Heuristic Power Research Articles

Heuristic power management of hybrid source electric vehicle based on PV-battery-PEMFC

Renewable energy sources are now being focused on for usage in electric vehicles. Although majority of electric vehicles run solely on batteries, they have eliminated local emissions but not pollution. To tackle this issue, this research looks into the performance and power management aspects of an electric vehicle's hybrid power system, with a particular emphasis on the interactions between fuel cells, batteries, and photovoltaic (PV) cells. Stable operation is ensured via a bi-directional buck-boost converter, which maintains the voltage from all sources around reference 430 V. Initially, the fuel cell supplies electricity under low light conditions. The PV system and battery take over when the irradiance surpasses 100 W/m2, with the battery first draining and then charging in tandem with the increase in irradiance. 1750 RPM is the constant motor speed that is reached after making the required torque adjustments. A model full electric vehicle with multiple sources has been proposed with an efficient power management algorithm. When the battery is in stationary mode and the state of charge (SOC) is less than 100%, extra solar power is used to charge the battery. The model also takes into consideration using the car for entertainment or gadget charging when the ignition is off. The biogas output can be regulated using an inferential control technique using artificial neural network (ANN) based on the fuel demand or consumption. The system also includes anaerobic digestion of organic waste to provide hydrogen for the fuel cell sustainably from biogas. Power, current, SOC profiles, motor performance graphs, and other validations show that the extensive simulations exhibit efficient power management and a strong model for hybrid electric car power systems. The model has been simulated in the MATLAB Simulink environment and has been tested under realistic conditions.

The Realism of Speculative Fiction: Planetary Polyphony and Scale in Kim Stanley Robinson’s The Ministry for the Future

In “Thinking in SF Mode” (2021), Isabelle Stengers distinguishes between philosophical thought experiments (such as Maxwell’s demon [1867] or Searle’s Chinese room) and the ability of speculative fiction to test hypothesizes in fully developed storyworlds, arguing for the heuristic power of fictional density and immersion. We push this hypothesis further by examining what embodied reading brings to the political and ecological exploration of terraforming (geoengineering) in Kim Stanley Robinson’s Mars Trilogy (1992–1996).

An efficient heuristic power analysis framework based on hill-climbing algorithm

Traditional nonprofiling side-channel analysis frequently adopts divide-and-conquer strategy to recover the secret key of a cryptographic algorithm. Only a single key byte is used, whereas the remaining bytes are considered extraneous noise. If the cryptographic algorithm is implemented in hardware with parallel processing, the strategy will lead to the inefficient use of information. The combination of intelligent algorithms and side-channel analysis offers a different idea for nonprofiling methods for parallel hardware implementations. It transforms the problem of key recovery into key optimization. In this paper, we adopt this idea and propose an efficient heuristic framework to assist correlation power analysis. The multipoint hill-climbing algorithm will be used to find the optimal key candidates with higher scores, and the correct key will be found in the optimal key candidates by key enumeration. Multiple bytes of the secret key are used to take full advantage of the helpful information. Besides, two search strategies are introduced to overcome the efficiency problem, and two key enumeration strategies are introduced to solve the key recovery problem when the traces are insufficient. Experimental results on the public DPA Contest V1 dataset show that the heuristic framework performs better than other classical methods, verifying its effectiveness.

Electric vehicle charging design: The factored action based reinforcement learning approach

Charging optimization design for Electric Vehicles (EV) is challenging because it must account for various uncertainties and design aspects such as random EVs’ arrivals and departures, battery degradation, and transformer Loss of Life (LoL). Model-free reinforcement learning (RL) can be employed to tackle such the EV charging design where it does not require to explicitly model the environment dynamics and accurately predict relevant system parameters. However, the high complexity involved in conventional RL-based approaches usually limits its application to only small-scale EV charging settings, which is impractical. To overcome this limitation, we employ the factored action based RL method to transform the formulated Markov Decision Process (MDP). Then, we propose novel reward shaping and hybrid learning methods combining the Convolutional Neural Network (CNN) and Proximal Policy Optimization (PPO) algorithm to extract relevant features from high-dimension state space and efficiently solve the transformed MDP problem. Extensive numerical studies demonstrate that the proposed design can be used to control a charging station (CS) supporting a large number of EVs. Moreover, we show that the proposed framework greatly outperforms other baselines including single-agent and multi-agent RL based strategies and a heuristic power scheduling algorithm in terms of the achieved reward.

