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The multi-energy system is a promising energy-efficient technology to supply electric and thermal energy to end-users simultaneously, which can realize the energy cascade utilization. However, it is challenging to optimize the operation of multi-energy systems due to their inherent structural complexity, as well as the highly coupled nature of multiple energy flows and the uncertainty of renewable energy generation. This paper proposed a collaborative demand-controlled operation strategy for a multi-energy system, which consists of an upper-level model and a lower-level model. In the upper-level model, a robust linear optimization method is adopted to optimize the system operation in a day-ahead stage. In the lower-level model, a stochastic rolling optimization method is applied to achieve a dynamic adjustment to cope with the fluctuation in both renewable electricity generation and electric load. The multiple energy demand-controlled strategy is also applied in the optimal operation strategy to achieve load shifting and to create flexibility in energy demand despite the “source-load” imbalance power fluctuation. A case study is carried out and simulation results verify the effectiveness and correctness of the proposed model of the coordinated operation framework.

Network slicing is a key element in 5G networks. It allows a mobile network operator (MNO) to offer multiple logical networks tailored to the requirements of different industry verticals over a shared infrastructure. From the point of view of an MNO, a mobile virtual network operator (MVNO) is a particular type of vertical network requiring not only the provision of logical networks with specific infrastructure resources but also customized management capabilities to allow the MVNO to offer network slice-based services to its clients over the infrastructure of multiple MNOs. However, the lack of flexibility to provide a customized and efficient network slice management system per tenant under the current proposals of the 3rd Generation Partnership Project (3GPP) and European Telecommunications Standards Institute (ETSI) limits the possibilities of MVNOs. This paper addresses this requirement by proposing a novel virtualized multidomain and multiclient orchestration system aligned with the functionalities of the network slicing management framework proposed by the 3GPP and ETSI. In particular, there are three contributions of this paper: (i) an extended service-based architecture is designed to provide an isolated management system to different MVNOs; (ii) a novel high-level slice instance template is defined to specify management and isolation requirements supporting network slicing as a service model; and (iii) finally, a teleoperated driving use case is described to showcase how our proposal provides an MVNO with an independent management system to orchestrate several network slices in cross-domain environments.

Non-deferrable loads equipped with distributed energy resources, such as solar photovoltaic generation and energy storage, can offer a wide range of benefits to the power system, including load leveling, hedging against forecast uncertainty, and ancillary services. This paper presents a techno-economic approach to optimally equip a flexible energy resource, namely a battery energy storage system (BESS), with non-deferrable loads for capacity enhancement of a distribution grid. The proposed approach is generic in applicability, and includes two different optimization mathematical models. The first model quantifies the needed flexibility with non-deferrable loads, based on a consideration of distribution grid limitations, while the second model determines the optimal flexibility amount associated with non-deferrable loads considering investor profitability. Thereafter, a distribution operations model is developed to evaluate the impact of non-deferrable loads, equipped with an optimal size of BESS, on the distribution system loading capacity. An example of non-deferrable loads considered in this paper is that of the rapid charging loads of plug-in electric vehicles (PEVs) that cannot be controlled or shifted due to the short stay of PEVs at a charging facility. Hence, a probabilistic model for PEV rapid charging loads is first developed. Case studies and simulation findings, considering a 32-bus distribution system, demonstrate that the proposed approach helps reduces the impact of non-deferrable loads on a distribution system and enhances the system power margin, which defers the need for system upgrades.

This paper discusses the development and implementation of the architecture of a Framework for Aerospace Vehicle Reasoning, ‘FAVER’. Integrated Vehicle Health Management systems require a holistic view of the aircraft to isolate faults cascading between aircraft systems. FAVER is a system-agnostic framework developed to isolate such propagating faults by incorporating Digital Twins (DTs) and reasoning techniques. The flexibility of FAVER to work with different types and scales of DTs and diagnostics, and its ability to adapt and expand for previously unknown faults and new systems are demonstrated in this paper. The paper also shows the novel combination of relationship matrix and fault attributes database used to structure the knowledge of FAVER’s expert system. The paper provides the working mechanism of FAVER’s reasoning and its ability to isolate faults at the system level, identify their root causes, and predict the cascading effects at the vehicle level. Four aircraft systems are used for demonstration purposes: i) the Electrical Power System, ii) the Fuel System, iii) the Engine, and iv) the Environmental Control System, and the use case scenarios are adapted from real aircraft incidents. The paper also discusses the pros and cons of FAVER’s reasoning via demonstrations and evaluates the performance of FAVER’s reasoning through a comparative study with a supervised neural network model.

The unmanned aerial vehicle (UAV) is inexpensive and offers a fast response speed and robust flexibility; thus, it is a promising tool in the maritime buoyage inspection scenario, which involves monitoring and accessing a lateral mark system far away from the coast. However, two main problems can occur during inspection. The first is extreme weather conditions, resulting in a deviation between the inspection route and the design route. The second is that the buoyage beacons are visited only once. Therefore, this article proposes a buoyage inspection system consisting of a single UAV and random coastal buoys. The UAV automatically takes off from the depot, performs a self-check on the buoy beacons, and then returns to the depot. A cascade active disturbance rejection controller (ADRC) is designed to adjust the real-time trajectory of the UAV system. A feasible trajectory planning method is also designed based on the continuous Hopfield neural network (CHNN) and genetic algorithm (GA) to minimize the inspection distance. Extensive simulations are conducted to demonstrate the effectiveness of the proposed method.

The multi-energy system (MES) provides a good environment for the local consumption of renewable energy such as wind and solar power because of its high operational flexibility. In the MES, the hybrid energy storage system (HESS) composed of the battery and thermal storage tank plays an important role in enhancing reliability, economics, and operational flexibility. Hence, determining the optimal size of HESS in the MES is a critical problem but has not received enough attention. In light of this problem, this paper focuses on the optimal HESS planning problem in the community MES (CMES) under diverse uncertainties. Firstly, a two-stage stochastic planning model is proposed for the CMES to coordinate the optimal long-term HESS allocation and the short-term system operation. The thermal inertia in the heating network, space heating demand, and domestic hot water demand is utilized to reduce both the planning and operational cost. Secondly, a deterministic equivalence is proposed for the two-stage planning model to convert it into a mixed-integer linear programming model, which is then solved by off-the-shelf solvers. Finally, simulation results verify the effectiveness of the proposed method. The results reveal that the HESS can enhance the operational flexibility of the CMES but only needs a very few investment costs and prove that the thermal inertia in the CMES can reduce the investment cost of HESS, the fuel, and the operational maintenance cost.

