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Highly secured EHR management system based on blockchain technology with digitally signed authentication using data sanitization and polynomial interpolation

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2024年第PartB期87卷

作者： Lakshmanan, M. Mala, G. S. Anandha Anandkumar, K. M. Sri Venkateswara Coll Engn Comp Sci & Engn Sriperumbudur India Easwari Engn Coll Comp Sci & Engn Chennai India

With the increased specifications of healthcare services, diagnosis become very common in most of the healthcare sector to provide better treatment for patients. Here, the person's medical information can be tracked with the help of an Electronic Health Record (EHR). The EHR system maintains the digital medical records of medical organizations. Enterprise-wide data systems, along with other networking activities, are used to exchange medical documents in the current environment where the patient seeks rapid access to their medical records. Moreover, the EHR system faces many problems regarding integrity, management and security. Still, there are some concerns regarding the security and privacy of patient medical records. However, it suffers from managing the large volume of electronically generated healthcare data. Thus, this work adapts blockchain technology, which effectively stores healthcare data. Therefore, decentralized technology was just recently introduced in order to offer an innovative viewpoint on data security and system effectiveness. In this work, a blockchain-related system is developed to secure patients' health data with high verifiability. A total of two mechanisms are carried out in the developed model to encrypt the privacy of the health data that is Data Sanitization and Polynomial Interpolation-based Cryptography (DSPIC). In the data sanitization mechanism, the optimal key is generated through the Modernized Position-based Coot and Penguins Search Optimization Algorithm (MP-CPeSOA). The polynomial interpolation method is carried out through the estimation of values between two data points to handle the heterogeneity. The objective function is considered for securing the patient's health data with Euclidian distance, hiding ratio, preservation ratio and correlation among the original data and the restored data. In this developed model, the generated key is digitally signed to provide high security for the patient's health data. At last, the eff

关键词： Electronic health record management system multi-objective function Blockchain technology Modernized position-based coot and penguins search optimization algorithm Data sanitization and polynomial interpolation- based cryptography Optimal key generation

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Performance analysis of solar PV parameter estimation techniques

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OPTIK 2023年 279卷

作者： Rawat, Nikita Thakur, Padmanabh Singh, Asheesh K. Bansal, R. C. G Era Deemed be Univ Dept Elect Engn Dehra Dun 248002 Uttaranchal India Motilal Nehru Natl Inst Technol Allahabad Dept Elect Engn Prayagraj 211004 Uttar Pradesh India Univ Sharjah Coll Engn Dept Elect Engn Sharjah U Arab Emirates Univ Pretoria Dept Elect Elect & Comp Engn Pretoria South Africa

Firstly, this paper investigates the performances of the three different established parameter estimation techniques, namely multi-objective function, five-point metaheuristic, and hybrid-based approaches. Using these three different techniques, unknown model parameters, such as photovoltaic current (I-pv), dark saturation current (I-o), series resistance (R-s), shunt resistance (R-sh), and Ideality factor (A), of mono-and poly-crystalline technology-based solar PV modules, are predicted. Afterwards, using the estimated unknown parameters and P-V & I-V characteristics curves, two different benchmarks, namely absolute relative maximum power error (ARMPE) and overall model error (OME), of these modules, are evaluated to reveal their best performances. In addition, various other parameter estimation techniques, such as iterative, analytical, curve-fitting, and metaheuristics-based methods, have also been considered for performance analysis. It is shown that the hybrid-based approach provides a better characterization than the other various established approaches. The value of %ARMPE is found almost zero with the hybrid approach for both modules. In addition, the smallest values of OME are obtained with the hybrid approach, i.e., 0.002 or 0.003 for poly- and 0.031 for the mono-crystalline technology-based solar PV module. Hence, a hybrid-based parameter estimation technique is suggested to achieve accurate characteristics of solar PV.

