Design and Development of Solar Panel Cleaning System

Solar energy is the most abundant source of energy for all the forms of life on the planet Earth. It is also the basic source for all the sources of energy except Nuclear Energy. But the solar technology has not matured to the extent of the conventional sources of energy. It faces lots of challenges such as high cost, erratic and unpredictable in nature, need for storage and low efficiency. This project aims at increasing the efficiency of solar power plants by solving the problem of accumulation of dust on the surface of solar panel which leads to reduction in plant output and overall plant efficiency. It proposes to develop a Solar Panel Cleaning System which could remove the accumulated dust on its surface on a regular basis and maintain the solar power plant output. The system is a robotic system which could move autonomously on the surface of solar panels by using pneumatic suction cups and use dry methods for cleaning such as rotating cylindrical brush and vacuum cleaning system keeping in mind the limited availability of water in areas where such plants are mainly located. This project also aims to reduce the human involvement in the process of solar panel cleaning as it is a very hazardous environment for them in scorching su

A Review on Simulation Optimization

One of the primary and most important employments of simulations is for optimization. Simulation optimization can be characterized as the way toward finding the best info variable qualities from among all potential outcomes without unequivocally evaluating each possibility. The goal of simulation optimization is to minimize the assets spent while boosting the data acquired in a simulation experiment. The purpose of this paper is to review the zone of simulation optimization. A critical review of the methods employed and applications developed in this generally new range are introduced and striking victories are highlighted. Simulation optimization software tools are discussed. The target group is simulation practitioners and theoreticians and additionally fledglings in the field of simulation

Enhancement of the physical and chemical properties of palm oil blends.

Fokus utama kajian ini adalah mengkaji perubahan sifat fizikal dan kimia bagi dwi-campuran daripada minyak sawit dan empat jenis minyak yang lain. Minyak sawit (PO) dicampurkan dengan minyak canola (CO), minyak kemiri (Cnd), minyak soya (SO) dan minyak bunga matahari (SF) secara berat / berat dengan nisbah yang berbeza. Kedua-dua kesan penuaan dan kesan pemanasan ke atas campuran minyak telah ditentukan. The main focus of this work was to investigate the changes in physico-chemical properties of the binary blends of palm olein with four other oils. Palm olein (PO) was blended w/w with Canola oil (CO), Candlenut oil (Cnd), Soybean oil (SO) and Sunflower oil (SF) in different proportions. Both aging effect and heating effect on these blends were determined

Characteristics of an adult photosensitive cohort

Intermittent Photic Stimulation (IPS) is a well recognized provocation maneuver during EEG recording which helps in identifying underlying photosensitivity. The frequency and characteristics of various responses to photosensitivity among our adult patients was determine. EEGs of subjects \u3e or =15 years from 2003-2006, were reviewed. Two main photosensitive responses were observed: Photoparoxysmal (PPR) and Photic-driving response (PDR). Demographic and clinical data was also collected. Out of 5950 EEG\u27s performed, response to IPS were detected in 1.2% (n=73) of EEGs. Out of which 49 (67%) had PDR and 24 (33%) had PPR. Mean age of PPR group was 25.7 +/- 12 years with equal gender distribution. In PPR subjects, epilepsy was the final EEG conclusion in majority of patients 96% (n=23) p-value \u3c 0.001. Primary Generalized Epilepsy being the most common 52% (n=12). In contrast, PDR observed in normal EEG\u27s was 45% (n=22). This study revealed low frequency 1.2% of photosensitive responses. PPR is seen frequently in patients with epilepsy (96%, p-value \u3c 0.001), and highest rate (52%) of photosensitivity was observed in patients with primary generalized epilepsy

Optimal lower bounds for universal relation, and for samplers and finding duplicates in streams

In the communication problem $\mathbf{UR}$ (universal relation) [KRW95], Alice and Bob respectively receive $x, y \in\{0,1\}^n$ with the promise that $x\neq y$. The last player to receive a message must output an index $i$ such that $x_i\neq y_i$. We prove that the randomized one-way communication complexity of this problem in the public coin model is exactly $\Theta(\min\{n,\log(1/\delta)\log^2(\frac n{\log(1/\delta)})\})$ for failure probability $\delta$. Our lower bound holds even if promised $\mathop{support}(y)\subset \mathop{support}(x)$. As a corollary, we obtain optimal lower bounds for $\ell_p$-sampling in strict turnstile streams for $0\le p < 2$, as well as for the problem of finding duplicates in a stream. Our lower bounds do not need to use large weights, and hold even if promised $x\in\{0,1\}^n$ at all points in the stream. We give two different proofs of our main result. The first proof demonstrates that any algorithm $\mathcal A$ solving sampling problems in turnstile streams in low memory can be used to encode subsets of $[n]$ of certain sizes into a number of bits below the information theoretic minimum. Our encoder makes adaptive queries to $\mathcal A$ throughout its execution, but done carefully so as to not violate correctness. This is accomplished by injecting random noise into the encoder's interactions with $\mathcal A$, which is loosely motivated by techniques in differential privacy. Our second proof is via a novel randomized reduction from Augmented Indexing [MNSW98] which needs to interact with $\mathcal A$ adaptively. To handle the adaptivity we identify certain likely interaction patterns and union bound over them to guarantee correct interaction on all of them. To guarantee correctness, it is important that the interaction hides some of its randomness from $\mathcal A$ in the reduction.Comment: merge of arXiv:1703.08139 and of work of Kapralov, Woodruff, and Yahyazade