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2022 ◽  
Vol 4 (3) ◽  
pp. 461-473
Sintiani Perdani ◽  
Didik Ari Wibowo ◽  
Desmira Desmira

Around 35% of the total utilization of coconuts at this time is still not fully utilized. Thermoelectric is a technology that converts heat energy directly into electrical energy or converts electrical energy into heating and cooling energy. Data retrieval using two multimeters and an electric thermometer, data collection was carried out for 2 minutes. From the test results, this tool can produce an average voltage of 10.05 Volt for 200gram coconut shells, an average current of 0.99 Ampere and an average power of 13.84 Watts and can fully charge the battery up to 3 hours 33 minutes, while for 300 grams produces an average voltage of 10.59 Volts for 300gram coconut shells, an average current of 0.995 Ampere and an average power of 13.56 Watts and the battery can be fully charged in about 3 hours 36 minutes, while a coconut shell weighing 400 grams can produces an average voltage of 10.94 Volts, an average current of 1 Ampere and an average power of 13.70 Watts and the battery can be fully charged in about 3 hours 30 minutes. The more coconut shells used for combustion, the hotter the temperature and the faster the voltage and current are obtained, but with a note that the maximum temperature limit of the thermoelectric is T not more than 200o C. Keywords: Coconut Shell, Thermoelectric, Electrical Energy.

Computers ◽  
2022 ◽  
Vol 11 (1) ◽  
pp. 13
Imran Zualkernan ◽  
Salam Dhou ◽  
Jacky Judas ◽  
Ali Reza Sajun ◽  
Brylle Ryan Gomez ◽  

Camera traps deployed in remote locations provide an effective method for ecologists to monitor and study wildlife in a non-invasive way. However, current camera traps suffer from two problems. First, the images are manually classified and counted, which is expensive. Second, due to manual coding, the results are often stale by the time they get to the ecologists. Using the Internet of Things (IoT) combined with deep learning represents a good solution for both these problems, as the images can be classified automatically, and the results immediately made available to ecologists. This paper proposes an IoT architecture that uses deep learning on edge devices to convey animal classification results to a mobile app using the LoRaWAN low-power, wide-area network. The primary goal of the proposed approach is to reduce the cost of the wildlife monitoring process for ecologists, and to provide real-time animal sightings data from the camera traps in the field. Camera trap image data consisting of 66,400 images were used to train the InceptionV3, MobileNetV2, ResNet18, EfficientNetB1, DenseNet121, and Xception neural network models. While performance of the trained models was statistically different (Kruskal–Wallis: Accuracy H(5) = 22.34, p < 0.05; F1-score H(5) = 13.82, p = 0.0168), there was only a 3% difference in the F1-score between the worst (MobileNet V2) and the best model (Xception). Moreover, the models made similar errors (Adjusted Rand Index (ARI) > 0.88 and Adjusted Mutual Information (AMU) > 0.82). Subsequently, the best model, Xception (Accuracy = 96.1%; F1-score = 0.87; F1-Score = 0.97 with oversampling), was optimized and deployed on the Raspberry Pi, Google Coral, and Nvidia Jetson edge devices using both TenorFlow Lite and TensorRT frameworks. Optimizing the models to run on edge devices reduced the average macro F1-Score to 0.7, and adversely affected the minority classes, reducing their F1-score to as low as 0.18. Upon stress testing, by processing 1000 images consecutively, Jetson Nano, running a TensorRT model, outperformed others with a latency of 0.276 s/image (s.d. = 0.002) while consuming an average current of 1665.21 mA. Raspberry Pi consumed the least average current (838.99 mA) with a ten times worse latency of 2.83 s/image (s.d. = 0.036). Nano was the only reasonable option as an edge device because it could capture most animals whose maximum speeds were below 80 km/h, including goats, lions, ostriches, etc. While the proposed architecture is viable, unbalanced data remain a challenge and the results can potentially be improved by using object detection to reduce imbalances and by exploring semi-supervised learning.

2022 ◽  
Vol 17 (01) ◽  
pp. P01011
I.V. Konoplev ◽  
S.A. Bogacz ◽  
Y. Shashkov ◽  
M.A. Gusarova

