Multi-Agent Deep Reinforcement Learning-Driven Mitigation of Adverse Effects of Cyber-Attacks on Electric Vehicle Charging Station

An electric vehicle charging station (EVCS) infrastructure is the backbone of transportation electrification. However, the EVCS has myriads of exploitable vulnerabilities in software, hardware, supply chain, and incumbent legacy technologies such as network, communication, and control. These standalone or networked EVCS open up large attack surfaces for the local or state-funded adversaries. The state-of-the-art approaches are not agile and intelligent enough to defend against and mitigate advanced persistent threats (APT). We propose the data-driven model-free distributed intelligence based on multiagent Deep Reinforcement Learning (MADRL)-- Twin Delayed Deep Deterministic Policy Gradient (TD3) -- that efficiently learns the control policy to mitigate the cyberattacks on the controllers of EVCS. Also, we have proposed two additional mitigation methods: the manual/Bruteforce mitigation and the controller clone-based mitigation. The attack model considers the APT designed to malfunction the duty cycles of the EVCS controllers with Type-I low-frequency attack and Type-II constant attack. The proposed model restores the EVCS operation under threat incidence in any/all controllers by correcting the control signals generated by the legacy controllers. Also, the TD3 algorithm provides higher granularity by learning nonlinear control policies as compared to the other two mitigation methods. Index Terms: Cyberattack, Deep Reinforcement Learning(DRL), Electric Vehicle Charging Station, Mitigation.Comment: Submitted to IEEE Transactions on Smart Grid

3-D Printed Radar Absorber with Meta-material Features for X-band Application

This paper presents a structured radar absorber with metamaterial features based on graphite SLS composite. The unit cell of the proposed design was simulated on COMSOL Multiphysics to determine its frequencydependent absorption characteristics and fabricated using low-cost selective laser sintering 3-D printing technology. The measurement and simulation results showed an effective absorption bandwidth of 1.04 GHz and 2.08 GHz respectively. The optimized structure however, revealed broadband absorption in a frequency range between 8.35 to 12.20 GHz (X band) under normal incidence. Besides, the absorption performance under different polarizations and incident angles were investigated. Results indicated that the absorber exhibits polarization indifference and high absorptivity at a wide angle of incidence. The advantages of low cost, ultra-broad operating band, wide-angle feature, and polarization insensitivity made the proposed absorber a promising candidate in military and civilian applications

Experimental investigation of two-phase pressure drop in a microchannel

This paper was presented at the 2nd Micro and Nano Flows Conference (MNF2009), which was held at Brunel University, West London, UK. The conference was organised by Brunel University and supported by the Institution of Mechanical Engineers, IPEM, the Italian Union of Thermofluid dynamics, the Process Intensification Network, HEXAG - the Heat Exchange Action Group and the Institute of Mathematics and its Applications.An experimental study of two-phase pressure drop in a horizontal circular microchannel is presented in this paper. Test tube is made of fused silica having an internal diameter of 781μm with a total length of 261mm and a heated length of 191mm.The external surface of the test tube is coated with an electrically conductive thin layer of ITO (Indium Tin Oxide) for direct heating of test section. Refrigerant R134a was used as the working fluid and mass flux during the experiments was varied from 100 to 650 kg/m2sec. Experiments were performed at a system pressure of 7.70 bar (corresponding to saturation temperature of 30oC). Two-phase frictional pressure drop characteristics with different mass flux, vapour fraction and heat flux were explored in detail. Finally, the prediction capability of well known available correlations in the literature, developed for macrochannels and others especially developed for microchannels was also checked. The homogeneous model predicts the data fairly well with a mean absolute deviation (MAD) of 19% and 69% of data within ±20% error band. The Müller-Steinhagen and Heck (1986) correlation developed for macrochannels predicts the data with a MAD of 19% and 61% of data within ±20% error band. The Mishima and Hibiki (1996) correlation, developed for microchannels, also shows fairly good approximation of the data with MAD of 19% and 57% of data within ±20% error band

Modeling the dispersal effect to reduce the infection of COVID-19 in Bangladesh.