关键词： Absolute relative maximum power error Hybrid approach Iterative technique multi-objective function Overall model error Photovoltaic cell

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An efficient MABC-ANN technique for optimal management and system modeling of micro grid

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SUSTAINABLE COMPUTING-INFORMATICS & SYSTEMS 2021年 30卷 100552-100552页

作者： Roy, Kallol Burdwan Univ Univ Inst Technol Burdwan W Bengal India

In this paper, a hybrid technique is proposed for the system modeling and energy management of Microgrid (MG) connected sources. The proposed hybrid technique is the hybridization of Artificial Bees Colony (ABC) with Bat Search Algorithm (BAT) and Artificial Neural Network (ANN). Here, the novelty of the proposed method is that the search space of the scout bees is gained with the utilization of the BAT and the optimal solution is found from this scout bees search space. The performance of the scout bees is generously enhanced by BAT. So the combination of ABC and BAT is called as MABC technique. The proposed algorithm optimizes the configuration of the MG sources combination by considering the multi-objective function, while fulfilling the load demand. The considered MG connected system such as Wind Turbine, Photovoltaic array, Fuel Cell, Micro Turbine, Diesel Generator and battery storage. Here, ANN is utilized for the prediction of the required load demand by using the inputs of MG connected sources. Based on the load demand, MABC technique is utilized to choose an optimal configuration of MG, i.e., fuel cost minimization, emission factors, operating, and maintenance cost. The proposed technique is executed in MATLAB/Simulink platform and compared with existing strategies. In this paper, the cost accuracy percentage (CAP) for proposed and the existing technique is also investigated, the proposed technique achieves 7.37880 %.

关键词： MABC ABC Bat-inspired algorithm Fuel cost Operation cost Maintenance cost MG multi-objective function

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Design of Controller for Automatic Voltage Regulator Using Teaching Learning Based Optimization

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Procedia Technology 2015年 21卷 295-302页

作者： V. Rajinikanth Suresh Chandra Satapathy Department of Electronics and Instrumentation Engineering St. Joseph's College of Engineering Chennai 600119 India Department of Computer Science and Engineering ANITS Visakhapatnam 531162 India.

In this paper, One Degree Of Freedom (1DOF) and Two Degrees Of Freedom (2DOF) Proportional + Integral + Derivative (PID) controller design is proposed and implemented on the Automatic Voltage Regulator (AVR) system using traditional Teaching Learning Based Optimization (TLBO) algorithm. Minimization of a multi-objective function guides the TLBO algorithm's exploration until the process converges with an optimal solution. A simulation study is carried to examine the performance of TLBO assisted controller design procedure for three, four and five dimensional searches. The performance of the proposed method is validated with most successful heuristic procedures, such as Particle Swarm Optimization (PSO), Bacterial Foraging Optimization (BFO) and Firefly Algorithm (FA). The result show that, 1DOF PID controller and PID controller with filter offers smooth reference tracking response and the 2DOF PID controller with the Feed Forward (FF) and Feed Back (FB) structure presents reduced time domain and error values compared to the alternatives.

关键词： AVR PID controller degrees of freedom teaching learning based optimization multi-objective function

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multi-scenario operation optimization model for park integrated energy system based on multi-energy demand response

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SUSTAINABLE CITIES AND SOCIETY 2020年第0期53卷 101973-000页

作者： Tan, Zhongfu Yang, Shenbo Lin, Hongyu De, Gejirifu Ju, Liwei Zhou, Feng'ao North China Elect Power Univ Sch Econ & Management Beijing 102206 Peoples R China

multi-energy demand response is an important means to achieve peak load shifting, and improve energy efficiency. It plays a significant role in promoting the sustainable development of park integrated energy system. In this context, we first constructed a mull-energy demand response model from the perspective of an elastic matrix. Second, we established performance evaluation indexes of the park integrated energy system based on economic and environmental factors, to match energy supply and demand. We then constructed an optimization model considering the unit output, energy balance, and other constraints to achieve optimal net profits, energy utilization efficiency, and degree of matching between load and output. Finally, we established multiple scenarios and used the multi-objective particle swarm optimization algorithm combined with fuzzy set theory for an example analysis. The results show that after redesigning the cooling, heating, and electricity prices, the multi-energy demand response model transfers the user load demand, without changing the total energy consumption. Compared with the traditional scenario, the energy utilization efficiency and net system profit increased by 2.30 % and $2,652.775, respectively, while the user expenditure decreased by $7,663.887, following implementation of the demand response model. This demonstrates win-win situations for the economy/environment and for the system/users.

