instead of being towed by a chain. The first such system was deployed at the Willis Tower (formerly Sears Tower) in Chicago, Illinois to deliver mail throughout its offices. AGVs are employed in nearly every industry, including pulp, paper, metals, newspaper, and general manufacturing. Transporting materials such as food, linen or medicine in hospitals is also done. Today, the AGV plays an important role in the design of new factories and warehouses, safely moving goods to their rightful destination.

Breathomics is the future of non invasive point-of-care devices. The field of breathomics can be split into isolation of disease specific volatile organic compounds and their detection. In the present work, an array of five polymer with nanomaterial additive-modified Quartz Tuning Fork (QTF)-based sensors has been utilized to detect samples of human breath spiked with ∼0.5 ppm of known volatile organic compounds (VOCs) which are bio-markers for certain diseases, namely, acetone, acetaldehyde, octane, decane, ethanol, methanol, styrene, propylbenzene, cyclohexanone, butanediol, and iso-propyl alcohol. Polystyrene was used as the base polymer and it was functionalized with 4 different fillers namely, Silver nanoparticles-reduced graphene oxide composite, titanium dioxide nanoparticles, Zinc Ferrite nanoparticles-reduced graphene oxide composite and cellulose acetate. Each of these fillers enhanced the selectivity of a particular sensor towards certain VOC compared to prestene polystyrene modified sensor. Their interaction with the VOCs in changing the mechanical properties of polymer giving rise to change in resonant frequency of QTF, is used as sensor response for detection. The interaction of functinolized polymers with VOCs was analyzed by FTIR and UV- vis spectroscopy. The Collective sensor response of five sensors is used to identify VOCs using ensemble classifier with 92.8% accuracy of prediction. The accuracy of prediction improved to 96% when isopropyl alcohol, ethanol and methanol were considered as one class.