Tuning In: Emotions, Relations, and Dynamics in the Analysis of an Archived Interview

ABSTRACT Oral historians have reflected for many years on how to analyze archived interviews. This article contributes to this vital discussion by demonstrating how the combination of different approaches (contextual, narrative, and auditory) enhances a secondary analysis. We base our argumentation on one specific example: a passionate conversation between an elderly man, who had lived through the Nazi period in Germany as a communist, and a much younger historian. After a long and mostly harmonious interview (lasting seven and a half hours), the interviewee broke off the conversation. Our focus therefore lies on exploring the development of the communicative relationship between interviewer and interviewee. To do this, we first locate the interview within the historical and biographical contexts from which it emerged. Second, we consider its auditory dimension and listen closely to when, how, and which questions are asked and answered. Finally, we take the narrative and conversational logics into account. We argue in favor of taking seriously the “heuristic power of the ears,” in the words of Barbara Duden. 1 By listening (rather than only reading transcripts), we can better reconstruct the interactional dynamics and emerging emotions in archived interviews.

Communicating science, mediating presence: reflections on the present, past and future of conferencing.

The move online of almost all meetings in 2020 in the wake of the COVID-19 pandemic threw into sharp relief the taken-for-granted centrality of conferences within scientific culture. While its impact on science has yet to be fully grasped, for the authors of this special issue, this situation held heuristic power for understanding the meanings and functions, now and historically, of international scientific conferencing. Ongoing discussions in the academic world about the pros and cons of virtual meetings bring out the central place of presence in these events and its mediation across space and time by modern infrastructures and technologies. From their rise in the mid-nineteenth century to the experiences of the present day, as well as in imagined futures, international conferences have been about communication. Following James Carey, they can be considered both as places for sharing knowledge and as rituals aimed at fostering and performing communities.

Chasing nomadic worlds: A new class of deep space missions

Nomadic worlds, i.e., objects not gravitationally bound to any star(s), are of great interest to planetary science and astrobiology. They have garnered attention recently due to constraints derived from microlensing surveys and the recent discovery of interstellar planetesimals. In this paper, we roughly estimate the prevalence of nomadic worlds with radii of 100km≲R≲104km. The cumulative number density n>>R appears to follow a heuristic power law given by n>∝R−3. Therefore, smaller objects are probably much more numerous than larger rocky nomadic planets, and statistically more likely to have members relatively close to the inner Solar system. Our results suggest that tens to hundreds of planet-sized nomadic worlds might populate the spherical volume centered on Earth and circumscribed by Proxima Centauri, and may thus comprise closer interstellar targets than any planets bound to stars. For the first time, we systematically analyze the feasibility of exploring these unbounded objects via deep space missions. We investigate what near-future propulsion systems could allow us to reach nomadic worlds of radius >R in a 50-year flight timescale. Objects with R∼100 km are within the purview of multiple propulsion methods such as electric sails, laser electric propulsion, and solar sails. In contrast, nomadic worlds with R≳1000 km are accessible by laser sails (and perhaps nuclear fusion), thereby underscoring their vast potential for deep space exploration.

Cost-inclusive heuristic power management of hybrid power generation plant with ANN-regulated biogas plant

