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Aquaponics systems and technologies are growing primary industries in many countries, with high environmental and socio-economic advantages. Aquaponics is a closed-loop system that produces aquatic animals and plants in a new way using recirculated water and nutrients. With a growing world population expected to reach 9.7 billion by 2050, food production sustainability is a primary issue in today’s world agenda, and aquaponics and aquaculture systems can be potential contributors to the challenge. Observing the climate changes and global warming’s impact on the ecosystem, decreasing aqua animal stocks, and responding to increasing demand are turning points in the sustainability era. In the past 15 years, fish production has doubled, thus denoting that aquaponics transforms into commercial scales with a revolutionized production, high efficiency, and fewer resources’ utilization, thus requiring proper operation and management standards and practices. Therefore, this study aims to shape a new framework for sustainable aquaponics modeling and utilization as the all-in-one solution platform covering technical, managerial, socio-economic, institutional, and environmental measures within the suitability requirements. The proposed model in this study offers a systematic approach to the design and implementation of sustainability-efficient aquaponics and aquaculture systems. Through an exhaustive coverage of the topic, this research effort can be counted as a practical reference for researchers, scholars, experts, practitioners, and students in the context of aquaponics and aquaculture studies.
Mir Sayed Shah Danish; Tomonobu Senjyu; Najib Rahman Sabory; Mahdi Khosravy; Maria Luisa Grilli; Alexey Mikhaylov; Hemayatullah Majidi. A Forefront Framework for Sustainable Aquaponics Modeling and Design. Sustainability 2021, 13, 9313 .
AMA StyleMir Sayed Shah Danish, Tomonobu Senjyu, Najib Rahman Sabory, Mahdi Khosravy, Maria Luisa Grilli, Alexey Mikhaylov, Hemayatullah Majidi. A Forefront Framework for Sustainable Aquaponics Modeling and Design. Sustainability. 2021; 13 (16):9313.
Chicago/Turabian StyleMir Sayed Shah Danish; Tomonobu Senjyu; Najib Rahman Sabory; Mahdi Khosravy; Maria Luisa Grilli; Alexey Mikhaylov; Hemayatullah Majidi. 2021. "A Forefront Framework for Sustainable Aquaponics Modeling and Design." Sustainability 13, no. 16: 9313.
Compressive sensing has the ability of reconstruction of signal/image from the compressive measurements which are sensed with a much lower number of samples than a minimum requirement by Nyquist sampling theorem. The random acquisition is widely suggested and used for compressive sensing. In the random acquisition, the randomness of the sparsity structure has been deployed for compressive sampling of the signal/image. The article goes through all the literature up to date and collects the main methods, and simply described the way each of them randomly applies the compressive sensing. This article is a comprehensive review of random acquisition techniques in compressive sensing. Theses techniques have reviews under the main categories of (1) random demodulator, (2) random convolution, (3) modulated wideband converter model, (4) compressive multiplexer diagram, (5) random equivalent sampling, (6) random modulation pre-integration, (7) quadrature analog-to-information converter, (8) randomly triggered modulated-wideband compressive sensing (RT-MWCS).
Mahdi Khosravy; Thales Wulfert Cabral; Max Mateus Luiz; Neeraj Gupta; Ruben Gonzalez Crespo. Random Acquisition in Compressive Sensing. International Journal of Ambient Computing and Intelligence 2021, 12, 140 -165.
AMA StyleMahdi Khosravy, Thales Wulfert Cabral, Max Mateus Luiz, Neeraj Gupta, Ruben Gonzalez Crespo. Random Acquisition in Compressive Sensing. International Journal of Ambient Computing and Intelligence. 2021; 12 (3):140-165.
Chicago/Turabian StyleMahdi Khosravy; Thales Wulfert Cabral; Max Mateus Luiz; Neeraj Gupta; Ruben Gonzalez Crespo. 2021. "Random Acquisition in Compressive Sensing." International Journal of Ambient Computing and Intelligence 12, no. 3: 140-165.
As a side effect caused by new technologies, supraharmonics attracted the attention of researchers in recent years. Mitigation and analysis of supraharmonics distortions are of great importance to guarantee the power quality (PQ) available in the power grids since such distortions can cause serious damage to the grid-connected equipment. However, there are few works in literature reporting techniques for supraharmonics analysis, and PQ equipment is rarely capable of performing measurements in the supraharmonic range. This work proposes a supraharmonic estimation system utilizing a digital filter bank through a multirate digital signal processing technique. The polyphase decomposition applied in the decimating filter bank enables the simplification of the system implementation. Both synthetic and real signals are used to evaluate the technique in analyzing supraharmonic distortions. The proposed method shows a good application prospect in accurately measuring supraharmonics and with a significant reduction in computational complexity compared with the method presented in the IEC 61000-4-30 standard.
Thais M. Mendes; Danton D. Ferreira; Leandro R.M. Silva; Mahdi Khosravy; Jan Meyer; Carlos A. Duque. Supraharmonic estimation by polyphase DFT filter bank. Computers & Electrical Engineering 2021, 92, 107202 .
AMA StyleThais M. Mendes, Danton D. Ferreira, Leandro R.M. Silva, Mahdi Khosravy, Jan Meyer, Carlos A. Duque. Supraharmonic estimation by polyphase DFT filter bank. Computers & Electrical Engineering. 2021; 92 ():107202.