Solid-state transformers (SSTs) enable electric energy distribution with higher flexibility, sustainability and efficiency, and the AC/DC hybrid microgrid (MG) is one of the representative applications. However, the design and analysis of SSTs and MGs were limited by lack of fast simulation tools that can accurately and efficiently handle systems with massive number of switching devices. This article demonstrates how the discrete-state event-driven (DSED) approach allows an accurate and faster simulation of the SSTs and MGs than the state of the art and benefit the practical design. The numerical prototyping of a hybrid MG composed of a megawatt SST together with multiple converters (e.g. PVs, EVs and batteries) is presented in this article. The performance is compared with existing simulation approaches. Various scenarios are investigated, including the grid-connected mode, the islanded mode, and the mode transition considering the communication delay. A time cost of several hours is decreased to tens of seconds with same accuracy using DSED, enabling faster simulations for complicated tasks. It is discussed how the speed and accuracy benefit the practical SST and MG design.

The physical function model has been effectively used for model-based development (MBD) of automobile systems. This research demonstrates a novel application of this modeling method to the state estimation of nonlinear mechanical systems based on the Kalman filtering theory. The physical function model is a block diagram that describes each engineering field by a common rule, which focuses on the energy flow. Compared to traditional modeling approaches, this model has the flexibility to incorporate a wide range of nonlinear characteristics and the know-how accumulated by the manufacturers. Hence, it has a quite high affinity with the industrial world. The purpose of this research is to pioneer a new application of the physical function model beyond simulation analysis. In particular, physical function modeling offers a model of a system with multiple nonlinearities in the form of a time-varying linear state equation. By focusing on this feature, this study applies it to the Kalman filtering theory. The proposed approach is applicable to a wide range of nonlinearities, reduces the calculation load, and considers the background of the current MBD. Finally, verifications using an experimental apparatus, which simplifies an automotive drivetrain with backlash, demonstrate the effectiveness of the proposed approach.

The AC / DC active distribution network (ADN) connected to a large number of renewable energy source (RES) and new loads such as EV will become the main form of the future ADN, which intensifies the volatility and uncertainty of the net load. To cope with this situation, considering various types of flexibility resources, this paper quantitatively evaluates the flexibility demand and supply in the system, and constructs a day-ahead flexible operation model of AC/DC ADN with a high proportion of RES. At first, the flexibility demand is quantified firstly by considering volatility and uncertainty comprehensively, and the conditional probability distribution of wind power volatility is obtained through the analysis of mutual information and other correlation coefficients. Then, a quantitative method to the time flexibility provided by orderly charging of electric vehicles (EVs) is proposed for the flexible cooperation with other resources. Besides, voltage source converter (VSC) with precise loss is considered as flexibility source to participate in dispatching. Furthermore, considering the preference of flexibility in different directions, a weighted node flexibility margin index is proposed. Considering the accuracy and computational efficiency, a second order cone relaxation method which is suitable for the elaborate VSC model is applied in this paper. In the end, Test results of an improved IEEE33-bus system with a three-terminal DC network show that the proposed model can improve the flexibility and reduce the operation cost of the ADN.

Cybercrime is growing at a rapid pace, and its techniques are becoming more sophisticated. In order to actively cope with such threats, new approaches based on machine learning and requiring less administrator intervention have been proposed, but there are still many technical difficulties in detecting security attacks in real time. To solve this problem, we propose a new machine learning-based real-time intrusion detection algorithm. Unlike the existing approaches, the one proposed can detect the presence of an attack every time a packet is received, enabling real-time detection. In addition, our algorithm effectively reduces the system load, which may significantly increase from real-time detection, compared to non-real-time detection. In the algorithm, the increase in the number of memory accesses can be minimized (to below 30 %) compared to conventional methods. Since the proposed method is pure software-based approach, it has excellent scalability and flexibility against various attacks. Therefore, the proposed method cannot support the high classification performance of the hardware-based method but also the high flexibility of the software-based method simultaneously, it can effectively detect and prevent various cyber-attacks.

Educational remote laboratories allow users to access and control via the Internet remote equipment, that may be physically located anywhere in the world. They have a great potential for education, since they offer many advantages, such as cost reduction, high flexibility or the possibility of sharing equipment among institutions. However, their use is not yet as widespread as it could be. Freely available laboratories often present significant reliability issues. Instructors often plan their lessons in advance, and incorporating third-party tools that are prone to failure is risky, since they may be unavailable for the class and present errors to the students, which may make it necessary to suspend the session or find alternatives on the go. This contribution analyzes the reliability of a set of state-of-the-art laboratories with a view to categorizing and quantifying the most common failures. It then describes a multi-layered software model for detecting errors in laboratories. This model has two main goals. First, to ensure that the laboratory management system is aware of the state of the laboratory, so that users do not experience errors and laboratory maintenance personnel can repair it as soon as possible. Second, to serve as a base for a fault-tolerance model that leverages replicated instances of the laboratory. This fault-tolerance model is also described, and is designed to provide high availability and support for multiple concurrent users. The effectiveness of this model is validated by analyzing the reliability and availability of two remote laboratories in which it has been implemented.

This article proposes a method for identifying potential self-adequate sub-networks in the existing power distribution grid. These sub-networks can be equipped with control and protection schemes to form microgrids capable of sustaining local loads during power systems contingencies, thereby mitigating disasters. Towards identifying the best microgrid candidates, this work formulates a chance-constrained optimal distribution network partitioning (ODNP) problem addressing uncertainties in load and distributed energy resources; and presents a solution methodology using the sample average approximation (SAA) technique. Practical constraints like ensuring network radiality and availability of grid-forming generators are considered. Quality of the obtained solution is evaluated by comparison with- a) an upper bound on the probability that the identified microgrids are supply-deficient, and b) a lower bound on the objective value for the true optimization problem. Performance of the ODNP formulation is illustrated through case-studies on a modified IEEE 37-bus feeder. It is shown that the network flexibility is well utilized; the partitioning changes with risk budget; and that the SAA method is able to yield good quality solutions with modest computation cost. Virginia Tech Open Access Subvention Fund Published version This work was supported in part by the Virginia Tech Open Access Subvention Fund.