In this paper, we propose a four compartmental model to understand the dynamics of infectious disease COVID-19. We show the boundedness and non-negativity of solutions of the model. We analytically calculate the basic reproduction number of the model and perform the stability analysis at the equilibrium points to understand the epidemic and endemic cases based on the basic reproduction number. Our analytical results show that disease free equilibrium point is asymptotically stable (unstable) and endemic equilibrium point is unstable (asymptotically stable) if the basic reproduction number is less than (greater than) unity. The dispersal rate of the infected population and the social awareness control parameter are the main focus of this study. In our model, these parameters play a vital role to control the spread of COVID-19. Our results reveal that regional lockdown and social awareness (e.g., wearing a face mask, washing hands, social distancing) can reduce the pandemic of the current outbreak of novel coronavirus in a most densely populated country like Bangladesh

Dynamic priority-based efficient resource allocation and computing framework for vehicular multimedia cloud computing

© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works In intelligent transportation system, smart vehicles are equipped with a variety of sensing devices those offer various multimedia applications and services related to smart driving assistance, weather forecasting, traffic congestion information, road safety alarms, and many entertainment and comfort-related applications. These smart vehicles produce a massive amount of multimedia related data that required fast and real-time processing which cannot be fully handled by the standalone onboard computing devices due to their limited computational power and storage capacities. Therefore, handling such multimedia applications and services demanded changes in the underlaying networking and computing models. Recently, the integration of vehicles with cloud computing is emerged as a challenging computing paradigm. However, there are certain challenges related to multimedia contents processing, (i.e., resource cost, fast service response time, and quality of experience) that severely affect the performance of vehicular communication. Thus, in this paper, we propose an efficient resource allocation and computation framework for vehicular multimedia cloud computing to overcome the aforementioned challenges. The performance of the proposed scheme is evaluated in terms of quality of experience, service response time, and resource cost by using the Cloudsim simulator

Lip syncing method for realistic expressive 3D face model

Lip synchronization of 3D face model is now being used in a multitude of important fields. It brings a more human, social and dramatic reality to computer games, films and interactive multimedia, and is growing in use and importance. High level of realism can be used in demanding applications such as computer games and cinema. Authoring lip syncing with complex and subtle expressions is still difficult and fraught with problems in terms of realism. This research proposed a lip syncing method of realistic expressive 3D face model. Animated lips requires a 3D face model capable of representing the myriad shapes the human face experiences during speech and a method to produce the correct lip shape at the correct time. The paper presented a 3D face model designed to support lip syncing that align with input audio file. It deforms using Raised Cosine Deformation (RCD) function that is grafted onto the input facial geometry. The face model was based on MPEG-4 Facial Animation (FA) Standard. This paper proposed a method to animate the 3D face model over time to create animated lip syncing using a canonical set of visemes for all pairwise combinations of a reduced phoneme set called ProPhone. The proposed research integrated emotions by the consideration of Ekman model and Plutchik’s wheel with emotive eye movements by implementing Emotional Eye Movements Markup Language (EEMML) to produce realistic 3D face model. © 2017 Springer Science+Business Media New Yor

A grey approach to predicting healthcare performance

© 2018 Elsevier Ltd The success of an organization or a particular activity is evaluated through the measurement of key performance indicators (KPIs). The aim of this paper is to analyze and predict the indicators of healthcare performance using grey systems theory. Recent advancements in science and technology have made the healthcare industry extremely efficient at collecting data using electronic claims systems such as electronic health records. Therefore, collecting field level primary data becomes easier and accumulate them to generate secondary data for research purpose and to get an insight of the organization performance is absolutely necessary. Our research analyzes the KPIs of a hospital based on a secondary data source. Since, secondary data contains uncertainty and sometimes poor information, grey prediction model suits best to make a prediction model in this regard. Conventional grey model has considerable drawbacks while making a rigorous prediction model. For this, we apply an improved grey prediction model to predict the KPIs of the healthcare performance indicators. Several error measures in our model give a best fit of the data and allow prediction of the KPIs. The prediction model gives good estimates of the quantitative indicators and produced error rate within an acceptable range. We observe that the KPIs of bed turnover rate (BTR) and bed occupancy rate (BOR) have an increasing trend, whereas the KPIs of average length of stay (ALOS), hospital death rate (HDR) and hospital infection rate (HIR) show a decreasing trend over time. The main contribution of this research is a grey-based prediction model that can provide managers with the information they need to evaluate and predict the performance of a hospital. The research indicates that managers should give greater priority to the indicators which will result in better patients’ satisfaction and improved profit margin. Healthcare managers striving towards better performance will now have an empirical basis upon which to formulate and adjust their strategies, after analyzing the predicted value