Chicago/Turabian StyleThais M. Mendes; Danton D. Ferreira; Leandro R.M. Silva; Mahdi Khosravy; Jan Meyer; Carlos A. Duque. 2021. "Supraharmonic estimation by polyphase DFT filter bank." Computers & Electrical Engineering 92, no. : 107202.
Multi-Input Multi-Output (MIMO) telecommunication systems with Orthogonal Frequency Division Modulation (OFDM) scheme support high rate data exchange which can be efficiently applied to the fast-growing networks of Internet of Things (IoT). In the case of Underwater IoT (UIoT) where the strength of electromagnetic waves rapidly falls off, the MIMO network can be effectively implemented by substitute of acoustic OFDM instead of the normal one. Due to these advantages, this research work suggests ICA based acoustic MIMO OFDM for UIoT wherein the data rate is further improved as the MIMO OFDM is implemented based on blind channel estimation and multi-user identification without the need for using any pilot and preamble data. Also, theoretical analysis shows the data rate increases and energy consumption decreases due to ICA-based MIMO acoustic channel estimation. In the case of the under-work UIoT network, it achieves 52.38% higher data rate, and 41.67% less energy consumption. As another specific result, the proposed method shows high efficiency on sparse underwater channels and as the channel sparseness gets lower, the efficiency decreases.
Mahdi Khosravy; Neeraj Gupta; Nilanjan Dey; Pablo Moreno Ger. Smart green ocean underwater IoT network by ICA-based acoustic blind MIMO OFDM transceiver. Earth Science Informatics 2021, 14, 1073 -1081.
AMA StyleMahdi Khosravy, Neeraj Gupta, Nilanjan Dey, Pablo Moreno Ger. Smart green ocean underwater IoT network by ICA-based acoustic blind MIMO OFDM transceiver. Earth Science Informatics. 2021; 14 (2):1073-1081.
Chicago/Turabian StyleMahdi Khosravy; Neeraj Gupta; Nilanjan Dey; Pablo Moreno Ger. 2021. "Smart green ocean underwater IoT network by ICA-based acoustic blind MIMO OFDM transceiver." Earth Science Informatics 14, no. 2: 1073-1081.
The hydraulic system is the backbone of industrial, construction, and agricultural machines. Its high-power density and efficiency execution make it the preferred choice for high-energy applications. This manuscript presents the evidence against the statement, Whenever the system runs to its full capacity, hydraulic oil heats up more as compared to partial loads.“ Our experimental study on Injection Molding Machine (IMM) hydraulic system tries to reshape the above statement by presenting the fact where we came across that lower system loading case is causing higher hydraulic oil temperatures than full loading.” The concern started when the machine at lower speeds results in frequent shutdowns due to increasing operating temperature high alarm!“ This article presents the study to resolve the stated issue completely. For the design evolution, a systematic diagnostic approach is displayed based on the six-sigma and the associated mathematical model. Our study concludes with an approach to find the self-balancing system for the case where the temperature increase is must be due to the higher rate of heat addition than the rate of heat rejection. Presented learning can be equally extended for the other fields of interest, as care should be taken to understand the passive system losses, which can result in elevated temperatures due to lower cooling capabilities.
Neeraj Gupta; Prashantha Kini; Saurabh Gupta; Hemant Darbari; Nisheeth Joshi; Mahdi Khosravy. Six Sigma based modeling of the hydraulic oil heating under low load operation. Engineering Science and Technology, an International Journal 2021, 24, 11 -21.
AMA StyleNeeraj Gupta, Prashantha Kini, Saurabh Gupta, Hemant Darbari, Nisheeth Joshi, Mahdi Khosravy. Six Sigma based modeling of the hydraulic oil heating under low load operation. Engineering Science and Technology, an International Journal. 2021; 24 (1):11-21.
Chicago/Turabian StyleNeeraj Gupta; Prashantha Kini; Saurabh Gupta; Hemant Darbari; Nisheeth Joshi; Mahdi Khosravy. 2021. "Six Sigma based modeling of the hydraulic oil heating under low load operation." Engineering Science and Technology, an International Journal 24, no. 1: 11-21.
Enhancement in solar energy (SE) injection into the power system network creates power quality (PQ) issues in the supply. This article presents an approach supported by Stockwell transform ( $S$ -transform) for assessment of PQ issues related with the grid interfaced solar photovoltaic (SPV) system under various operating conditions. This will help to enhance the SE integration level into the utility grid. The set up, to perform assessment of the PQ issues includes an emulated SPV system interfaced with the utility at the point of common coupling (PCC). Measurements of voltage and current signals are performed by utilizing power network analyzer. The captured voltage signals are analyzed using $S$ -transform for the detection of a variety of PQ problems associated with the grid interfacing and outage of the SPV system. Effects on PQ due to presence of the various types of loads at PCC have also been investigated under the same operating conditions. Effect of partial shading of SPV plates on the PQ is also investigated. Harmonic analysis is performed for all the investigated events. The proposed algorithm proved to be successful for detecting different PQ disturbances under all the investigated operating conditions.