Starting with a product quality problem, this paper presents the practical applications of Industry 4.0 technology to solve the problem in manufacturing technique and to establish manufacturing process control in new product introduction (NPI) with flexibility and reconfigurability, under the condition that this is a new plant with limited resources. With regard to solving the manufacturing technique problem, the Finite Element Method (FEM) has been used to analyze failure mode and to support design optimization, the regression analysis has been adopted to obtain the force-deformation relationship and to guide the improvement for manufacturing parameter adaptation. The analysis method that combined the FEM analysis and the regression analysis can be an effective approach for dealing with manufacturing technique problem. With regard to establishing the manufacturing process control, the Radio Frequency Identification (RFID) implementation functions as the medium for data transfer and for status confirmation, thus realizing the process control and the quality control in the form of cyber-physical system (CPS). Data strategy of the process control and the quality control can be applied to general manufacturing system. The Industry 4.0 implementations and methods mentioned in this paper can serve as a reference for the company that are planning to build a small or medium-sized manufacturing plant and to enhance its performance with Industry 4.0.

Most studies about the solar forecasting topic do not analyze and exploit the temporal and spatial components that are inherent to such a task. Furthermore, they mostly focus just on precision and not on other meaningful features, such as flexibility and robustness. With the current energy production trends, where many solar panels are distributed across city rooftops, there is a need to manage all this information simultaneously and to be able to add and remove sensors as needed. Likewise, robust models need to be able to cope with (inevitable) sensor failure and continue producing reliable predictions. Due to all of this, solar forecasting models need to be as decoupled as possible from the number of data sources that feed them and their geographical distribution, enabling also the reusability of the models. This article contributes with a family of Deep Learning models for solar irradiance forecasting complying with the aforementioned features, i.e. flexibility and robustness. In the first stage, several Artificial Neural Networks are trained as a basis for predicting solar irradiance on several locations at the same time. Thereupon, a family of models that work with irradiance maps thanks to Convolutional Long Short-Term Memory layers is presented, obtaining forecast skills between 7.4% and 41% (depending on the location and horizon) compared to the baseline. The latter family comes with flexibility and robustness features, which are required in large-scale Intelligent Environments, such as Smart Cities. Working with irradiance maps means that new sensors can be added (or removed) as needed, without requiring rebuilding the model. Experiments carried out show that sensor failures have a mild impact on the prediction error for several forecast horizons.

Choosing optimal Deep Learning (DL) architecture and hyperparameters for a particular problem is still not a trivial task among researchers. The most common approach relies on popular architectures proven to work on specific problem domains led on the same experiment environment and setup. However, this limits the opportunity to choose or invent novel DL networks that could lead to better results. This paper proposes a novel approach for providing general recommendations of an appropriate DL architecture and its hyperparameters based on different configurations presented in thousands of published research papers that examine various problem domains. This architecture can further serve as a starting point of investigating DL architecture for a concrete data set. Natural language processing (NLP) methods are used to create structured data from unstructured scientific papers upon which intelligent models are learned to propose optimal DL architecture, layer type, and activation functions. The advantage of the proposed methodology is multifold. The first is the ability to eventually use the knowledge and experience from thousands of DL papers published through the years. The second is the contribution to the forthcoming novel researches by aiding the process of choosing optimal DL setup based on the particular problem to be analyzed. The third advantage is the scalability and flexibility of the model, meaning that it can be easily retrained as new papers are published in the future, and therefore to be constantly improved.

Service firms not only need to develop differentiated services to meet the requirements of customers with various preferences, but also have to improve service flexibility and the efficiency of the service system. A service family is a strategy by which different modules are configured, based on the service platform, to create a variety of differentiated services. This research considered both the effect of multi-server queues and the heterogeneous service processes in service family design problems to establish a framework of service modularization from three different perspectives—process, activity, and component. To optimize the service family design, a nonlinear integer-programming model was established to determine the optimal configurations of modules and prices for the service family and the optimal number of servers. The model is transformed into a linear form, and thus, can be solved using a commercial optimization software for small-scale problems. An improved genetic algorithm integrated with a neighborhood search was further developed to solve large-scale problems. The correctness of the linearized model and the effectiveness of the meta-heuristic algorithm were demonstrated through case studies and numerical experiments.

Low-pass and high-pass non-integer order shelving filter designs, which are suitable for acoustic applications, are presented in this work. A first design is based on a standard fractional-order bilinear transfer function, while a second one is based on the transposition of the integer-order transfer function into its power-law counterpart. Both transfer functions are approximated using the Oustaloup approximation tool, while the implementation in the case of the power-law filters is performed through the employment of the concept of driving-point impedance synthesis. An attractive benefit is the extra degree of freedom, resulting from the variable order of both fractional-order and power-law filters, which allows improved design flexibility compared to the case of integer-order filters. From the implementation point of view, only one building block is required to realize both filter types, thanks to the employment of the Voltage Conveyor. European Union (European Social Fund~ESF) through the Operational Program Human Resources Development, Education and Lifelong Learning [MIS-5000432]; Istanbul Technical University VLSI Laboratories for the Cadence Design Environment This work was supported in part by the Greece and the European Union (European Social Fund~ESF) through the Operational Program Human Resources Development, Education and Lifelong Learning in the context of the project Strengthening Human Resources Research Potential via Doctorate Research-2nd Cycle implemented by the State Scholarships Foundation (IKY) under Grant MIS-5000432, and in part by the Istanbul Technical University VLSI Laboratories for the Cadence Design Environment.

With the booming development of prosumers, there is an urgent need for a prosumer energy management system to take full advantage of the flexibility of prosumers and take into account the interests of other parties. However, building such a system will undoubtedly reveal users’ privacy. In this paper, by solving the non-independent and identical distribution of data (Non-IID) problem in federated learning with federated cluster average (FedClusAvg) algorithm, prosumers’ information can efficiently participate in the intelligent decision making of the system without revealing privacy. In the proposed FedClusAvg algorithm, each client performs cluster stratified sampling and multiple iterations. Then, the average weight of the parameters of the sub-server is determined according to the degree of deviation of the parameter from the average parameter. Finally, the sub-server multiple local iterations and updates, and then upload to the main server. The advantages of FedClusAvg algorithm are the following two parts. First, the accuracy of the model in the case of Non-IID is improved through the method of clustering and parameter weighted average. Second, local multiple iterations and three-tier framework can effectively reduce communication rounds.

