Finite element analysis method as an alternative for furniture prototyping process and product testing

In the current furniture industry, making furniture goes through many steps. There are ordering materials, designing, building a prototype, and testing samples. This process is considered quite complex, requiring significant costs, and lengthy production time. The application of finite element analysis (FEA) can be a solution to simulate the furniture manufacturing process. Objective of this research was to determine FEA could substitute making and test prototype furniture thereby saving costs and time. This method utilizes ANSYS 18.1 software for more accurate and rapid calculations, incorporating load variables of 400 N, 600 N, 800 N, and 1,000 N, along with gravitational acceleration of 10 \frac{m}{s^2}. The research evaluates the difference (expressed as a percentage) between the results obtained from simulations and those obtained directly from experiments, considering maximum equivalent stress, maximum principal stress, and total deformation values. The final step involves comparing the simulation with direct testing in terms of cost and time. The research results show an average error factor of 5% across all aspect. In terms of cost, the method can save 1,807 USD and reduce production time by up to one month. From these findings, it can be concluded that the process of prototyping and sample testing can be replaced using the finite element method

A study of IoT based real-time monitoring of photovoltaic power plant

Global electricity demand has increased in the last few years. This need is growing all the time as energy consumption increases using conventional energy, which will soon be phased out. So, we had to look at alternative energies, namely renewable energies. The largest and most efficient of these is solar energy, and to make the most of this energy with the greatest efficiency, the performance of these solar panels needs to be directly monitored. This study presents an independent monitoring system based on the internet of things (IoT) to measure essential factors (terminal voltage, load current, energy consumption, humidity, temperature, and light intensity). These values are realistic and accurate, based on the sensors used to measure the aforementioned factors and then using the Node MCU ESP8266 to transmit the analyzed data to the circuit. The Thingspeak platform was then employed to display, analyze, and store these results in real time

An approximate model SpMV on FPGA assisting HLS optimizations for low power and high performance

High performance computing (HPC) in embedded systems is particularly relevant with the rise of artificial intelligence (AI) and machine learning at the edge. Deep learning models require substantial computational power, and running these models on embedded systems with limited resources poses significant challenges. The energy-efficient nature of field-programmable gate arrays (FPGAs), coupled with their adaptability, positions them as compelling choices for optimizing the performance of sparse matrix-vector multiplication (SpMV), which plays a significant role in various computational tasks within these fields. This article initially did analysis to find a power and delay efficient SpMV model kernel using high level synthesis (HLS) optimizations which incorporates loop pipelining, varied memory access patterns, and data partitioning strategies, all of this exert influence on the underlying hardware architecture. After identifying the minimum resource utilization model, we propose an approximate model algorithm on SpMV kernel to reduce the execution time in Xilinx Zynq-7000 FPGA. The experimental results shows that the FPGA power consumption was reduced by 50% when compared to a previously implemented streaming dataflow engine (SDE) flow, and the proposed approximate model improved performance by 2× times compared to that of original compressed sparse row (CSR) sparse matrix

A novel approach to transparent and accurate fuel dispensing for consumer protection

Consumer rights are exploited around the world and it is necessary for to protect consumer rights by means of safeguarding consumers from various unfair trade practices. Those most vulnerable to such exploitation must be shielded, and this is achieved through consumer protection measures. One such example of unethical behavior is fuel stealing at fuel stations. To overcome this critical issue, a low-cost fuel quantity sensing and monitoring system is proposed in this paper. A fuel detection system will ensure the exact quantity of fuel filled in fuel tank and will detect fuel theft, if any, at fuel pumps. An embedded system is developed for this purpose, consisting of sensors, display devices, communication devices and microcontroller. The quantity of fuel filled in the tank is transmitted to mobile phone of the consumer to avoid fuel theft. Performance of the system is validated by comparing the displayed amount of fuel dispensed and actual filled in the tank and achieve 99.95% accuracy. With this consumer right to get the value for amount paid for the petrol will be protected. This novel feature can be added in the fuel tank of the smart vehicle development and design as a future scope

Machine learning methods for energy sector in internet of things

This research paper focuses on exploring machine learning studies and conducting a comparative analysis of their advantages, disadvantages, implementation environments, and algorithms. A key aspect of the study involves evaluating the energy efficiency using machine learning algorithms to predict energy consumption. Additionally, a feature selection algorithm is employed to rank the features, with the highest-ranking feature identified as one of the most significant. The experimentation is conducted using the Weka tool, incorporating several machine learning algorithms such as linear regression, k-nearest neighbors, decision stump, radial basis function (RBF) network, and isotonic regression. The RBF algorithm, which relies on RBF, shares similarities with neural network algorithms. Results indicate a minimum error value of 1.546 for cooling load and 1.364 for heating load. The random forest algorithm emerges as the most suitable choice within the context of this study

Test and measurement automation of printed circuit board assembly using digital oscilloscope