关键词： Park integrated energy system multi-energy demand response Performance evaluation indexes multi-objective function multi-scenario optimization

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Balancing mixed-model assembly lines with parallel workstations through a genetic algorithm approach

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INTERNATIONAL JOURNAL OF INDUSTRIAL ENGINEERING-THEORY APPLICATIONS AND PRACTICE 2006年第4期13卷 402-411页

作者： Tiacci, Lorenzo Saetta, Stefano Martini, Andrea Univ Perugia Dept Ind Engn I-06125 Perugia Italy

This study presents a genetic algorithm approach to the Assembly Line Balancing Problem (ALBP), that includes the possibility of paralleling (the possibility of utilizing parallel workstations which perform the same set of tasks). The implementation of this feature has required a double chromosome representation of feasible solutions. The objective function considers two components: minimizing design costs and maximizing workload smoothness. The algorithm takes into account other realistic aspects, such as the randomness in task times execution, and the realization of a mix of products. All of this makes it applicable to a large number of real cases. The approach is tested on a set of problems taken from literature;the computation results, obtained via simulation, validate the effectiveness of the algorithm. Significance: Many traditional line balancing approaches may lead to poor solutions when applied in modem production environment. The motivation for this research is to investigate the problem of balancing a production or assembly line in a realistic, modern manufacturing environment.

关键词： line balancing genetic algorithms mixed-model production JIT paralleling multi-objective function

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Development of optimal channel and power allocation through enhanced artificial ecosystem-based optimisation strategy

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JOURNAL OF CONTROL AND DECISION 2024年第2期11卷 257-270页

作者： Babu, T. Sarath Satyanarayana, Penke Rao, S. Nagaraja KL Deemed Univ Dept ECE Guntur 522502 Andhra Pradesh India G Pulla Reddy Engn Coll Dept ECE Kurnool India

Cognitive Radio (CR) is developed to provide effective spectrum usage. CR is much significant in improving the efficiency of the global internet in applications. The evolutionary measurement technology is utilised to improve the evaluation of channel-state information. The outcome attained very few spectrums sensing in CR for complex mobility. A good optimisation method is needed to improve the accurate channel state prediction in successful channel access. Thus, this paper aims to implement a novel power and channel allocation mechanism with the help of a new Modified Levy Flight-based Artificial Ecosystem Optimisation (MLF-AEO) Optimisation Strategy. This paper achieves the optimal power control and channel allocation mechanism intending to solve the multiple objective functions based on the constraints like Interference among users, Outage Probability, and throughput. The superiority of the proposed algorithm is thoroughly verified by various simulation results.

关键词： Cognitive radio network modified levy-based artificial ecosystem optimisation multi-objective function throughput outage probability channel allocation interference power control

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multi-objective-derived energy efficient routing in wireless sensor networks using hybrid African vultures-cuckoo search optimization

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INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS 2023年第6期36卷 e5438-e5438页

作者： Asha, A. Rajesh, A. Verma, Neha Poonguzhali, I Rajalakshmi Engn Coll Dept Elect & Commun Engn Chennai Tamil Nadu India SIMATS Saveetha Sch Engn Dept Elect & Commun Engn Chennai Tamil Nadu India GGSIPU Vivekananda Inst Profess Studies Dept Informat Technol New Delhi India Panimalar Inst Technol Dept Elect & Commun Engn Poonamallee Tamil Nadu India