To deal with the issue of the distribution system (DS) resilience enhancement under extreme weather events, this paper presents a new methodological framework for enhancing the load restoration of DS during a hurricane. As distinct from existing studies, this approach integrates mobile emergency generators (MEGs) and prosumer communities (PCs) incorporating combined heat and power (CHP) units, electric boilers (EBs), photovoltaic (PV) sources, and demand response (DR) resources and comprehensively explores the coordination and flexibility of supply-side and demand-side resources to boost the post-disaster DS recovery. The discussed problem is formulated by using a two-stage robust optimization model. In the first stage, the MEGs are pre-positioned prior to the hurricane with the objective of minimizing the outage cost of the load to promote the capability of DS to resist extreme disturbance. The second stage determines the real-time allocation of MEGs, output power of CHP units and EBs, and the power consumption of electric loads after the hurricane to maximize DS’s load recovery considering the worst-case of the uncertainty realization. Since the fragility analysis of network elements has an essential impact on the efficacy of the optimal strategy, we introduce the Z-number-based approach to scientifically determine the failure probability of components considering the effect of the aging of components and the credibility of the failure information obtained from the fragility. The effectiveness of the proposed methodology is examined based on an IEEE 123-bus distribution test system, and the obtained results confirm the validity of the proposed approach in actual implementations.

Nowadays, cluster tools tend to concurrently process multiple types of wafers with similar recipes in order to improve their utilization and flexibility in semiconductor manufacturing. Different wafer types may have different wafer flow patterns, resulting in that cluster tools are deadlock-prone. It is challenging to develop a general method to solve the deadlock problem of cluster tools without restriction on the wafer types. This work aims at solving such a challenging problem for single-arm cluster tools. To do so, a general Petri net model is developed for single-arm cluster tools. Given the wafer flow patterns of all wafer types to be processed in a single-arm cluster tool, such a Petri net model can be easily obtained by defining the relationship between places and transitions. Then, a control method by using self-loops is presented to prevent the model from deadlocks during the evolutions from the initial state to the final state. Furthermore, such a control method is proved to be optimal. Illustrative examples are given to verify the proposed method at last.

The electricity demand has grown continuously in recent years, raising the necessity to expand generation sources, distribution networks, and equipment efficiency. In addition, it is necessary to attend sustainable development in a conciliated manner. Applications involving the intelligent management of the distributed networks have increased to achieve a balance between growth and sustainability. In this context, this article presents the development of an Intelligent Electric Power Management System (IEPMS) for the economic maximisation of a photovoltaic system applied to a prosumer residential unit without storage in Brazil. Using historical meteorological data and a heuristic to simulate energy use habits, the IEPMS forecasts both generation and demand in 24 hours. From the projections, an optimisation problem was built and solved using the genetic algorithm technique to find the most economical moments for driving loads. This model aims to reach the lowest daily cost of electricity, considering the return (sale) of unused energy to the power distribution company. The validation of the IEPMS considered four usage patterns, integrating 26 scenarios, those composed by the (i) flexibility; (ii) type of tariff; and (iii) hit rates provided by the climate forecasting method proposed for the system. As a result, the IEPMS savings considering the white tariff were 34.72% for one year, assuming full-time external work usage. Additionally, it was possible to identify in all scenarios that the proposed method’s performance was not less than 97%, measured through the relative error among distinct hit rates of the evaluated climatic forecast.

Real-time three-dimensional reconstruction is increasingly important in many fields. There are mainly two ways to improve real-time performance: increasing hardware rate and reducing captured frames count. For increasing hardware rate, the binary defocusing technique can effectively break the limitation of speed. For reducing captured frames count, the fusion technique is typically introduced. However, the simultaneous work of binary defocusing technique and fusion technique is hard to achieve. To this end, we establish a novel system framework with a camera and dual projectors. The dual projectors can control defocusing level independently, i.e. enhance the flexibility of projector. Based on this, our paper exhibits two decoding phase methods: the strict-alignment method and the loose-alignment method. The strict-alignment method requires the projection chips to be strictly aligned, and a virtual example demonstrated effectiveness with three captured frames. In order to further improve practicality, the loose-alignment method, which relaxes the restriction, is proposed. Finally, experiments verify the performance of our proposed method.

Due to the ongoing slowdown of Dennard scaling, heterogeneous hardware architectures are inevitable to meet the increasing demand for energy efficient systems. However, one of the most important aspects that shape today’s computing landscape is the wide availability of software that can run on any system. Current applications that use accelerators, in contrast, are often especially tailored to a specific hardware setup and therefore not universally deployable. This is particularly true for reconfigurable logic as their internal structure requires the circuits and their integration to be designed as well. This makes them inherently difficult to use and therefore less accessible for a general audience. Nevertheless, their balance of flexibility and efficiency puts reconfigurable accelerators in a unique position between CPUs, GPUs, and ASICs. Therefore, one of the main challenges of future heterogeneous systems is to foster collaborative computing between these vastly different components while still being simple to use. Previous approaches mostly focused on subproblems instead of a holistic view of hardware and software in the context of commonplace usability. This paper analyzes the general demands on a reconfigurable platform and derives their requirements regarding accessibility and security. Hereby, we investigate several key features like hardware virtualization, system shared virtual memory, and the use of wide-spread programming paradigms. Then, we systematically build up such a platform based on the established ROCm GPU framework and its internal HSA standard. This new common HERA methodology is finally also demonstrated as a prototype.

In the Demand Side Management (DSM) context, residential customers have the potential for reducing costs and relieving the grid with non-thermostatic appliances. These appliances might be optimally scheduled by a central entity, taking into account user preferences. However, the user might not be able to communicate its preferences “a-priori”, leaving to the central entity the task of understanding preferences that should be learnt without causing discomfort to the user. With this premise, this study aims at exploring a DSM program that learns the acceptance of realistic simulated users to shift in time of home appliances, such as washing machines and dishwashers, analysing the benefits that arise from their inclusion. To this end, the proposed Acceptance Learning Algorithm 2.0 (ALA 2.0) minimises costs in scenarios with different energy sources and with a certain level of acceptance to shift in time, optimally scheduling the appliances according to the boundaries found by the proposed algorithm. ALA 2.0 is able to understand preferences also when modelling a behaviour of the user which is influenced by external factors not directly observable and when users make very few requests, interacting with the user in a simple way. Experimental results highlight that it is possible to understand the acceptance to the shift in time of the simulated users without any prior knowledge and without causing too much discomfort, achieving a win-win situation. As an example, more than 90% of requests were accepted in December, which is chosen as a representative month.