To mitigate the negative effects of fossil fuels, renewable energy sources are being promoted for power generation. Biomass, a renewable energy source, is also being promoted to reduce the negative impact of organic waste generated in societies and industries. Waste biomass is used to produce biogas fuel in a biogas plant via an anaerobic digestion process, which is typically used to generate electricity as well as fuel for cooking and vehicles. However, the high local electrical load in biogas plants reduces profit for plant owner. Biogas-based power generation is hybridised with Photovoltaics (PV) and Battery energy storage systems (BESS), to meet power demand, to increase profit and power generation capacity while reducing the burden of local electrical load on biogas plants. A Proton-exchange membrane fuel cell (PEMFC) stack has been used for power generation from the hydrogen produced from the biogas obtained from the anaerobic digestion process. A cost-inclusive heuristic power management system has been designed for power management in hybrid power generation, considering the availability of sources and their dynamic tariffs. An artificial neural network (ANN) has also been designed to determine the digester operating temperature according to methane yield requirements. The designed system has been investigated for hydrogen consumption, carbon emission, power generation cost, operating cost, power factor, frequency, distortion, efficiency, and voltage regulation. The proposed system effectively meets the load demand at a lesser cost in different scenarios. Cost saving of 28.42%, carbon emission reduction by 96% and 4.53% Total harmonic distortion (THD) at a power factor of 0.99 has been observed. The complete proposed hybrid power plant is ideal for implementation in societies and industries.

Distributed MMSE-based uplink receive combining, downlink transmit precoding and optimal power allocation in cell-free massive MIMO systems

In cell-free massive multiple-input multiple-output systems, a large number of distributed wireless access points (AP) simultaneously serve a number of user equipments (UEs). This setup has recently been introduced as a promising alternative for the current 5G cellular networks. The setup has the ability to offer a good quality of service, especially for the UEs on the cell edges, be it that there is still a need for low-complexity signal processing algorithms. In this paper, the problem of optimal power allocation combined with uplink receive combining and downlink transmit precoding is tackled by providing efficient distributed MMSE-based algorithms. The necessary fronthaul communications to estimate the combining/precoding vectors and the necessary large-scale channel statistics are reduced to a minimum and rely on in-network summation that can be accomplished whenever the APs can be arranged into a tree-topology. Non-weighted max-sum and max–min are used as utilities for the power allocation, but the algorithms are not limited to these cases. Simulations show that the proposed algorithms outperform heuristic power allocation methods, both in uplink and downlink.

Reflections on the heuristic power of Contemplative Art in teaching and research

In this article I discuss the heuristic power of combining contemplative and creative practices using different stages of my research into learning through contemplation. This began with a realisation I had, in my first meditation retreat, about the similarities between creative and contemplative consciousness. It initiated twenty-three years of applied and theoretical research that started with contemplative art workshops I ran in rehabilitation centres. In PhD research that followed I tested a hypothesis of learning through contemplation founded on the concept of an elemental ground of learning that contained an integrating force I termed the feeling nexus. In later research examining the egress of meaning where practitioners return from contemplation, I discovered that art-making can act as a bridge between pre-conceptual knowing and cognitive assimilation of that knowledge. Recently I created the Meditative Process Art (MPA) method that engages the heuristic power of combining creative and contemplative practices.

The explanatory and heuristic power of mathematics

The explanatory and heuristic power of mathematics

The efficacy of instruction in application of mole ratios and submicro- and macro-scopic equivalent forms of the mole within the unit factor method

The unit factor method, a generic strategy for solving any proportion-related problem, is known to be effective at reducing cognitive load through unit-cancellation providing step-by-step guidance. However, concerns have been raised that it can be applied mindlessly. This primarily quantitative prepost study investigates the efficacy of instruction aimed at addressing such concerns. This was done by making submicro- and macro-scopic equivalent forms of the mole concept, and the meanings of mole ratios, explicit, and emphasising the application of these within the unit factor method to solve stoichiometry calculations. Data were collected from 161 South African Physical Sciences teachers’ answers to four calculation, and 14 conceptual, questions in each of a pre- and a post-test written at the start and end, respectively, of a two-day workshop at which such instruction was implemented. These data were analysed for changes in strategy type and calculation and conceptual knowledge, i.e., heuristic power. A small (n = 7) group retained deficient calculation strategies in which they failed to recognise the need to apply proportion to the mole ratio. For the remainder, a weak but significant correlation was found between their conceptual and calculation improvements. There was high uptake of the unit factor method in the posttest, although a group (n = 33) which began with relatively good calculation knowledge largely rejected this method. Statistically significant improvements in both conceptual and calculation knowledge were found regardless of the extent of uptake of the unit factor method, however the calculation improvement measured was significantly lower for the group which showed moderate uptake of the unit factor method, suggesting they may have needed a longer intervention. Based on the findings, speculations are made regarding the nature of knowledge and the mechanism of development of heuristic power. Long-term effects of such an intervention would, however, still need to be determined.