Abstract Energy-frontier accelerators provide powerful tools performing high precision measurements confirming the fundamentals of the physics and broadening new research horizons. Such machines are either driven by circular or linear accelerators. The circular machines, having the centre-of-mass (CM) energy values reaching 200 GeV (for leptons) and above, experience beam energy loss and quality dilution, for example, due to synchrotron radiation, limiting the overall CM energy achievable and requiring a constant energy top-up to compensate the loss and the beam quality dilution. Linear colliders overcome these limitations, while the finite capabilities of generating high average current beams limits the luminosity. This is partially compensated by the quality of the colliding beams. In this work, we suggest a novel design of circular-linear accelerator based on the merging of the “non-emitting”, low-energy storage rings and energy recovery linear accelerators. We suggest using the recently considered dual-axis asymmetric cavities to enable the operation of such a system, and in particular the energy recovery from spent, high-intensity beams. The machine considered, under the scope of the SNOWMASS-2021 initiative, can be potentially used to reach ultimate energy frontiers in high-energy physics as well as to drive next generation light sources. The merging of circular and linear systems, and applications of dual axes cavities, should allow the maintaining of high beam quality, high luminosity, and high energy efficiency, while offering a flexible energy management and opening clear opportunity for reducing the running cost. We note that the numbers shown in the paper are for illustration purpose and can be improved further.

2021 ◽  
Vol 2021 ◽  
pp. 1-9
A. Arbie ◽  
Z. A. Hasan ◽  
A. W. Nuayi

This study is aimed at determining the piezoelectric configuration for generating electricity from wave power through the design of a prototype model named Cov-TOTal. The study was carried out in Tomini Bay, Lopo Village, Batudaa Pantai District, Gorontalo Regency, located at approximately ±50 meters from the shoreline, while the piezoelectric construction was arranged in parallel with varying numbers of 28, 70, and 90 pieces. The result showed that the amount of piezoelectric configuration affects the value of the voltage and electric current generated by the Cov-TOTal model. Furthermore, the average electric voltage values were 17.58, 20.76, and 29.85 volts, while the average current was 1.16, 1.73, and 2.01 mA for each piezoelectric amount. Therefore, the largest values of power and electrical energy for each piezoelectric are 16.65 mW and 0.56 joules, 31.82 mW and 1.20 joules, and 44.59 mW and 1.77 joules, respectively. This study concluded that the amount of piezoelectric configuration has a significant effect on the voltage, current, power, and electrical energy produced.

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Yue Wang ◽  
Ghulam Bary ◽  
Riaz Ahmad ◽  
Dameng Yin ◽  
Shiwei Xie ◽  

Geometry, electrostatics, and single-electron tunneling contribute to the nonlinearity in the quantum dot embedded nanomechanical resonator, while “Duffing term” is a kind of mathematics describing the third-order nonlinearity of the system as a whole. We study theoretically the influence of a variation of a mathematical parameter Fuffing term on the actual physical effect. The position probability distribution, the average current, and the displacement fluctuation spectrum with the different Duffing parameter and electromechanical coupling are obtained through numerically calculating the Fokker Planck equation. The mechanical bistability has been described by these quantities under different electromechanical coupling and Duffing parameters. We conclude that the nonlinearities of the nanotube resonator contribute to the mechanical bistability, which induces the asymmetry of the position probability distribution, compresses the current, and softens or stiffens the mechanical resonance frequency as the same as the electromechanical coupling to use it in mechanical engineering.

2021 ◽  
pp. 11-15
Sergei Moshkunov ◽  
Kiril Romanov ◽  
Vladislav Khomich ◽  
Ekaterina Shershunova

This work is devoted to the study of the electrical characteristics of a discharge with a microhollow cathode in air at atmospheric pressure. It was experimentally found that the discharge with a microhollow cathode developed in a self-pulsing mode with an average current of hundreds of microamperes. According to the experimental data it was concluded that the self-pulsing frequency linearly depended on the consumed current. The values of the ignition voltage of the self-pulsing discharge with a microhollow cathode at different hole diameters were obtained.

2021 ◽  
Vol 2108 (1) ◽  
pp. 012025
Yang Lu ◽  
Puqiong Yang

Abstract In recent years, digital control has been widely used in the field of power electronics. Automatic code generation, as a graphical software writing method, has brought great convenience to the writing of electronic control software due to a series of advantages such as easy to write and easy to simulate and verify. Simcoder in PSIM is an automatic code generation software that enables the automatic generation of complete DSP control program projects. In this article, the current and voltage loops of a typical average current controlled BUCK circuit are designed as an example, and the use of PSIM and Simcoder is described in detail. Based on the main circuit and Matlab parameter calculations, the model is simulated and the self-generated code is downloaded into the TMS320F28335 to test the output voltage and current of the hardware circuit. The circuit output has the advantage of stable output voltage with small steady-state error and low voltage overshoot, demonstrating the practicality and efficiency of automatic code generation on this project.

2021 ◽  
Vol 7 ◽  
pp. 292-299
Hanquan Zhang ◽  
Hongyu Zhu ◽  
Dongdong Zhang ◽  
Haisen Zhao ◽  
Yanli Zhang ◽  

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