With the speeding up of the fifth generation (5G) new radio (NR) worldwide commercialization, one of the paramount questions for operators and vendors is how to optimize the radio links, considering the widely diverse scenarios envisioned. One of the key pillars of 5G has been an unprecedented flexibility on the configuration of the radio access network (RAN) on scenarios that include cellular, vehicular, and industrial networks among others. This flexibility has its main exponent on link adaptation (LA), which has evolved into a multi-domain technique where a plethora of parameters, like numerology, bandwidth part, radio frequency beam, power, modulation and coding scheme (MCS) or multiple antenna precoding can be adapted to the instantaneous link conditions. Although such enhancements open the door to a significant performance improvement, they also pose many challenges to LA optimization. In this article, we first present the signaling aspects of NR technology for multi-domain LA and the challenges that need to be faced. Then, we explore the latest advances on LA for wireless networks. We envision a combination of machine learning (ML) tools with multi-domain LA as a key enabler for 5G and beyond networks. Finally, we investigate emerging ML approaches for LA and present a promising application of ML for LA that is assessed with simulations. With this scheme, the training is performed at the network side to relieve the user equipment (UE) to do such a complex task. It is shown with simulations that our ML approach outperforms the well-known outer loop link adaptation (OLLA) algorithm in terms of instantaneous block error rate (BLER), while reaching the same average spectral efficiency (SE). Interestingly, it is shown that the proposed scheme only requires 4 bits to represent the features used to train the model, which makes it suitable for implementation in real systems with limited feedback.

Precision Agriculture (PA) is a management strategy that utilizes communication and information technology for farm management. It is a key to improve productivity by using the best agricultural practices and optimal usage of resources. Agriculture faces diverse challenges due to soil degradation, climate variation, and increasing costs. To unfold these challenges, PA uses Wireless Sensor Networks(WSNs) and exploits acquisition, communication, and processing of the data as basic enabling technologies to amplify the crop yield. Also, many other multidisciplinary technologies are supporting PA in finding the most novel use cases for PA. The use of Machine Learning (ML) and Artificial Intelligence (AI) has transformed PA at almost every level. The fog/edge paradigm is mitigating many challenges such as network bandwidth and security by bringing computation closer to the deployed network. At the same time, Software Defined Networks (SDN) brings flexibility, big data assists in handling data, and nanotechnology plays a crucial part in driving the innovation in PA. This paper delves into ways these technologies are transforming PA in respective tracks, exhibiting the significance of integrating multidisciplinary approaches towards the future of PA. In addition to a comprehensive survey, this paper proposes a multidisciplinary architecture: AgriFusion, for efficient and cost-effective agriculture solutions. A list of industrial solutions for different aspects of farm management and their underlying focused technology have been highlighted. This can help to align research and industrial goals for PA. Furthermore, this paper defines a step approach to describe the performance dichotomy between resource availability and objectives for PA. In addition, solution architecture is proposed for designing Key Performance Indicators (KPI) in PA. In the end, some open research issues in implementing PA and respective future scopes have been presented.

Producción Científica, Elastic Optical Networks (EONs) are a promising optical technology to deal with the ever-increasing traffic and the vast number of connected devices of the next generation of the Internet, associated to paradigms like the Internet of Things (IoT), the Tactile Internet or the Industry 4.0, to name just a few. In this kind of optical network, each optical circuit or lightpath is provisioned by means of superchannels of variable bandwidth. In this manner, only the necessary bandwidth to accommodate the demand is allocated, improving the spectrum usage. When establishing a connection, the EON control layer determines the modulation format to be used and allocates a portion of the spectrum in a sequence of fibers from the source to the destination node providing the user-demanded bandwidth. This is known as the routing, modulation level and spectrum assignment (RMSA) problem. In this work, we firstly review the most important contributions in that area, and then, we propose a novel RMSA algorithm, multi-path best-fit (MP-BF), which uses a split spectrum multi-path strategy together with a spectrum assignment technique (best-fit), and which jointly exploit the flexibility of EONs. A simulation study has been conducted comparing the performance of EONs when using MP-BF with other proposals from the literature. The results of this study show that, by using MP-BF, the network can increase its performance in terms of lightpath request blocking ratio and supported traffic load, without affecting the energy per bit or the computation time required to find a solution.

The Fifth Generation (5G) mobile networking coupled with Internet of Things (IoT) can provide innovative solutions for a wide range of uses cases. The flexibility of virtualized, softwarized and multi-tenant infrastructures and the high performance promised by 5G technology are key to cope with the deployment of the IoT use cases demanded by various vertical businesses. Such 5G IoT use cases incur challenging Quality of Service (QoS) requirements especially connectivity for millions of IoT devices to achieve massive Machine-Type Communication (mMTC). In addition, network slicing is a key enabling technology in 5G multi-tenant networks to create logical virtualized networks for delivering customised solutions to meet diverse QoS requirements. This work presents a 5G IoT framework with network slicing capabilities able to manage a vast number of heterogeneous IoT network slices dynamically on demand. The proposed solution has been empirically tested and validated in five realistic vertical-oriented IoT use cases. The achieved results demonstrate a excellent stability, isolation and scalability while being able to meet extreme QoS requirements even in the most congested and stressful scenarios.

The timetabling problem is common to academic institutions such as schools, colleges or universities. It is a very hard combinatorial optimisation problem which attracts the interest of many researchers. The university course timetabling problem (UCTTP) is difficult to address due to the size of the problem and several challenging hard and soft constraints. Over the years, various methodologies were proposed to solve UCTTP. The purpose of this survey paper is to provide the most recent scientific review of the methodologies applied to UCTTP. The paper unveils a classification of methodologies proposed in recent years based on chronology and datasets used. Perspectives, trends, challenges and opportunities in UCTTP are also presented. It is observed that meta-heuristic approaches are popular among researchers. This is followed closely by hybrid methodologies. Hyper-heuristic approaches are also able to produce effective results. Another observation is that the state-of-art methodologies in the scientific literature are not fully utilised in a real-world environment perhaps due to the limited flexibility of these methodologies.