Om Prakash Mahela; Abdul Gafoor Shaik; Neeraj Gupta; Mahdi Khosravy; Baseem Khan; Hassan Haes Alhelou; Sanjeevikumar Padmanaban. Recognition of Power Quality Issues Associated With Grid Integrated Solar Photovoltaic Plant in Experimental Framework. IEEE Systems Journal 2020, 15, 3740 -3748.
AMA StyleOm Prakash Mahela, Abdul Gafoor Shaik, Neeraj Gupta, Mahdi Khosravy, Baseem Khan, Hassan Haes Alhelou, Sanjeevikumar Padmanaban. Recognition of Power Quality Issues Associated With Grid Integrated Solar Photovoltaic Plant in Experimental Framework. IEEE Systems Journal. 2020; 15 (3):3740-3748.
Chicago/Turabian StyleOm Prakash Mahela; Abdul Gafoor Shaik; Neeraj Gupta; Mahdi Khosravy; Baseem Khan; Hassan Haes Alhelou; Sanjeevikumar Padmanaban. 2020. "Recognition of Power Quality Issues Associated With Grid Integrated Solar Photovoltaic Plant in Experimental Framework." IEEE Systems Journal 15, no. 3: 3740-3748.
This study presented a new multi-species binary coded algorithm, Mendelian evolutionary theory optimization (METO), inspired by the plant genetics. This framework mainly consists of three concepts: first, the “denaturation” of DNA’s of two different species to produce the hybrid “offspring DNA”. Second, the Mendelian evolutionary theory of genetic inheritance, which explains how the dominant and recessive traits appear in two successive generations. Third, the Epimutation, through which organism resist for natural mutation. The above concepts are reconfigured in order to design the binary meta-heuristic evolutionary search technique. Based on this framework, four evolutionary operators—(1) Flipper, (2) Pollination, (3) Breeding, and (4) Epimutation—are created in the binary domain. In this paper, METO is compared with well-known evolutionary and swarm optimizers: (1) binary hybrid GA, (2) bio-geography-based optimization, (3) invasive weed optimization, (4) shuffled frog leap algorithm, (5) teaching–learning-based optimization, (6) cuckoo search, (7) bat algorithm, (8) gravitational search algorithm, (9) covariance matrix adaptation evolution strategy, (10) differential evolution, (11) firefly algorithm and (12) social learning PSO. This comparison is evaluated on 30 and 100 variables benchmark test functions, including noisy, rotated, and hybrid composite functions. Kruskal–Wallis statistical rank-based nonparametric H-test is utilized to determine the statistically significant differences between the output distributions of the optimizer, which are the result of the 100 independent runs. The statistical analysis shows that METO is a significantly better algorithm for complex and multi-modal problems with many local extremes.
Neeraj Gupta; Mahdi Khosravy; Nilesh Patel; Nilanjan Dey; Om Prakash Mahela. Mendelian evolutionary theory optimization algorithm. Soft Computing 2020, 1 -46.
AMA StyleNeeraj Gupta, Mahdi Khosravy, Nilesh Patel, Nilanjan Dey, Om Prakash Mahela. Mendelian evolutionary theory optimization algorithm. Soft Computing. 2020; ():1-46.
Chicago/Turabian StyleNeeraj Gupta; Mahdi Khosravy; Nilesh Patel; Nilanjan Dey; Om Prakash Mahela. 2020. "Mendelian evolutionary theory optimization algorithm." Soft Computing , no. : 1-46.
Today’s Agriculture vehicles (AgV)s are expected to encompass mainly the three requirements of customers; economy, the use of High technology and reliability. In this manuscript, we investigate the technology solution for efficient health monitoring and diagnostic (HM&D) strategy to maximize the field efficiency and minimize the machine cost. Based on the data captured by various IoT sensors, we demonstrate the facts to shift the HM&D technology from costly sensor to economic microphone based mechanism. The adopted strategy is capable to reduce the bulky data transmission on the internet, and to increase the up-time of AgVs. We experimented on the essential red hot chili peppers system of the AgV’s backbone hydraulic system—the hydraulic filter and pump. The measurement system analysis is adopted to determine the preciseness of data captured near the considered components. The envision of the correlation between the collected data extracts significant information to draw the facts to embrace the future HM&D technology shift. Correlation between the signals captured from costly sensors and Microphone for the generated faults in hydraulic components demonstrates the effectiveness of audio to replace existing HM&D technology.
Neeraj Gupta; Saurabh Gupta; Mahdi Khosravy; Nilanjan Dey; Nisheeth Joshi; Rubén González Crespo; Nilesh Patel. Economic IoT strategy: the future technology for health monitoring and diagnostic of agriculture vehicles. Journal of Intelligent Manufacturing 2020, 32, 1117 -1128.
AMA StyleNeeraj Gupta, Saurabh Gupta, Mahdi Khosravy, Nilanjan Dey, Nisheeth Joshi, Rubén González Crespo, Nilesh Patel. Economic IoT strategy: the future technology for health monitoring and diagnostic of agriculture vehicles. Journal of Intelligent Manufacturing. 2020; 32 (4):1117-1128.
Chicago/Turabian StyleNeeraj Gupta; Saurabh Gupta; Mahdi Khosravy; Nilanjan Dey; Nisheeth Joshi; Rubén González Crespo; Nilesh Patel. 2020. "Economic IoT strategy: the future technology for health monitoring and diagnostic of agriculture vehicles." Journal of Intelligent Manufacturing 32, no. 4: 1117-1128.
