Nowadays, different schemes and ways are proposed to meet the user's load requirement of energy towards the Demand Side (DS) in order to encapsulate the energy resources. However, this Load Demand (LD) increases d...
详细信息
ISBN:
(纸本)9781538621950
Nowadays, different schemes and ways are proposed to meet the user's load requirement of energy towards the Demand Side (DS) in order to encapsulate the energy resources. However, this Load Demand (LD) increases day by day. This increase in LD is causing serious energy crises to the utility and DS. As the usage of energy increases with the increase in user's demand respectively, the peak is increased in these hours which affect the customer's in term of high-cost prices. This issue is tackled using some schemes and their proper integration. Two-way communication is done by the utility through Smart Grid (SG) between utility and customers. Customers that show some good behavior and helps the utility to control this LD, can perform a key role here. In this paper, our main focus is to control the Customer Side Management (CSM) by reducing the peak generation from on-peak hours. In our scenario, we focus on saving the cost expenditure of users by giving them comfort and shifting the load of appliances from high LD hours to low LD hours. In this study, we adopt the optimizationalgorithms, like bacterial foraging optimization algorithm (BFOA), Flower Pollination algorithm (FPA) and proposed our Hybrid bacterial Flower Pollination algorithm (HBFPA) to optimize the solution of our problem using the famous electricity scheme named as Critical Peak Pricing(CPP) with three different Operational Time intervals (OTIs). Simulations and results show that our scheme reduces the cost and peak to the average ratio by proper shifting the appliances from highly load demanding hours to the low demanding hours with the negligibly small difference between the maximum and minimum 90% of confidence interval.
Design engineers play a vital role in conceptualizing and designing an assembly. The assembly is then disintegrated into several subassemblies and geometric tolerance symbols and values are allocated. Often, the alloc...
详细信息
Design engineers play a vital role in conceptualizing and designing an assembly. The assembly is then disintegrated into several subassemblies and geometric tolerance symbols and values are allocated. Often, the allocated symbols and values are not realistic and conflict occurs as a result of improper assembly function, difficulty in manufacturing, and increased time and production costs. This article proposes a new design methodology. First, the functional behaviour of the assembly is predicted through finite element analysis, and the deformed geometry is expressed as a geometric tolerance zone constraint. Then, the functional assembly requirement is mathematically defined as a fit constraint. Simultaneously, rotational and planar machining constraints are developed. Finally, a combinatorial optimization problem is formulated to minimize the manufacturing cost. For optimal trade-offs, the bacterialforagingalgorithm is applied to solve the combinatorial optimization problem;when it is applied to a mounted disc brake assembly, promising results are obtained.
Cellular manufacturing has the potential to reduce the complexity of a manufacturing system, decoupling a complex factory in mini-factories or cells. This concept has an immense potential to be implemented in remanufa...
详细信息
Cellular manufacturing has the potential to reduce the complexity of a manufacturing system, decoupling a complex factory in mini-factories or cells. This concept has an immense potential to be implemented in remanufacturing, which is a promising circular economy strategy which allows to recover the value added to used products. A remanufacturing system is complex, and this creates a barrier to its implementation. This study introduces an ant-based algorithm which addresses the formation of product/part families and remanufacturing cells. A novel approach used in this study mimics ant behaviour by using an artificial pheromone that reinforces similarities between machines and parts, allowing the definition of cells and families with superior performance over Genetic algorithms (GA) and bacterial foraging optimization algorithm (BFOA) in the solution of benchmark problems. Copyright (C) 2022 The Authors.
The function of the back, hip, knee, ankle and other orthopedic alterations of the human body can be analyzed through plantar pressure distribution. The development of Clinical Decision Support Systems (CDSS) can hand...
详细信息
The function of the back, hip, knee, ankle and other orthopedic alterations of the human body can be analyzed through plantar pressure distribution. The development of Clinical Decision Support Systems (CDSS) can handle the uncertainties present in biological data using different Artificial Intelligence techniques to obtain accurate and easy-to-use systems. This paper presents the application of a Fuzzy Cognitive Map (FCM) formulation, for knowledge extraction in the classification of human plantar foot alterations, with a relatively small and transparent model. The FCM is trained using the bacterial Search optimizationalgorithm (BFOA). One hundred and twenty-five volunteer subjects (aged 20-68 years) participated in the study. Classification of the foot into normal (n=31), flat (n=32), cavus type III (n=31) and cavus type IV (n=31) to train the system was performed by specialized physicians. The test was performed by walking on a FreeMed platform. The proposed method shows an accuracy rate of about 89% in the classification task and allows extracting information related to the important factors that the system considers to make a decision.
