There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

[신간의 별자리x] 우리/미술, 그리고 ‘슬픔의 박물관’

http://ieeexplore.ieee.org/iel5/5610941/5624686/05624745.pdf

Power electronics

The use of microbial fuel cells to generate electrical current is increasingly being seen as a viable source of renewable energy production In this Progress article, Bruce Logan highlights recent advances in our understanding of the mechanisms used by exoelectrogenic bacteria to generate electrical current and the important factors to consider in microbial fuel cell design There has been an increase in recent years in the number of reports of microorganisms that can generate electrical current in microbial fuel cells Although many new strains have been identified, few strains individually produce power densities as high as strains from mixed communities Enriched anodic biofilms have generated power densities as high as 69 W per m2 (projected anode area), and therefore are approaching theoretical limits To understand bacterial versatility in mechanisms used for current generation, this Progress article explores the underlying reasons for exocellular electron transfer, including cellular respiration and possible cell–cell communication

Exoelectrogenic bacteria that power microbial fuel cells

Solar Engineering Of Thermal Processes

Because of its high computation speed, shorter Because of its high computation speed, shorter design period, and higher density, the use of Field Programmable Gate Array (FPGA) has gained a lot of attention in the design and practical implementation of PWM (Pulse width modulation) techniques for MLI (Multilevel Inverter) structures. PWM techniques are designed to reduce switching losses, voltage stress of switches, total harmonic distortion (THD) and to increase the number of output voltage levels of inverter structures. This paper presents an Automatic Pulse Sequence Selector (APSS) for the Novel PWM technique by using FPGA LabVIEW software. The pulse sequence selection is done by using the sample index generated by the reference sinusoidal wave. By the automatic selection of specific pulse sequences from the sequences, the output wave of the inverter structure will be closer to the reference sinusoidal wave. The THD of the inverter output wave will be less than compared to direct feeding of those pulses to MLI structures. Using MATLAB/Simulink, a simulation study is performed on developed MLI structures for the validation of the proposed design. Comparison between the FFT analysis of these inverter structures verifies the efficacy of the design.

Automatic Pulse Sequence Selector for Novel PWM Technique: FPGA LabVIEW Implementation

Recent trends of power flow control in power system networks

ABSTRACT For a smart grid system operating with clustered generating units, the challenge usually lies in the optimal scheduling of energy resources. Thus, the improvement of a unit commitment problem gains importance in the present power network. In this paper, a novel probability algorithm is proposed for solving the unit commitment problem, in which five separate units from a four-cluster group are operated for one day. This is done with the aim of optimizing the characteristics such as optional units, generation, operating, and marginal cost. Being a multi-objective function, the other factors such as cost response, unit commitment of supply clustering units, clustering of combined operational units, and CO2 emissions are also included. For this study, an RLC load is mathematically modeled in the cluster system, whereas the Unit Control is provided by a Converter and Battery Management Unit. The clustering model developed along with the Unit demand aims to increase the optional units (from 20 to 100 units), decrease the average demand (from 99 to 93 percentage), and lower the generating and marginal costs (from Rs.23,881 to Rs.21,079, Rs.43,082 to Rs.42,111, Rs.71,162 to Rs.64,955, Rs.83,169 to Rs.81,694, Rs.104,104 to Rs.102,928). The suggested algorithm is simulated for a comprehensive smart grid system and the response for marginal cost, CO2 emissions are obtained. In comparison to conventional schemes, the results of the proposed optimization approach show a reduction in the Carbon dioxide emission (in the range of 100 to 400 kg) and in the losses after adding working units with cluster spans of 0.5 to 1.5 kW.

Balanced Standalone Clustering Unit Commitment Solution for Smart Grid Using Probability Algorithms

Five-phase induction machines (5pIMs) are a viable alternative for a wide range of industrial applications. The penetration of drives with 5pIM into industrial applications is slow because of the complexity of the proposed control algorithms. In standard 
 
 V
 /
 f
 
 control of induction machines, the stator current is not controlled directly and options for current limitation are restricted. This paper hence addresses a novel current limitation technique for the 
 
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 control algorithm of 5pIM. In addition, a simple speed sensorless control algorithm based on the slip compensation is introduced. A combination of both algorithms is suitable for low-cost medium-precision drive applications. Experimental results, realized on Texas Instruments F28335 DSP, illustrating the performance of novel current limitation technique and sensorless control, are included.

Current Limitation Method for 
 V
 /
 f
 Control of Five-Phase Induction Machines

The applications of wireless sensor networks are extensively used to detect and control home residents’ activities in smart homes. However, the sensors are battery-powered, so keeping them in active mode consumes tremendous energy. In this regard, we propose a solution to activate the smart home sensors based on detecting the upcoming activities using a Deep Long-Short Term Memory (DLSTM) model. The pre-trained model is then transferred to the same and different Target Domains (TDs) to reduce the time for training. The proposed system applies to preprocess and feature mapping steps to both the source and target data to make grounds for efficient transfer. Further, applying the trained model to the TD may miss the essential activities. Therefore, a reinforcement learning model is applied in the TD. To handle unusual activities in real-time, guard sensors are appointed among the idle sensors. The performance evaluation shows that the proposed scheme detects the activities with an accuracy of 96.1%. Additionally, the proposed scheme outperforms the sentry and prediction-based schemes in energy consumption of the sensors and network lifetime.

Sanjeevikumar Padmanaban

Papers

Automatic Pulse Sequence Selector for Novel PWM Technique: FPGA LabVIEW Implementation

Recent trends of power flow control in power system networks

Balanced Standalone Clustering Unit Commitment Solution for Smart Grid Using Probability Algorithms

Current Limitation Method for V / f Control of Five-Phase Induction Machines

Deep Transfer Learning-Enabled Energy Management Strategy for Smart Home Sensor Networks