Upaya Meningkatkan Motivasi Belajar Matematika Melalui Metode Bermain Peran Pada Siswa Kelas II SD Negeri 6 Sindurejo Tahun Pelajaran 2012 / 2013

Tujuan penelitian ini untuk meningkatkan motivasi belajar matematika melalui metode bermain peran pada siswa kelas 2 SD Negeri 6 Sindurejo Kecamatan Toroh Kabupaten Grobogan Jawa Tengah pada Tahun 2012 /2013. Pada saat kegiatan pembelajaran pra siklus, siswa tidak memperhatikan penjelasan guru, kurang antusias, kurang aktif, dan perhatian siswa terpecah pada hal lain. Metode penelitian yang dilakukan untuk mengatasi masalah tersebut ialah dengan menggunakan metode bermain peran yang melibatkan unsur sosial dalam pembelajaran sehingga melibatkan aktivitas seluruh siswa. Hasil penelitian ini menunjukkan secara teoritik dan empirik melalui metode bermain peran dapat meningkatkan motivasi belajar matematika. Hal ini dibuktikan pada pra siklus dari 18 siswa yang motivasinya meningkat hanya 28 %, naik menjadi 33 % pada siklus I dan 94% pada siklus II. Data itu menunjukkan tindakan perbaikan telah mencapai keberhasilan karena sudah melampaui indikator pencapaian peningkatan motivasi yang ditetapkan yaitu sebesar ≥80%

real time fault detection in photovoltaic systems

Abstract In this paper, a method for real time monitoring and fault diagnosis in photovoltaic systems is proposed. This approach is based on a comparison between the performances of a faulty photovoltaic module, with its accurate model by quantifying the specific differential residue that will be associated with it. The electrical signature of each default will be fixed by considering the deformations induced on the I-V curves. Some faults, such as: interconnection resistance faults and different shading patterns are considered. The proposed technique can be generalized and extended to more types of faults. The fault diagnosis will be determined by fixing a normal and a fault threshold for each fault. These thresholds are calculated based on the Euclidean norm between ideal and normal measurement or between ideal and fault mode measurement. Each threshold is set in a range bounded by the minimum and maximum values of the differential residue obtained for the considered fault. The proposed approach provides identification of faults by calculating their specific threshold ranges. This method allows the instantaneous monitoring of the electrical power delivered by the photovoltaic system

Drivers to sustainable manufacturing practices and circular economy: A perspective of leather industries in Bangladesh

© 2017 Elsevier Ltd Sustainable manufacturing practices and the circular economy have recently received significant attention in academia and within industries to improve supply chain practices. Manufacturing industries have started adopting sustainable manufacturing practices and a circular economy in their supply chain to mitigate environmental concerns, as sustainable manufacturing practices and a circular economy result in the reduction of waste generation and energy and material usage. The leather industry, in spite of it contributing remarkably to a country's economic growth and stability, does not bear a good image because of its role in polluting the environment. Therefore, the leather industries of Bangladesh are trying to implement sustainable manufacturing practices as a part of undertaking green supply chain initiatives to remedy their image with the buyer and to comply with government rules and regulations. The main contribution of this study is to assess, prioritize and rank the drivers of sustainable manufacturing practices in the leather industries of Bangladesh. We have used graph theory and a matrix approach to examine the drivers. The results show that knowledge of the circular economy is paramount to implementing sustainable manufacturing practices in the leather industry of Bangladesh. This study will assist managers of leather companies to formulate strategies for the optimum utilization of available resources, as well as for the reduction of waste in the context of the circular economy