The testing and measurement (TM) of electrical parameters of printed circuit board assembly (PCBA) plays an integral part in the manufacturing sectors. These industries work on embedded system which widely use digital oscilloscopes (DO) for such purposes, however, operate them manually. An exponential rise in the implementation of industry 4.0 with the increasing demand for industrial products makes manual TM cumbersome. The automation of oscilloscopes (AO) remains a viable alternative to these issues requiring further investigation. An accurate and automated TM block facilitates efficient design, development, and assembly of a fully functional system hence addressed here. The AO has been carried out using generalized software that can be configured based on industry requirements. It subsequently stores the data on the server for better traceability. The automated software is developed using VB.NET and installed on a personal computer. Experiments reveal the proposed approach saves approximately 60%-70% of the time required for each PCBA operation than that of the manual system. This can enhance the productivity of the industry in terms of manpower and Resource utilization with a reduction in operating costs

Design of a dual-band bandpass filter with shunt stubs for wireless local area network and satellite communication system

High-performance radio frequency (RF) modules are required in transmitter and reception devices due to the recent expansion of wireless technology. The power amplifier, low-noise amplifier, filter, and mixer are the most crucial components in the RF transmitter/receiver chain. This work presents the design and analysis of a dual-band bandpass filter (BPF) for wireless local area network (WLAN) and C-band satellite applications. Stubs of the proper electrical length that are open and short-circuited are used to implement the filter. The low pass performance is generated by the open-circuited stubs. Short-circuited stubs achieve high-pass performance, while the combination of open and short-circuited stubs achieves bandpass performance. We confirm the filter's behaviour using the advanced design system 2022 simulation tool. The findings of return loss and insertion loss confirm the simulation-level performance analysis of the filter. The result demonstrates the suggested BPF's dual-band behaviour at 4 GHz and 6 GHz

Different methods of antenna reconfiguration by switches: a review

The rapid advancement of wireless communication technology has focused researcher's attention on reconfigurable antennas with multiple input and output (MIMO) and cognitive radio operation in high-data-rate modern wireless applications. Reconfigurable antennas perform various functions in terms of operating frequency, radiation pattern, and polarization. Electronic, mechanical, physical, and optical switches are used in reconfigurable antennas as control elements to adjust the switching mechanism and accomplish dynamic tuning. Electronic switches are the most widely used component in reconfigurable antennas because of their effectiveness, dependability, and simplicity in integrating with microwave circuitry. In this paper, a review of various kinds of efficient implementation methods for electrically controlled frequency reconfigurable antennas are proposed. More electrical switches are being used for reconfiguration such as micro electromechanical systems (MEMS), P-type, intrinsic, N-type (PIN), and varactor diodes. Even though PIN diodes are more frequently employed for reconfiguration due to their stability and constant variation in internal inductor and capacitor values. This study provides a deep analysis of the PIN diode usage in reconfigurable antennas and how to reduce the diodes in different microstrip reconfigurable antenna structures

Performance comparison of indoor navigation and obstacle avoidance methods for low-cost implementation in wheelchairs

Wheelchairs are a huge support for the movement of people who have disabilities. The wheelchairs that were traditionally moved using manual effort have given way to powered and smart wheelchairs with various controlling methods. When powered wheelchairs are used indoors, navigation and avoiding obstacles become challenging and tricky for a disabled user. To address these challenges there have been implementations of expensive and high-end systems to make the wheelchair move autonomously but as a result such a wheelchair is not economically viable for many users. Thus, there is a need for an alternative low cost method for users to be able to navigate and move in an indoor environment. The paper reviews low-cost methods for implementing indoor navigation systems, weighing their performances to validate if these methods can be used as a viable alternative to the high-cost systems for autonomous navigation in an indoor environment

Self-attention encoder-decoder with model adaptation for transliteration and translation tasks in regional language

The recent advancements in natural language processing (NLP) have highlighted the significance of integrating machine transliteration with translation for enhanced language services, particularly in the context of regional languages. This paper introduces a novel neural network architecture that leverages a self-attention mechanism to create an autoencoder without the need for iterative or convolutional processes. The selfattention mechanism operates on projection matrices, feature matrices, and target queries, utilizing the Softmax function for optimization. The introduction of the self-attention encoder-decoder with model adaptation (SAEDM) represents a breakthrough, marking a substantial enhancement in transliteration and translation accuracy over previous methodologies. This innovative approach employs both student and teacher models, with the student model's loss calculated through the probabilities and prediction labels via the negative log entropy function. The proposed architecture is distinctively designed at the character level, incorporating a word-to-word embedding framework, a beam search algorithm for sentence generation, and a binary classifier within the encoder-decoder structure to ensure the uniqueness of the content. The effectiveness of the proposed model is validated through comprehensive evaluations using transliteration and translation datasets in Kannada and Hindi languages, demonstrating its superior performance compared to existing models