To solve the problem that the uncertainty of distributed generation output seriously affects the safe and stable operation of distribution network, heat load, as a potential flexibility resource, can improve flexibility and eliminate system fluctuation without increasing new equipment. Therefore, an optimal dispatching model of the distribution network is proposed in this paper to improve the system flexibility while ensuring human comfort and operation economy. Firstly, the virtual energy storage model and human thermal comfort model of electric water heater (EWH) and air conditioning (AC) load are established. Secondly, a flexibility evaluation method is proposed based on the virtual energy storage of heat load. Furthermore, to decrease comprehensive operation cost and increase system flexibility, a two-stage distributed robust optimal dispatching model, which considers three kinds of flexible resources of EWH, AC and gas turbine (GT) is constructed. Then, the norm is used to constrain the uncertainty probability of wind turbine and photovoltaic output, and the column and constraints generation (C&CG) algorithm is used to solve the model. Finally, a simulation is given to verify the effectiveness of the proposed method in improving the flexibility of distribution network.

Vehicle detection and classification plays an important role in intelligent transportation system. Compared with traditional detectors, the detection and classification based on traffic surveillance video shows a huge advantage in its flexibility and continuity. However, to get wide applicability and strong robustness, most current methods focus on improving the accuracy of detectors by adjusting network parameters constantly, or increasing the size of training sets, which challenges the collection and labeling of data, the performance of computers, the scope of application and so on. Moreover, the unique continuity characteristic of the video, which can be used to describe the motion features of vehicle, is often ignored. Take these facts into account, this paper proposed a video-based vehicle detection and classification method, which is based on static appearance features and motion features both. Four detectors of different performance were trained with small training sets, and the designed algorithms for the remove, selection and reorganization of detected objects contribute to obtaining the optimal results of detection and classification. The experiment results show that the proposed method is able to detect and classify vehicles with more than 0.95 accuracy dealing with different road environments.

While providing charging service for electric vehicles, charging stations are also aggregation centers which supply adjustment capabilities to the power grid. It is necessary for charging stations to take care of all parties’ interests with respect to users’ independent choice. This article focuses on multi-element charging stations which contain conventional load, energy storage and distributed renewable energy generation besides charging piles. Firstly, charging price mechanism that improves participation motivation of users in providing charging flexibility were designed. Next, rational model based on expected utility and bounded rational model based on prospect theory were established respectively. Model predictive control(MPC) has been selected to handle uncertainties caused by users’ behavior and output of distributed renewable energy generation. Both operating economy and adjustment flexibility of charging stations have been taken into account in proposed comprehensive scheduling strategy. Based on the setting of dummy variables, double-layer optimization has been converted into single-layer optimization which can reduce the calculation time significantly, making the strategy suitable for online application. Advantage of the bounded rational model in predicting users’ behavior was verified by experiments towards real people. The selection of time scale for MPC was discussed through simulation experiments in different scenarios. In the simulation experiment, the proposed scheduling model integrating social-physical methods has shown good performance in power grid requirement following and economic optimization.

Centralised, front-of-the-meter battery energy storage systems are an option to support and add flexibility to distribution networks with increasing distributed photovoltaic systems, which generate renewable energy locally and help decarbonise the power sector. However, the provision of specific services at distribution level remains under development for real applications in industry. To this end, this paper presents an exhaustive techno-economic analysis of the role of front-of-the-meter battery energy storage systems in primary distribution networks with presence of distributed PV covering: (i) the siting decision for storage systems using multi-objective genetic algorithm optimisation; (ii) the response when smart capabilities for PV inverters (e.g., volt-var control) are present; and (iii) the quantification of revenue streams and compensation schemes that would bring positive profitability when providing distribution-specific services based on the supply of real and reactive power. The performance of grid-level battery energy storage technology is evaluated in the IEEE 34-bus system particularised to the distribution code of Northern Ireland, UK. The techno-economic assessment covers one year of simulations at 1-minute resolution performed with the simulation tool EPRI OpenDSS and its Python communication interface. The results show that, besides the technical benefits of centralised battery storage systems, the economic compensation based on traditional transmission-level services is not sufficient for the profitability of these projects. Several ideas are explored to improve the profitability of these systems considering the local value and decarbonisation they add. The findings presented can direct system operators and regulators towards developing schemes to incentivise centralised battery energy storage projects in distribution networks in the context of distribution-level services.

In low latitudes, ice storage air conditioners (ISACs) have been widely used to cool while locally responding to the distribution network demand. However, due to the lack of the direct cold energy exchange between ISACs, the cooling load could only be shifted on the time scale instead of the space scale, resulting in an unsatisfactory regulation result. To address the above problem, this article proposes a novel collaborative expansion planning scheme for integrated cooling and power system. Firstly, to increase the regulation flexibility, a novel cold energy supply system is designed, where ice making stations and trucks are used to produce and deliver ices to multiple terminal ISACs. On this basis, taking the capacity of the wind turbines (WTs), large ice makers (LIMs), and trucks as configuration decisions, an optimal expansion planning model is established considering wind generation uncertainties. This model is converted into a classic mixed-integer second-order cone programming (MISOCP) problem using linear techniques, and efficiently solved by the Benders decomposition method. Finally, Shapley value method in economics is used to fairly distribute the revenues between the grid operator (GO) and ISAC owners. Simulation studies on IEEE 14-node distribution network indicate the proposed expansion model is effective and beneficial.

Nowadays, the increase in the number of mobile users and cellular traffic leads to new challenges in the fifth-generation (5G) of cellular networks. The increase in the demand for high data rates brings challenges like scalability and flexibility in the 5G network. Software-defined networking (SDN) is a network paradigm that separates the control plane and data plane in the network and ease the management of the network. In this work, an SDN based 5G core architecture is proposed, in order to introduce flexibility and ease of management in the network. Another benefit of using SDN is to make the network vendor-independent. Furthermore, the explanation of initial attachment and handover procedures in the proposed architecture is provided. A network simulator is built to evaluate the performance of proposed architecture, in terms of end-to-end delay, throughput and resource utilization of controller, under different network factors. A performance comparison, in terms of end-to-end delay, between proposed SDN based 5G architecture and traditional 5G architecture is provided. Results show that the proposed architecture provides 18% to 62% less end-to-end delay, under different factors for different procedures, compared to the traditional 5G architecture. A comparison with previous works is also provided, which indicates similar trends in delay between our work and previous studies.