Penetration level of solar photovoltaic (PV) energy in the utility network is steadily increasing. This changes the fault level and causes protection problems. Furthermore, multi-tapped structure of distribution network deployed to integrate solar PV energy to the grid and supplying loads at the same time also raised the protection challenges. Hence, this manuscript is aimed at introducing an algorithm to identify and classify the faults incident on the network of utilities where penetration level of the solar PV energy is high. This fault recognition algorithm is implemented in four steps: (1) calculation of Stockwell transform-based fault index (STFI) (2) calculation of Wigner distribution function-based fault index (WDFI) (3) calculation of combined fault index (CFI) by multiplying STFI and WDFI (4) calculation of index for ground fault (IGF) used to recognize the involvement of ground in a fault event. The STFI has the merits that its performance is least affected by the noise associated with the current signals and it is effective in identification of the waveform distortions. The WDFI employs energy density of the current signals for estimation of the faults and takes care of the current magnitude. Hence, CFI has the merit that it considers the current magnitude as well as waveform distortion for recognition of the faults. The classification of faults is achieved using the number of faulty phases. An index for ground fault (IGF) based on currents of zero sequence is proposed to classify the two phase faults with and without the ground engagement. Investigated faults include phase to ground, two phases fault without involving ground, two phases fault involving ground and three phase fault. Fault recognition algorithm is tested for fault recognition with the presence of noise, various angles of fault incidence, different impedances involved during faulty event, hybrid lines consisting of overhead line (OHL) and underground cable (UGC) sections, and location of faults on all nodes of the test grid. Fault recognition algorithm is also tested to discriminate the transients due to switching operations of feeders, loads and capacitor banks from the faulty transients. Performance of the fault recognition algorithm is compared with the algorithms based on discrete wavelet transform (DWT), Stockwell transform (ST) and hybrid combination of alienation coefficient and Wigner distribution function (WDF). Effectiveness of the fault recognition algorithm is established using a detailed study on the IEEE-13 nodes test feeder modified to incorporate solar PV plant of capacity 1 MW in MATLAB/Simulink. Algorithm is also validated on practical utility grid of Rajasthan State of India.
Atul Kulshrestha; Om Prakash Mahela; Mukesh Kumar Gupta; Neeraj Gupta; Nilesh Patel; Tomonobu Senjyu; Mir Sayed Shah Danish; Mahdi Khosravy. A Hybrid Fault Recognition Algorithm Using Stockwell Transform and Wigner Distribution Function for Power System Network with Solar Energy Penetration. Energies 2020, 13, 3519 .
AMA StyleAtul Kulshrestha, Om Prakash Mahela, Mukesh Kumar Gupta, Neeraj Gupta, Nilesh Patel, Tomonobu Senjyu, Mir Sayed Shah Danish, Mahdi Khosravy. A Hybrid Fault Recognition Algorithm Using Stockwell Transform and Wigner Distribution Function for Power System Network with Solar Energy Penetration. Energies. 2020; 13 (14):3519.
Chicago/Turabian StyleAtul Kulshrestha; Om Prakash Mahela; Mukesh Kumar Gupta; Neeraj Gupta; Nilesh Patel; Tomonobu Senjyu; Mir Sayed Shah Danish; Mahdi Khosravy. 2020. "A Hybrid Fault Recognition Algorithm Using Stockwell Transform and Wigner Distribution Function for Power System Network with Solar Energy Penetration." Energies 13, no. 14: 3519.
In the era of Internet of things (IoT), network Connection of an enormous number of agriculture machines and service centers is an expectation. However, it will be with a generation of massive volume of data, thus overwhelming the network traffic and storage system especially when manufacturers give maintenance service typically by various data analytic applications on the cloud. The situation is more complex in the context of low latency applications such as health monitoring of agriculture machines, although require emergency responses. Performing the computational intelligence on edge devices is one of the best approaches in developing green communications and managing the blast of network traffic. Due to the increasing usage of smartphone applications, the edge computation on the smartphone can highly assist the network traffic management. In connection with the mentioned point, in the context of exploiting the limited computation power of smartphones, the design of an AI-based data analytic technique is a challenging task. On the other hand, the users’ need for economic technology makes it not to be easily pierced. This research work aims both targets by presenting a bi-level genetic algorithm approach of an optimized data analytic AI technique for monitoring the health of the agriculture vehicles which can be economically utilized on smartphone end-devices using the built-in microphones instead of expensive IoT sensors.
Neeraj Gupta; Mahdi Khosravy; Nilesh Patel; Nilanjan Dey; Saurabh Gupta; Hemant Darbari; Rubén González Crespo. Economic data analytic AI technique on IoT edge devices for health monitoring of agriculture machines. Applied Intelligence 2020, 50, 1 -27.
AMA StyleNeeraj Gupta, Mahdi Khosravy, Nilesh Patel, Nilanjan Dey, Saurabh Gupta, Hemant Darbari, Rubén González Crespo. Economic data analytic AI technique on IoT edge devices for health monitoring of agriculture machines. Applied Intelligence. 2020; 50 (11):1-27.
