A new method for accurate detection of movement intention from single channel EEG for online BCI

Low frequency readiness potential (RP) is elicited in electroencephalograms (EEGs) as one intends to perform an imagery (IMI) or real movement (RMI). While in most brain-computer-interface (BCI) applications the challenge is to classify RPs of different limbs from the given EEG trials, the objective of this study is fast and automatic detection of RPs from the entire single channel EEG signal. The proposed algorithm has two threshold blocks based on the nonlinear Teager-Kaiser energy operator (TEO) in the first block and the morphological properties of the RP waveform as constraints in the second block. The performance is strongly influenced by the abrupt energy changes due to transients and artefacts. As the major contribution, the proposed nonlinear convex optimization algorithm enables separation of transients from low frequency components by providing a fast thresholding mechanism. Application of the proposed method to Physionet RMI dataset, BCI competitionIV-1 IMI dataset and our own left hand movement datasets of healthy subjects led to true positive rates (TPRs) of 76.5±8.27%, 83.85±11.4%, and 81.1±5.23%, number of FPs/min of 2.4±1.07, 1.4±0.7, and 1.6±0.69 and accuracy rates of 85.4±3.83%, 90±3.56%, and 91.2±2.04%. Movement onset detection latency from our automatic RP detector was -384.9±296.5 ms. As a conclusion, the proposed method outperforms state-of-the-art techniques using as low as single channel EEG making it suitable for real-time neuro-rehabilitation of paralyzed subjects suffering from stroke

Simultaneous removal of phenol and linear alkylbenzene sulfonate from automotive service station wastewater: Optimization of coupled electrochemical and physical processes

This work investigates the feasibility of the coupled electrochemical (electrocoagulation/flotation) and physical processes (sedimentation, sand filtration, and activated carbon) for the treatment of automotive service wastewater (ASWW). The impacts of critical parameters, viz. pH solution, reaction time and current intensity on linear alkylbenzene sulfonate (LAS), and phenol removal efficiencies as well as energy consumption and operating cost are studied. Central composite design results reveal that at the optimum conditions, LAS and phenol removal efficiencies, energy consumption and operating cost are obtained 96.7%, 87.65%, 15.99 Wh, 0.001 US$, respectively. This process reveals a feasible technology for phenol and LAS removal from ASW

Simultaneous removal of phenol and linear alkylbenzene sulfonate from automotive service station wastewater: Optimization of coupled electrochemical and physical processes

This work investigates the feasibility of the coupled electrochemical (electrocoagulation/flotation) and physical processes (sedimentation, sand filtration, and activated carbon) for the treatment of automotive service wastewater (ASWW). The impacts of critical parameters, viz. pH solution, reaction time and current intensity on linear alkylbenzene sulfonate (LAS), and phenol removal efficiencies as well as energy consumption and operating cost are studied. Central composite design results reveal that at the optimum conditions, LAS and phenol removal efficiencies, energy consumption and operating cost are obtained 96.7%, 87.65%, 15.99 Wh, 0.001 US$, respectively. This process reveals a feasible technology for phenol and LAS removal from ASWW

A novel underdetermined source recovery algorithm based on k-sparse component analysis

Sparse component analysis (SCA) is a popular method for addressing underdetermined blind source separation in array signal processing applications. We are motivated by problems that arise in the applications where the sources are densely sparse (i.e. the number of active sources is high and very close to the number of sensors). The separation performance of current underdetermined source recovery (USR) solutions, including the relaxation and greedy families, reduces with decreasing the mixing system dimension and increasing the sparsity level (k). In this paper, we present a k-SCA-based algorithm that is suitable for USR in low-dimensional mixing systems. Assuming the sources is at most (m−1) sparse where m is the number of mixtures; the proposed method is capable of recovering the sources from the mixtures given the mixing matrix using a subspace detection framework. Simulation results show that the proposed algorithm achieves better separation performance in k-SCA conditions compared to state-of-the-art USR algorithms such as basis pursuit, minimizing norm-L1, smoothed L0, focal underdetermined system solver and orthogonal matching pursuit

About the Health and Safety Executive. Process Safety Based on Risk and Performance Indicators

From the perspective of risk-based safety, the risk level of the risks in the process industry is not the same and it is necessary to identify larger risks, with the approach of the various resources available in the organization, which are often finite, in an optimal and effective manner. The section will be used. Measuring and monitoring performance indicators in this strategy is very important. These indicators provide a complete picture of the usefulness and effectiveness of the safety-related activities of the process, while identifying gaps and weaknesses in the system, will provide a good guide for complementary actions in order to enhance the safety of the process. In this paper, while describing the principles of safety of the risk-based process, its elements have been introduced and 105 functional indicators, including progressive and bottom-up, have been defined for it. From the indicators defined in this research, we can select the most suitable ones and use them to improve the safety of the process industries

Energy feasibility of hybrid PV/wind systems with electricity generation assessment under Iran environment

The need for the use of renewable energy helps human beings transmit the world to future generations. People, farmers, and governments tend to use renewable energy to generate electricity for their own consumption. Among the many sorts of renewable energy resources available in Iran, wind energy and solar energy are the fastest growing and most attractive renewable energy resources for electricity production. In this study, wind and solar energy potentiality is evaluated for four cities in Iran including Ahvaz, Sirjan, Neyshabur and Tabriz. The numerical analysis utilized wind speed, solar radiation and temperature data measured in 2018 from Iran Meteorological Organization to study electricity generation for the four cities. The results show that electricity is generated by solar and wind energy for all four cities of Tabriz, Neyshabur, Ahvaz and Sirjan are 5444.56, 7642.49, 9335.89 and 8084.47 MW h, respectively. Using solar energy in Ahvaz and Sirjan cities and wind energy in Neyshabur and Tabriz cities are rational to generate electricity.https://link.springer.com/journal/11949hj2021Mechanical and Aeronautical Engineerin

Performance evaluation of solar power plants: a review and a case study

The world’s electricity generation has increased with renewable energy technologies such as solar (solar power plant), wind energy (wind turbines), heat energy, and even ocean waves. Iran is in the best condition to receive solar radiation due to its proximity to the equator (25.2969◦ N). In 2020, Iran was able to supply only 900 MW (about 480 solar power plants and 420 MW home solar power plants) of its electricity demand from solar energy, which is very low compared to the global average. Yazd, Fars, and Kerman provinces are in the top ranks of Iran, with the production of approximately 68, 58, and 47 MW using solar energy, respectively. Iran also has a large area of vacant land for the construction of solar power plants. In this article, the amount of electricity generation using solar energy in Iran is studied. In addition, the construction of a 10 MW power plant in the city of Sirjan is economically and technically analyzed. The results show that with US$16.14 million, a solar power plant can be built in the Sirjan region, and the initial capital will be returned in about four years. The results obtained using Homer software show that the highest maximum power generation is in July