Wireless Sensor Network (WSN) plays an essential role in consumer electronics, remote monitoring, an electromagnetic signal, and so forth. The functional capacity of WSN gets enhanced everyday with different technologies. The rapid development of wireless communication, as well as digital electronics, provides automatic sensor networks with low cost and power in various functions, but the challenge faced in WSN is to forward a huge amount of data between the nodes, which is a highly complex task to provide superior delay and energy loss. To overcome these issues, the development of a routing protocol is used for the optimal selection of multipath to perform efficient routing in WSN. This paper developed an energy-efficient routing in WSNs utilizing the hybrid meta-heuristic algorithm with the help of Hybrid African Vultures-Cuckoo Search Optimization (HAV-CSO). Here, the designed method is utilized for choosing the optimal cluster heads for progressing the routing. The developed HAV-CSO method is used to enhance the network lifetime in WSN. Hence, the hybrid algorithm also helps select the cluster heads by solving the multi-objective function in terms of distance, intra-cluster distance, delay, inter-cluster distance, throughput, path loss, energy, transmission load, temperature, and fault tolerance. The developed model achieved 7.8% higher than C-SSA, 25.45% better than BSO-MTLBO, 23.21% enhanced than AVOA, and 1.29% improved than CSO. The performance of the suggested model is validated, and the efficacy of the developed work is proved over other existing works.

关键词： cluster head selection energy-efficient routing hybrid African vultures-cuckoo search optimization multi-objective function wireless sensor network

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A genetic based algorithm for loss reduction in distribution systems

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AEJ - Alexandria Engineering Journal 2004年第6期43卷 747-753页

作者： Moussa, A. El-Gammal, M. Abdallah, E.N. Attia, A.I. Elect. Eng. Dept. Faculty of Eng. Alexandria University Alexandria Egypt Research and Energy Conservation Sector Alexandria Elect. Distrib. Co. Alexandria Egypt

The distribution system is considered not only as one of the important parts of the electric power system but one of the most complicated systems created by the mankind. It constitutes the link between electricity utilities and consumers. Usually, it suffers from unbalanced feeder structures and unbalanced loading which affects system power quality and electricity price. This paper introduces a Genetic based Algorithm (G.A) to determine the states of the switches for minimum loss configuration. The proposed G.A is applied for multi-objective programming to solve the reconfiguration problem in distribution systems. The problem of feeder configuration can be looked upon as an optimization problem, where the objective function reflects the different goals that the individual utilities may pursue. A weighed multi-objective function is used in this paper to investigate the utilities interests. The algorithm can be directed to minimize the losses which are a major sign of better power quality. Also, the operator has the ability to direct it to minimize the active power loss, voltage drop, complex power and neutral current of the main transformer. A radial distribution system is used to demonstrate the capability of the proposed G.A. © Faculty of Engineering Alexandria University, Egypt.

关键词： Distribution network Genetic algorithm Loss reduction multi-objective function Reconfiguration

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Analytical study on optimized configuration strategy of electrochemical energy storage system under multiple scenarios

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Applied Mathematics and Nonlinear Sciences 2024年第1期9卷

作者： Li, Tou Zhang, Wei Zhang, Xuan School of Electrical Engineering Nanjing Institute of Technology Jiangsu Nanjing210000 China Nanjing Weilixuan Information Technology Co. Ltd Jiangsu Nanjing210000 China

This paper models the electrochemical energy storage system and proposes a control method for three aspects, such as battery life, to generate a multiobjective function for optimizing the capacity allocation of electrochemical energy storage under multiple scenarios, with conditional constraints on the system, storage, and progression aspects. The improved whale optimization algorithm is used to solve the multi-objective function to find the most reasonable electrochemical energy storage system capacity optimization allocation scheme. Using the model constructed in this paper under multi-scenario conditions, it is found after solving that the optimal allocation scheme purchases power from the grid at around 25MW during the highest peak hours in summer and 5MW in winter, which ensures the economic benefits. Meanwhile, the maximum power fluctuation of the electrochemical energy storage system at point A of the optimization strategy provided by the model is only 2.16%, which is much lower than the preset 4.32%, so the optimal allocation strategy reaches the optimum. Comparing the performance of configured energy storage in different scenarios, the peak-valley power difference of the model proposed in this paper decreases from 11.6 MW to 8.9 MW, which is a better performance than that of the control group, which is 10.8 MW-9.1 MW, and the effect of peak shaving and valley filling is obvious. © 2024 Tou Li, Wei Zhang and Xuan Zhang, published by Sciendo.

关键词： Electrochemical energy storage multi-objective function Optimal allocation Whale optimization algorithm

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