Chicago/Turabian StyleNeeraj Gupta; Mahdi Khosravy; Nilesh Patel; Nilanjan Dey; Saurabh Gupta; Hemant Darbari; Rubén González Crespo. 2020. "Economic data analytic AI technique on IoT edge devices for health monitoring of agriculture machines." Applied Intelligence 50, no. 11: 1-27.
This paper focuses on developing a computationally economic lightweight artificial intelligence (AI) technology for smartphones. Until date, no commercial system is available on this technology. Thus the developed breakthrough technology can enhance the capability of users on the field for monitoring the agricultural vehicles (AgV)s health by analyzing the acoustic noise using smartphone‘s app. This paper can enable the user of AgVs to optimize their farming by management at edge devices: smartphones. Since smartphones use a small integrated computing unit with computational limited resources, thus lighter the system, more favorable to work on. Artificial neural network (ANN) is one of the most favorite AI techniques, but its lightweight architecture—attributed by the number of inputs and hidden layers and neurons—, is one of the most important issues in the context of smartphones. Under the framework of bi-level optimization, we aim to analyze the tournament selection operator based genetic algorithm with hybrid crossover operators, at level-I, to evolve ANN, at level-II to design the lightweight edge device enabled AI technique. The obtained results and numerical evaluative analysis indicate that the optimized design of the lightweight fault detection system responds well to the soundtracks received via microphones. We present the evaluation of the proposed technology on serial programming, parallel programming (PP), GPU programming, and GPU with PP. The results show that PP is enough efficient for the proposed technology and can save the cost of GPU for large scale implementation of the technology.
Neeraj Gupta; Mahdi Khosravy; Saurabh Gupta; Nilanjan Dey; Rubén González Crespo. Lightweight Artificial Intelligence Technology for Health Diagnosis of Agriculture Vehicles: Parallel Evolving Artificial Neural Networks by Genetic Algorithm. International Journal of Parallel Programming 2020, 1 -26.
AMA StyleNeeraj Gupta, Mahdi Khosravy, Saurabh Gupta, Nilanjan Dey, Rubén González Crespo. Lightweight Artificial Intelligence Technology for Health Diagnosis of Agriculture Vehicles: Parallel Evolving Artificial Neural Networks by Genetic Algorithm. International Journal of Parallel Programming. 2020; ():1-26.
Chicago/Turabian StyleNeeraj Gupta; Mahdi Khosravy; Saurabh Gupta; Nilanjan Dey; Rubén González Crespo. 2020. "Lightweight Artificial Intelligence Technology for Health Diagnosis of Agriculture Vehicles: Parallel Evolving Artificial Neural Networks by Genetic Algorithm." International Journal of Parallel Programming , no. : 1-26.
In practical operating conditions, the Solar-Photo Voltaic (SPV) system experiences multifarious irradiation and temperature levels, which generate power with multiple peaks. This is considered as the nonuniform operating condition (NUOC). This requires accurate tracking of global power peaks to achieve maximum power from SPV, which is a challenging task. Hence, this paper presents an incremental Conductance based Particle Swarm Optimization (ICPSO) algorithm for accurate tracking of maximum global power from active power multiple peaks generated by the SPV. The proposed algorithm continuously adjusts the individual particle’s weight component, which depends on its distance from the global best position during the tracking process. The proposed algorithm has the merit of continuous adjustment of weight components which reduces active power oscillations at the optimal global position area. Proposed ICPSO algorithm has been successfully designed and implemented for Solar-photo voltaic (PV) under nonuniform operating condition. It is established that the proposed algorithm enhances the output power of the Solar-PV up to 7% with the maximum power tracking of 0.1 s compared to other maximum power point tracking algorithms.
Gajendra Singh Chawda; Om Prakash Mahela; Neeraj Gupta; Mahdi Khosravy; Tomonobu Senjyu. Incremental Conductance Based Particle Swarm Optimization Algorithm for Global Maximum Power Tracking of Solar-PV under Nonuniform Operating Conditions. Applied Sciences 2020, 10, 4575 .
AMA StyleGajendra Singh Chawda, Om Prakash Mahela, Neeraj Gupta, Mahdi Khosravy, Tomonobu Senjyu. Incremental Conductance Based Particle Swarm Optimization Algorithm for Global Maximum Power Tracking of Solar-PV under Nonuniform Operating Conditions. Applied Sciences. 2020; 10 (13):4575.
Chicago/Turabian StyleGajendra Singh Chawda; Om Prakash Mahela; Neeraj Gupta; Mahdi Khosravy; Tomonobu Senjyu. 2020. "Incremental Conductance Based Particle Swarm Optimization Algorithm for Global Maximum Power Tracking of Solar-PV under Nonuniform Operating Conditions." Applied Sciences 10, no. 13: 4575.