Network function (NF) developers have traditionally prioritized performance when creating new packet processing capabilities. This was usually driven by a market demand for highly available solutions with differentiating features running at line rate, even at the expense of flexibility and tightly-coupled monolithic designs. Today, however, the market advantage is achieved by providing more features in shorter development cycles and quickly deploying them in different operating environments. In fact, network operators are increasingly adopting continuous software delivery practices as well as new architectural styles (e.g., microservices) to decouple functionality and accelerate development. A key challenge in revisiting NF design is state management, which is usually highly optimized for a deployment by carefully selecting the underlying data store. Therefore, migrating to a data store that suits a different use case is time-consuming as it requires code refactoring and adaptation to new application programming interfaces, APIs. As a result, refactoring NF software for different environments can take up to months, reducing the pace at which new features and upgrades can be deployed in production networks. In this paper, we demonstrate experimentally that it is feasible to introduce an abstraction layer to decouple NF state management from the data store adopted while still approaching line-rate performance. We present FlexState, a state management system that exposes data store functionality as configuration options, which reduces code refactoring efforts. Experiments show that FlexState achieves significant flexibility in optimizing the state management, and accelerates deployment on new scenarios while preserving performance and scalability.

Systems whose entities interact with each other are common. In many interacting systems, it is difficult to observe the relations between entities which is the key information for analyzing the system. In recent years, there has been increasing interest in discovering the relationships between entities using graph neural networks. However, existing approaches are difficult to apply if the number of relations is unknown or if the relations are complex. We propose the DiScovering Latent Relation (DSLR) model, which is flexibly applicable even if the number of relations is unknown or many types of relations exist. The flexibility of our DSLR model comes from the design concept of our encoder that represents the relation between entities in a latent space rather than a discrete variable and a decoder that can handle many types of relations. We performed the experiments on synthetic and real-world graph data with various relationships between entities, and compared the qualitative and quantitative results with other approaches. The experiments show that the proposed method is suitable for analyzing dynamic graphs with an unknown number of complex relations., Accepted by IEEE Access on Oct. 25, 2021

Based on a Double Deep-Q Network with deep ResNet (DDQN-ResNet), this paper proposes a novel method for transmission network expansion planning (TNEP). Since TNEP is a large scale and mixed-integer linear programming (MILP) problem, as the transmission network scale and the optimal constraints increase, the numerical calculation and heuristic learning-based methods suffer from heavy computational complexities in calculation and training. Besides, due to the black box characteristic, the solution processes of the heuristic learning-based methods are inexplicable and usually require repeated training. By using DDQN-ResNet, this paper constructs a high-performance and flexible method to solve large-scale and complex-constrained TNEP problem. Firstly, we form a two-objective TNEP model, in which one objective is to minimize the comprehensive cost, and another objective is to maximize the transmission network reliability. The comprehensive cost takes into account the expansion cost, the network loss cost, and the maintenance cost. The transmission network reliability is evaluated by the expected energy not served (EENS) and the electrical betweenness. Secondly, from the TNEP model, the TNEP task is constructed based on the Markov decision process. By abstracting the task, the TNEP environment is obtained for DDQN-ResNet. In addition, to identify the construction value of lines, an agent is establish based on DDQN-ResNet. Finally, we perform the static planning and visualize the reinforcement learning process. The dynamic planning is realized by reusing the training experience. The validity and flexibility of DDQN-ResNet are verified on RTS 24-bus test system.

Supply chain security is becoming an important factor in security risk analysis for modern information and communication technology (ICT) systems. As Internet of Things (IoT) devices proliferate and get adopted into critical infrastructure, the role of suppliers in risk assessment becomes all the more significant. IoT security risks are affected by supplier trust since suppliers possess the capacity to modify black box systems without detection. The risks posed by potentially malicious or compromised suppliers are compounded by interdependence among suppliers. In this paper, we propose I-SCRAM, a framework to analyze supply chain risks in IoT systems and to support risk mitigating decisions. After defining an expanded system model that consists of interconnected components and a hierarchy of component vendors, we develop and propose metrics to quantify systemic risks. Finally, we present a decision framework that helps in selection of vendors to mitigate supply chain risk. Through a case study and simulation, we show that I-SCRAM successfully minimizes system risk as higher budget and more reliable component sources become available, while allowing flexibility in prioritizing sources of risk.

Mid-air collisions (MACs) are commonly caused by temporary or permanent loss of flight control resulting in catastrophic damages and hence must be countered with suitable technology. The avionic industry has been using Traffic Alert and Collision Avoidance System (TCAS) for avoiding mid-air collisions since 1987. The TCAS either uses an Omni-directional antenna or a directional antenna for the surveillance of other nearby aircraft. The existing TCAS directional antennas, i.e., an array of monopole antennas are characterized by a maximum gain of 3.6 dB, which is regarded as low. As a result, many research has been carried out to find the alternative for the TCAS antenna and have found microstrip patch antenna (MPA) which yield some promising results such as feed flexibility, multiband, beam steering, etc. However, the MPA still suffers from disadvantages such as low gain and narrow bandwidth. Hence, lately, a tremendous amount of work has been carried out to enhance the gain of the MPA. In this paper, we first review the key methods that has been reported so far to improve the gain of the traditional TCAS antenna. We note that, in most of the reported work, conventional techniques such as parasitic patches, and shorting pins are used to improve the gain of MPA, and no substantial work was done using advanced techniques such as electromagnetic-band gap (EBG), metamaterials and metasurfaces. Hence, we further review the work that has been carried out to improve the gain of vertically polarized MPAs using advanced techniques for L- and S-band (up to 3 GHz). Many advanced structures that are easily adapted to enhance the gain of TCAS antenna are thoroughly discussed in this work.