Learning-Based Downlink Power Allocation in Cell-Free Massive MIMO Systems

This paper considers a cell-free massive multiple-input multiple-output (MIMO) system that consists of a large number of geographically distributed access points (APs) serving multiple users via coherent joint transmission. The downlink performance of the system is evaluated, with maximum ratio and regularized zero-forcing precoding, under two optimization objectives for power allocation: sum spectral efficiency (SE) maximization and proportional fairness. We present iterative centralized algorithms for solving these problems. Aiming at a less computationally complex and also distributed scalable solution, we train a deep neural network (DNN) to approximate the same network-wide power allocation. Instead of training our DNN to mimic the actual optimization procedure, we use a heuristic power allocation, based on large-scale fading (LSF) parameters, as the pre-processed input to the DNN. We train the DNN to refine the heuristic scheme, thereby providing higher SE, using only local information at each AP. Another distributed DNN that exploits side information assumed to be available at the central processing unit is designed for improved performance. Further, we develop a clustered DNN model where the LSF parameters of a small number of APs, forming a cluster within a relatively large network, are used to jointly approximate the power coefficients of the cluster.

On the heuristic power of mathematical representations

I argue that mathematical representations can have heuristic power since their construction can be ampliative. To this end, I examine how a representation (a) introduces elements and properties into the represented object that it does not contain at the beginning of its construction, and (b) how it guides the manipulations of the represented object in ways that restructure its components by gradually adding new pieces of information to produce a hypothesis in order to solve a problem.In addition, I defend an ‘inferential’ approach to the heuristic power of representations by arguing that these representations draw on ampliative inferences such as analogies and inductions. In effect, in order to construct a representation, we have to ‘assimilate’ diverse things, and this requires identifying similarities between them. These similarities form the basis for ampliative inferences that gradually build hypotheses to solve a problem.To support my thesis, I analyse two examples. The first one is intra-field (intra-mathematical), that is, the construction of an algebraic representation of 3-manifolds; the second is inter-fields, that is, the construction of a topological representation of DNA supercoiling.

The Heuristic Power of Theory Classification, the Case of General Relativity

The Heuristic Power of Theory Classification, the Case of General Relativity

Psychoanalysis as a scientific research program

Aim to consider some basic aspects of the development of psychoanalysis in the methodology proposed by Imre Lakatos. We outlined the constituent elements of the "solid core" that make up a psychoanalytic metapsychology; the work of the "protective layer" is shown by examples. The development of psychoanalysis and the preservation of its heuristic power have been facilitated by the transformations and discoveries that have led to the formation of the modern form of this scientific research program. Since its emergence, psychoanalysis has become a way of studying the human being outside of medicine as well. The dissemination of the ideas of psychoanalysis in philosophy, sociology, politics, anthropology, etc., involved more and more researchers in the development of theoretical constructions, critically revising the fundamental positions and giving the program a contemporary resonance. Throughout the development of psychoanalysis as a scientific research program, the reliance on therapeutic practice has been critical.