By the increasing growth of the Internet of Things (IoT) which provides interconnection and communications between electronic devices and corresponding sensors, a large volume of data is exchanged by multi-input multi-output (MIMO) telecommunication systems. In the case of IoT, reducing the data volume by removing the data redundancy results in more green communication by less power consumption for data transmission and less required storage memory. An approach to avoid the pilot data thus having less redundant data is using blind techniques. This research work presents an improvement to Stone’s blind source separation (BSS) precision, robustness, and computation load and its application to blind MIMO IoT interference channel estimation, multi-nodes IoT data detection, separation and identification in a MIMO-OFDM IoT network. Stone’s BSS is based on complexity conjecture indicating the independent sources have higher predictability than the mixtures. The presented improvement to Stone’s BSS is by a probabilistic modification to the short-term predictability merit by acquiring the prediction coefficients proportional to probability weights which follow a super Gaussian distribution assumption for sources. The probabilistic modification to Stone’s BSS (P-Stone) makes it maximally compatible with a pre-specified probability distribution model, and thereof the signal recovery is not only due to predictability maximization, but it is also inherent to increasing non-gaussianity which results in more independent recoveries, and less dependent on serial dependency of sources. Despite the Stone’s BSS, the proposed merit function does not need any long-term predictor; thus, it achieves around fifty times lower complexity load using just short-term predictors. The superiority of P-Stone to Stone BSS, AMUSE, and SOBI as well-known second-order techniques has been statistically evaluated and clarified through the experiments over MIMO-IoT networks of different combinations. As well, the comparative analysis over multimedia mixtures of music, speech, and images demonstrates its efficiency dominance.
Mahdi Khosravy; Neeraj Gupta; Nilesh Patel; Nilanjan Dey; Naoko Nitta; Noboru Babaguchi. Probabilistic Stone’s Blind Source Separation with application to channel estimation and multi-node identification in MIMO IoT green communication and multimedia systems. Computer Communications 2020, 157, 423 -433.
AMA StyleMahdi Khosravy, Neeraj Gupta, Nilesh Patel, Nilanjan Dey, Naoko Nitta, Noboru Babaguchi. Probabilistic Stone’s Blind Source Separation with application to channel estimation and multi-node identification in MIMO IoT green communication and multimedia systems. Computer Communications. 2020; 157 ():423-433.
Chicago/Turabian StyleMahdi Khosravy; Neeraj Gupta; Nilesh Patel; Nilanjan Dey; Naoko Nitta; Noboru Babaguchi. 2020. "Probabilistic Stone’s Blind Source Separation with application to channel estimation and multi-node identification in MIMO IoT green communication and multimedia systems." Computer Communications 157, no. : 423-433.
This study presented a new multi-species binary coded algorithm, Mendelian Evolutionary Theory Optimization (METO), inspired by the plant genetics. This framework mainly consists of three concepts: First, the “denaturation” of DNA’s of two different species to produce the hybrid “offspring DNA”. Second , the Mendelian evolutionary theory of genetic inheritance, which explains how the dominant and recessive traits appear in two successive generations. Third, the Epimuation, through which organism resist for natural mutation. The above concepts are reconfigured in order to design the binary meta-heuristic evolutionary search technique. Based on this framework, four evolutionary operators – 1) Flipper, 2) Pollination, 3) Breeding, and 4) Epimutation – are created in the binary domain. In this paper, METO is compared with well-known evolutionary and swarm optimizers 1) Binary Hybrid GA (BHGA), 2) Bio-geography Based Optimization (BBO), 3) Invasive Weed Optimization (IWO), 4) Shuffled Frog Leap Algorithm (SFLA), 5) Teaching-Learning Based Optimization (TLBO), 6) Cuckoo Search (CS), 7) Bat Algorithm (BA), 8) Gravitational Search Algorithm (GSA), 9) Covariance Matrix Adaptation Evolution Strategy(CMAES), 10) Differential Evolution (DE), 11) Firefly Algorithm (FA) and 12) Social Learning PSO (SLPSO). This comparison is evaluated on 30 and 100 variables benchmark test functions, including noisy, rotated, and hybrid composite functions. Kruskal Wallis statistical rank-based non-parametric H-test is utilized to determine the statistically significant differences between the output distributions of the optimizer, which are the result of the 100 independent runs. The statistical analysis shows that METO is a significantly better algorithm for complex and multi-modal problems with many local extremes.
Neeraj Gupta; Mahdi Khosravy; Nilesh Patel; Nilanjan Dey; Om Prakash Mahela. Mendelian Evolutionary Theory Optimization Algorithm. 2020, 1 .
AMA StyleNeeraj Gupta, Mahdi Khosravy, Nilesh Patel, Nilanjan Dey, Om Prakash Mahela. Mendelian Evolutionary Theory Optimization Algorithm. . 2020; ():1.
Chicago/Turabian StyleNeeraj Gupta; Mahdi Khosravy; Nilesh Patel; Nilanjan Dey; Om Prakash Mahela. 2020. "Mendelian Evolutionary Theory Optimization Algorithm." , no. : 1.