The closed loop configuration of a RO control system is depicted. The controller FOPID takes the input and calculates the frequency input of the final control element. The final control element is variable frequency d...
详细信息
The closed loop configuration of a RO control system is depicted. The controller FOPID takes the input and calculates the frequency input of the final control element. The final control element is variable frequency drive whose flow rate changes with the change in frequency. When the flow rate changes pH and the turbidity of the RO process changes which is measured using the feedback mechanism in this case the pH meter and the turbidity sensors are the feedback mechanisms.
Non-orthogonal multiple access (NOMA) is a strong contender multicarrier waveform technique for the fifth generation (5G) communication system. The high peak-to-average power ratio (PAPR) is a serious concern in desig...
详细信息
Non-orthogonal multiple access (NOMA) is a strong contender multicarrier waveform technique for the fifth generation (5G) communication system. The high peak-to-average power ratio (PAPR) is a serious concern in designing the NOMA waveform. However, the arrangement of NOMA is different from the orthogonal frequency division multiplexing. Thus, traditional reduction methods cannot be applied to NOMA. A partial transmission sequence (PTS) is commonly utilized to minimize the PAPR of the transmitting NOMA symbol. The choice phase aspect in the PTS is the only non-linear optimization obstacle that creates a huge computational complication due to the respective non-carrying sub-blocks in the unitary NOMA symbol. In this study, an efficient phase factor is proposed by presenting a novel bacterial foraging optimization algorithm (BFOA) for PTS (BFOA-PTS). The PAPR minimization is accomplished in a two-stage process. In the initial stage, PTS is applied to the NOMA signal, resulting in the partition of the NOMA signal into an act of sub-blocks. In the second stage, the best phase factor is generated using BFOA. The performance of the proposed BFOA-PTS is thoroughly investigated and compared to the traditional PTS. The simulation outcomes reveal that the BFOA-PTS efficiently optimizes the PAPR performance with inconsequential complexity. The proposed method can significantly offer a gain of 4.1 dB and low complexity compared with the traditional OFDM.
Proficient clustering method has a vital role in organizing sensor nodes in wireless sensor networks (WSNs), utilizing their energy resources efficiently and providing longevity to network. Hybrid energy-efficient dis...
详细信息
Proficient clustering method has a vital role in organizing sensor nodes in wireless sensor networks (WSNs), utilizing their energy resources efficiently and providing longevity to network. Hybrid energy-efficient distributed (HEED) protocol is one of the prominent clustering protocol in WSNs. However, it has few shortcomings, i.e., cluster heads (CHs) variation in consecutive rounds, more work load on CHs, uneven energy dissipation by sensor nodes, and formation of hot spots in network. By resolving these issues, one can enhance HEED capabilities to a greater extent. We have designed variants of Optimized HEED (OHEED) protocols named as HEED-1 Tier chaining (HEED1TC), HEED-2 Tier chaining (HEED2TC), ICHB-based OHEED-1 Tier chaining (ICOH1TC), ICHB-based OHEED-2 Tier chaining (ICOH2TC), ICHB-FL-based OHEED-1 Tier chaining (ICFLOH1TC), and ICHB-FL-based OHEED-2 Tier chaining (ICFLOH2TC) protocols. In HEED1TC and HEED2TC protocols, we have used chain-based intra-cluster and inter-cluster communication in HEED, respectively, for even load balancing among sensor nodes and to avoid more work load on CHs. Furthermore, for appropriate cluster formation, minimizing CHs variation in consecutive rounds and reducing complex uncertainties, we have used bacterial foraging optimization algorithm (BFOA)-inspired proposed intelligent CH selection based on BFOA (ICHB) algorithm for CH selection in ICOH1TC and ICOH2TC protocols. Likewise, in ICFLOH1TC and ICFLOH2TC protocols, we have used novel fuzzy set of rules additionally for CH selection to resolve the hot spots problem, proper CH selection covering whole network, and maximizing the network lifetime to a great extent. The simulation results showed that proposed OHEED protocols are able to handle above-discussed issues and provided far better results in comparison to HEED.