Distributed DRL-Based Downlink Power Allocation for Hybrid RF/VLC Networks

Hybrid radio frequency (RF) and visible light communication (VLC) networks can provide high throughput and energy efficiency with VLC access points (APs) while ensuring ubiquitous coverage with RF APs. Due to dynamic channel conditions and limited resources, the hybrid RF/VLC networks’ resource allocation problem is complex and challenging. Conventional resource allocation techniques fail to overcome these challenges. Heuristic methods can solve high complexity problems; however, they are not robust against changes such as dynamic channel conditions or alternating user requirements. Heuristic methods require centralized control for stability which adds communication overhead between APs. Deep Reinforcement Learning (DRL) based solutions can solve high complexity, dynamic channel conditions, and alternating user requirements while not requiring centralized control. In this paper, we formulate a distributed downlink power allocation problem to optimize the transmit power for users to reach target data rates in hybrid RF/VLC networks. Then, we propose a distributed DRL-based algorithm Deep Deterministic Policy Gradient (DDPG), to solve the formulated computationally-intensive problem. We implement a simulation environment to benchmark the proposed distributed DRL-based method against other methods such as Q-Learning (QL) and Deep Q-Networks (DQN), and centralized heuristic power allocation algorithms. Our simulation results show that the distributed DDPG-based algorithm learns to adapt against changes in the channel or user requirements, while centralized Genetic Algorithm and Particle Swarm Optimization-based algorithms fail to endure against these changes even with coordination between APs. Additionally, we quantify the performance of the DDPG-based algorithm to prevail amid DRL-based algorithms at the expense of higher implementation complexity.

Controversias marítimas. Conocimiento y regulación

This introduction addresses a dual question: the first concerns the specificity of the sea as an object of research and the second questions the heuristic power of the notion of controversy to understand maritime issues. Based on several studies on the sea and its issues, this article attempts to show how the first disputes over the sea contributed to its qualification as a political space, an object of competition and regulation, requiring the production of specific political and regulatory frameworks. The article then focuses on the links between the knowledge produced about the sea and its resources and the social order. In particular, it considers the forms of production and legitimization of knowledge, their hierarchization and their translation into political decisions. Finally, the presentation of the articles in the issue allows us to emphasize the plurality of social worlds and justifications mobilized to qualify maritime challenges. This leads us, in fine, to highlight the relevance of an approach through the bias of controversies in order to analyze in the same set of productions that are heterogeneous both from the point of view of the perspectives retained and the scales taken into account.

Energy management of stationary hybrid battery energy storage systems using the example of a real-world 5 MW hybrid battery storage project in Germany

Utility-scale Battery Energy Storage Systems (BESS) are becoming increasingly important for the transition to large shares of renewable energy sources in the electricity grid. Hybrid battery storage systems are an interesting option to increase the profitability of BESS by combining low-cost battery technologies with more expensive, but also more efficient and robust ones. In theory, such a hybrid system can be cheaper than a single-technology system by leveraging synergy effects while still satisfying the requirements of a given application. An energy management system, which allocates the setpoint power of the BESS to the individual battery technologies, is crucial for taking advantage of the hybrid system layout. This paper details an analysis of different energy management algorithms for hybrid BESS using the example of a real-world project called M5BAT and compares the latter to alternative BESS layouts. Besides two heuristic algorithms, a non-predictive optimization and a predictive optimization are developed. Models comprising the electrical, thermal and aging behavior of the considered BESS components are introduced. Simulations of the operation of the BESS providing frequency containment reserve are conducted for determining the operating costs. The performance of the algorithms is evaluated based on the simulation results. Comparing the results shows significant differences in the operating costs between the algorithms, especially when optimized to reduce operating costs. The economic advantage of hybrid BESS is validated by additional simulations of a virtual hybrid BESS and a virtual single-technology BESS. Although the layout has not been optimized in terms of individual sizing of the different battery technologies, the hybrid BESS show a considerable advantage over the single-technology BESS.Highlights-Comparative study of energy management algorithms for power allocation in hybrid battery energy storage systems.-Development of heuristic power allocation algorithms and optimization approaches for cost-optimal operation.-Modeling of all relevant components and effects that contribute to the operating costs.-Simulations based on frequency-containment reserve operation of a real-world system.-Evaluation of the advantage of hybrid over mono-technology battery energy storage.

Supergroups of eukaryotes through a biotechnologist’s look. The system of eukaryotes and the need for a taxonomic/biotechnological interface

Eukaryotes represent a group of rich biotechnological potential, and its classification having high heuristic power and great predictive capabilities is needed by the biotechnological community. The requirements for biological classification by applied sciences can be reduced to 1) the stability of the classification system and 2) its adequacy to the nature relationships. The present paper provides a retrospective review of eukaryotic megataxonomy, assesses the stability of current system, and outlines approaches to building an interface that ensures a crosstalk of taxonomic and biotechnological communities.