This study presented a new multi-species binary coded algorithm, Mendelian Evolutionary Theory Optimization (METO), inspired by the plant genetics. This framework mainly consists of three concepts: First, the “denaturation” of DNA’s of two different species to produce the hybrid “offspring DNA”. Second , the Mendelian evolutionary theory of genetic inheritance, which explains how the dominant and recessive traits appear in two successive generations. Third, the Epimuation, through which organism resist for natural mutation. The above concepts are reconfigured in order to design the binary meta-heuristic evolutionary search technique. Based on this framework, four evolutionary operators – 1) Flipper, 2) Pollination, 3) Breeding, and 4) Epimutation – are created in the binary domain. In this paper, METO is compared with well-known evolutionary and swarm optimizers 1) Binary Hybrid GA (BHGA), 2) Bio-geography Based Optimization (BBO), 3) Invasive Weed Optimization (IWO), 4) Shuffled Frog Leap Algorithm (SFLA), 5) Teaching-Learning Based Optimization (TLBO), 6) Cuckoo Search (CS), 7) Bat Algorithm (BA), 8) Gravitational Search Algorithm (GSA), 9) Covariance Matrix Adaptation Evolution Strategy(CMAES), 10) Differential Evolution (DE), 11) Firefly Algorithm (FA) and 12) Social Learning PSO (SLPSO). This comparison is evaluated on 30 and 100 variables benchmark test functions, including noisy, rotated, and hybrid composite functions. Kruskal Wallis statistical rank-based non-parametric H-test is utilized to determine the statistically significant differences between the output distributions of the optimizer, which are the result of the 100 independent runs. The statistical analysis shows that METO is a significantly better algorithm for complex and multi-modal problems with many local extremes.
Neeraj Gupta; Mahdi Khosravy; Nilesh Patel; Nilanjan Dey; Om Prakash Mahela. Mendelian Evolutionary Theory Optimization Algorithm. 2020, 1 .
AMA StyleNeeraj Gupta, Mahdi Khosravy, Nilesh Patel, Nilanjan Dey, Om Prakash Mahela. Mendelian Evolutionary Theory Optimization Algorithm. . 2020; ():1.
Chicago/Turabian StyleNeeraj Gupta; Mahdi Khosravy; Nilesh Patel; Nilanjan Dey; Om Prakash Mahela. 2020. "Mendelian Evolutionary Theory Optimization Algorithm." , no. : 1.
This chapter illustrates the characteristics of plant genetics-inspired evolutionary optimization (PGEO). The computation strategy of PGEO is inspired by the theory of Mendelian evolution. Presented PGEO optimizer is a binary-coded algorithm based on mainly three concepts from plant genetics: (i) the “denaturation” of DNA of two different species to produce the hybrid “offspring DNA,” (ii) the Mendelian evolutionary theory of genetic inheritance, which explains how the dominant and recessive traits appear in two successive generations, (iii) the epimutation, through which organism resists for natural mutation. The above concepts are reconfigured in order to design the binary meta-heuristic evolutionary training technique. Based on this framework, four evolutionary operators—(1) flipper, (2) pollination, (3) breeding, and (4) epimutation—are created in the binary domain. The chapter gives characteristics and a detailed tutorial to PGEO theory.
Neeraj Gupta; Mahdi Khosravy; Nilesh Patel; Om Prakash Mahela; Gazal Varshney. Plant Genetics-Inspired Evolutionary Optimization: A Descriptive Tutorial. Springer Tracts in Nature-Inspired Computing 2020, 53 -77.
AMA StyleNeeraj Gupta, Mahdi Khosravy, Nilesh Patel, Om Prakash Mahela, Gazal Varshney. Plant Genetics-Inspired Evolutionary Optimization: A Descriptive Tutorial. Springer Tracts in Nature-Inspired Computing. 2020; ():53-77.
Chicago/Turabian StyleNeeraj Gupta; Mahdi Khosravy; Nilesh Patel; Om Prakash Mahela; Gazal Varshney. 2020. "Plant Genetics-Inspired Evolutionary Optimization: A Descriptive Tutorial." Springer Tracts in Nature-Inspired Computing , no. : 53-77.
A very recent meta-heuristic optimizer is by inspiration from plant biology where the Mendel law of heredity is implemented through multi-species in two generations. Plant biology-inspired optimizer named as Mendelian Evolutionary Optimization Algorithm (METO), which has several advantages outperforming the state-of-the-art optimizers. It is highly capable of finding the best solution for multimodal problems with global optimal solution and computationally fast. METO not only performs well over the problems with around thirty variables but also performs well on the very high-dimensional problems such as hundred variables. Besides the literature introducing the characteristics of the METO, this chapter investigates the way METO explores the search space of the problem by exchanging the gene’s information between the multi-species. Each plan in a species represents by double strands DNA. Here, we will observe how METO covers the search space and avoid being stuck in a local minimum and moves toward the global solution. In this chapter, we investigate the behavior of the operators of METO such as flipper, pollination, self- and cross-breeding, and epimutation.
Mahdi Khosravy; Neeraj Gupta; Nilesh Patel; Om Prakash Mahela; Gazal Varshney. Tracing the Points in Search Space in Plant Biology Genetics Algorithm Optimization. Springer Tracts in Nature-Inspired Computing 2020, 180 -195.
AMA StyleMahdi Khosravy, Neeraj Gupta, Nilesh Patel, Om Prakash Mahela, Gazal Varshney. Tracing the Points in Search Space in Plant Biology Genetics Algorithm Optimization. Springer Tracts in Nature-Inspired Computing. 2020; ():180-195.
Chicago/Turabian StyleMahdi Khosravy; Neeraj Gupta; Nilesh Patel; Om Prakash Mahela; Gazal Varshney. 2020. "Tracing the Points in Search Space in Plant Biology Genetics Algorithm Optimization." Springer Tracts in Nature-Inspired Computing , no. : 180-195.