Aiming at the low power level of the two-level Z-source inverter, the current and voltage harmonic distortion rate is high, the output power quality is low, The diode Neutral Point Clamp (NPC) three-level Z source inv...
详细信息
Aiming at the low power level of the two-level Z-source inverter, the current and voltage harmonic distortion rate is high, the output power quality is low, The diode Neutral Point Clamp (NPC) three-level Z source inverter has insufficient boost capacity, and the capacitor voltage stress is low, the Z source network of the three-level inverter is improved and applied to photovoltaic grid-connected. In order to speed up the dynamic response speed of the quasi-Z-source photovoltaic grid-connected system, and for the quasi-Z-source photovoltaic grid-connected system with the existing current inner loop control output harmonic distortion rate is high, and the steady-state error is large. the power feedforward bacterialforaging proportional complex integral (BFOA-PCI) control method is put foreward. The system about this method reduces the steady-state error through the PCI controller, and adds the bacterialforagingoptimization proportional coefficient K-P and integral coefficient K-i, makes K-P and K-i any the optimal conditions can be achieved, and introduces the output power feedforward of the photovoltaic array in the voltage outer loop, and its output is used as the reference value of the current inner loop. The method speeds up the system's response changes to the external environment, reduces the harmonic distortion rate, improves steady-state accuracy and the output power quality.
The electrical demand and generation in power systems is currently the biggest source of uncertainty for an electricity provider. For a dependable and financially advantageous electricity system, demand response (DR) ...
详细信息
The electrical demand and generation in power systems is currently the biggest source of uncertainty for an electricity provider. For a dependable and financially advantageous electricity system, demand response (DR) success as a result of household appliance energy management has attracted significant attention. Due to fluctuating electricity rates and usage trends, determining the best schedule for apartment appliances can be difficult. As a result of this context, the Improved Cockroach Swarm optimizationalgorithm (ICSOA) is combined with the Innovative Apartments Appliance Scheduling (IAAS) framework. Using the proposed technique, the cost of electricity reduction, user comfort maximization, and peak-to-average ratio reduction are analyzed for apartment appliances. The proposed framework is evaluated by comparing it with BFOA and W/O scheduling cases. In comparison to the W/O scheduling case, the BFOA method lowered energy costs by 17.75%, but the ICSA approach reduced energy cost by 46.085%. According to the results, the created ICSA algorithm performed better than the BFOA and W/O scheduling situations in terms of the stated objectives and was advantageous to both utilities and consumers.
A variety of clusters are grouped together to form a multi-cluster environment which can tackle the computational needs of a system which cannot be addressed by a single cluster. Studying multi-cluster frameworks is t...
详细信息
ISBN:
(纸本)9789811501081;9789811501074
A variety of clusters are grouped together to form a multi-cluster environment which can tackle the computational needs of a system which cannot be addressed by a single cluster. Studying multi-cluster frameworks is turning challenging day by day as it requires contemporary tools to move alongside with rapidly development and enhanced complexity of one system. Job scheduling in considered as NP hard problem in parallel and distributed computing environments such as cluster, grid and clouds. The way jobs are scheduled by the scheduler is dependent on various factors like number of jobs, processor availability, arrival time etc. Metaheuristics techniques like Genetic algorithms, Ant Colony optimization, Artificial Bee Colony, Cuckoo Search, Firefly algorithm, Bat algorithm etc. are used by researchers to get near optimal solutions to job scheduling problems. This work addresses a scheduling problem with multiple objectives. The makespan and flowtime are minimized simultaneously solving the issue of optimal job allocation. This work also includes the detailed description of parallel computing and various types scheduling as well as scheduling environments. The performance of the multi-cluster environment is optimized by applying a novel meta-heuristic technique named bacterial foraging optimization algorithm. This algorithm has better convergence and is not affected by the size of problem. The proposed algorithm was evaluated for different job sets on 3 types of processor configurations. And the final values were compared to those of the existing algorithm. The results show that the proposed algorithm has performed better than the existing one and it can be concluded that the proposed algorithm is feasible and effective for optimal allocation of jobs.
暂无评论