As a great computational intelligence technique, artificial neural networks (ANNs) have intensively attracted the interest of researchers of artificial intelligence. Due to the easy implementation of ANN, vast types of structures and associated rules, their successful application can be seen in real-life and industrial problems. From a wide variety of ANN such as feed-forward ANN, Kohonen self-organizing ANN, radial basis function (RBF) ANN, and spiking ANN, we describe a multi-layer perceptron ANN with the focus on designing AI-based condition monitoring system. This chapter presents the neuroevolution-based monitoring system to detect the oil filter condition in agricultural (Ag) machines using meta-heuristic METO algorithm. Evolutionary learning algorithm finds the optimal weights of ANN along with the behavior of each neuron.
Neeraj Gupta; Mahdi Khosravy; Nilesh Patel; Saurabh Gupta; Gazal Varshney. Artificial Neural Network Trained by Plant Genetic-Inspired Optimizer. Springer Tracts in Nature-Inspired Computing 2020, 266 -280.
AMA StyleNeeraj Gupta, Mahdi Khosravy, Nilesh Patel, Saurabh Gupta, Gazal Varshney. Artificial Neural Network Trained by Plant Genetic-Inspired Optimizer. Springer Tracts in Nature-Inspired Computing. 2020; ():266-280.
Chicago/Turabian StyleNeeraj Gupta; Mahdi Khosravy; Nilesh Patel; Saurabh Gupta; Gazal Varshney. 2020. "Artificial Neural Network Trained by Plant Genetic-Inspired Optimizer." Springer Tracts in Nature-Inspired Computing , no. : 266-280.
Artificial neural networks (ANN) have a great impact on research in the field of artificial intelligence. It has great capability besides the easy implementation, and due to that, it has been widely used in a wide area of real-life and industrial applications. Today, we can see a variety of ANNs such as feed-forward ANN, Kohonen self-organizing ANN, radial basis function (RBF) ANN, spiking ANN, etc. This chapter focuses on evolutionary ANN wherein the learning process is by nature-inspired optimization techniques instead of the classic routine. The focus of this chapter is the neuro-evolution-based ANN techniques by different state-of-the-art nature-inspired meta-heuristic optimization techniques and comparison of them over a monitoring system to detect the oil filter condition in agricultural machines (Ag machines). In this comparative study, the fourteen state-of-art meta-heuristic optimizers are compared in the same regard.
Neeraj Gupta; Mahdi Khosravy; Nilesh Patel; Saurabh Gupta; Gazal Varshney. Evolutionary Artificial Neural Networks: Comparative Study on State-of-the-Art Optimizers. Springer Tracts in Nature-Inspired Computing 2020, 302 -318.
AMA StyleNeeraj Gupta, Mahdi Khosravy, Nilesh Patel, Saurabh Gupta, Gazal Varshney. Evolutionary Artificial Neural Networks: Comparative Study on State-of-the-Art Optimizers. Springer Tracts in Nature-Inspired Computing. 2020; ():302-318.
Chicago/Turabian StyleNeeraj Gupta; Mahdi Khosravy; Nilesh Patel; Saurabh Gupta; Gazal Varshney. 2020. "Evolutionary Artificial Neural Networks: Comparative Study on State-of-the-Art Optimizers." Springer Tracts in Nature-Inspired Computing , no. : 302-318.
Within the framework of this study, the inductive analysis of voltage stability indices’ theoretical formulation, functionality, and overall performances are introduced. The prominence is given to investigate and compare the original indices from three main dimensions (formulation, assessment, and application) standpoints, which have been frequently used and recently attracted. The generalizability of an exhaustive investigation on comparison of voltage stability indices seems problematic due to the multiplicity of the indices, and more importantly, their variety in theoretical foundation and performances. This study purports the first-ever framework for voltage stability indices classification for power system analysis. The test results found that indices in the same category are coherent to their theoretical foundation. The paper highlights the fact that each category of the indices is functional for a particular application irrespective of the drawback ranking, and negated the application of the Jacobian matrix-based indices for online application. Finally, the research efforts put forward a novel classification of voltage stability indices within the main three aspects of formulation, assessment, and behavior analysis in a synergistic manner as an exhaustive reference for students, researchers, scholars, and practitioners related to voltage stability analysis. The simulation tools used were MATLAB® and PowerWorld®.
Hameedullah Zaheb; Mir Sayed Shah Danish; Tomonobu Senjyu; Mikaeel Ahmadi; Abdul Malik Nazari; Mohebullah Wali; Mahdi Khosravy; Paras Mandal. A Contemporary Novel Classification of Voltage Stability Indices. Applied Sciences 2020, 10, 1639 .
AMA StyleHameedullah Zaheb, Mir Sayed Shah Danish, Tomonobu Senjyu, Mikaeel Ahmadi, Abdul Malik Nazari, Mohebullah Wali, Mahdi Khosravy, Paras Mandal. A Contemporary Novel Classification of Voltage Stability Indices. Applied Sciences. 2020; 10 (5):1639.
Chicago/Turabian StyleHameedullah Zaheb; Mir Sayed Shah Danish; Tomonobu Senjyu; Mikaeel Ahmadi; Abdul Malik Nazari; Mohebullah Wali; Mahdi Khosravy; Paras Mandal. 2020. "A Contemporary Novel Classification of Voltage Stability Indices." Applied Sciences 10, no. 5: 1639.