Emergency crowd evacuation, especially in congested indoor scenes, is an important issue in public areas' daily management. Computer simulation is a widely adopted technique to study crowd evacuations and help design reasonable emergence plans due to its flexibility, convenience, and cost-effectiveness. In this paper, we propose ECEM, a novel evacuation model based on agent simulation. In ECEM, we consider a special individual behavior in the evacuation, called seeking behavior, which happens when relatives or friends (i.e., social groups) are in the crowd, and the members within a group may tend to seek each other once separated instead of evacuating alone. Moreover, we incorporate the crowd chaos based on Boltzmann entropy (i.e., crowd entropy) into ECEM to measure the evacuating population's disorder level and present an adaptive velocity smoothing method using crowd entropy for updating individual's velocity. Extensive simulation results demonstrate the effectiveness of ECEM and provide several insights on designing the evacuation strategies.

International audience; Wearable devices are ideal for personalized electronic applications in several domains such as healthcare, entertainment, sports and military. Although wearable technology is a growing market, current wearable devices are predominantly battery powered accessory devices, whose form factors also preclude them from utilizing the large area of the human body for spatiotemporal sensing or energy harvesting from body movements. E-textiles provide an opportunity to expand on current wearables to enable such applications via the larger surface area offered by garments, but consumer devices have been few and far between because of the inherent challenges in replicating traditional manufacturing technologies (that have enabled these wearable accessories) on textiles. Also, the powering of e-textile devices with battery energy like in wearable accessories, has proven incompatible with textile requirements for flexibility and washing. Although current e-textile research has shown advances in materials, new processing techniques, and one-off e-textile prototype devices, the pathway to industry scale commercialization is still uncertain. This paper reports the progress on the current technologies enabling the fabrication of e-textile devices and their power supplies including textile-based energy harvesters, energy storage mechanisms, and wireless power transfer solutions. It identifies factors that limit the adoption of current reported fabrication processes and devices in the industry for mass-market commercialization.

Networked control systems have advantages such as flexibility, efficiency, but at the same time they are exposed to cyberattacks. Among many lethal attacks, the zero-dynamics attack is a model based attack and it is very hard to detect. In this paper, a new strategy for intrusion detection and defense against zero-dynamics attack is proposed, and it is based on the generalized sampler that takes a weighted average of multiple samples obtained during one sampling interval. By using the generalized sampler instead of the simple sampler, the zeros of the sampled-data system can be placed at arbitrary locations, and if all zeros are placed inside the unit circle, the attack signal becomes no longer effective. This strategy still works even if all the information is exposed to hackers and it is considerably insensitive to the shift of intrinsic zeros. A design procedure for the generalized sampler is provided under mild assumptions. Furthermore, optimal designs for the selection of desired zeros are formulated considering practical issues. Theoretical findings are validated through numerical simulations.

Publisher Copyright: Author Recently, renewable energy sources (RESs) have increasingly being integrated into the power grids as a result of environmental and governmental perspectives. In this regard, the installation of RESs as potential power sources in active distribution systems would benefit the grid by decreasing the power losses, as well as addressing the fossil fuel shortages, and their environmental aspects. Nevertheless, the high-level integration of RESs as well as the development of distributed formations in local systems could challenge the reliable operation of the grid. In this context, conventional approaches could not optimally mitigate the regulation-voltage issue; therefore, utilities have to exploit the scheduling of local responsive sources to address the regulation-voltage issue in systems with high-level penetration of RESs. Consequently, the offered scheme in this paper enables the system operator to activate flexibility service from local responsive sources with the aim of addressing the regulation-voltage issue in the grid. Respectively, the system operator as the leader provides incentive control signals to ensure collaboration of the independent agents in voltage regulating procedure. Finally, the developed framework is applied on the 37-bus IEEE test network to investigate its application in mitigating the regulation-voltage issue in the active distribution systems with multi-agent formations.

Industrial control systems control and supervise our most important and critical infrastructures, such as power utilities, clean water plants and nuclear plants, as well as the manufacturing industries at the base of our economy. These systems are currently undergoing a transformation driven by the Industry 4.0 evolution, characterized by increased connectivity and flexibility. Consequently, the cybersecurity threat landscape for industrial control systems is evolving as well. Current strategies used for access control within industrial control systems are relatively rudimentary. It is evident that some of the emerging cybersecurity threats related to Industry 4.0 could be better mitigated using more fine-grained access control policies. In this article we discuss and describe a number of access control strategies that could be used within manufacturing systems. We evaluate the strategies in a simulation experiment, using a number of attack-scenarios. Moreover, a method is outlined for automatic policy-generation based on engineering-data, which is aligned with one of the best performing strategies.

Increasing flexibility and efficiency of energy-intensive industrial processes is generally seen as a big lever towards a decarbonized energy system of the future. However, to leverage these potentials, the accurate prediction of unit behavior is essential to be able to close the gap between supply and demand. Not only pose nonlinear relations a serious challenge in thermal systems engineering and optimization but real-world unit behavior furthermore changes during operation due to wear, fouling and other effects. In the present work, a novel framework for automated data-driven model adaption is presented which is capable of automating fast and accurate predictions of current system behavior. The framework is based on open protocol bidirectional live communication and mechanistic grey box modeling. While especially thermal energy storage is considered a solution to increase flexibility, it is very challenging for operation optimization. A packed bed thermal energy storage operated under severe conditions leading to continuous fouling acts as proof of concept of the proposed framework. The obtained results indicate major improvement for storage output prediction with the novel framework compared to a conventional approach without readjustment. Furthermore, the presented framework is perfectly suitable and an essential foundation for live condition monitoring, fault prediction, predictive maintenance, and operation optimization.

The structural mass of a building provides inherent thermal storage capability. Through sector coupling, the building mass can provide additional flexibility to the electric power system, using, for instance, combined heat and power plants or power-to-heat. In this work, a mathematical model of building inertia thermal energy storage (BITES) for integration into optimized smart grid control is introduced. It is shown how necessary model parameters can be obtained using generalized additive modeling (GAM) based on measurable building data. For this purpose, it is demonstrated that the ceiling surface temperature can serve as a proxy for the current state of energy. This allows for real-world implementation using only temperature sensors as additionally required hardware. Compared with linear modeling, GAM enable improved modeling of the nonlinear characteristics and interactions of external factors influencing the storage operation. Two case studies demonstrate the potential of using building storage as part of a virtual power plant (VPP) for optimized smart grid control. In the first case study, BITES is compared with conventionally used hot water tanks, revealing economic benefits for both the VPP and building operator. The second case study investigates the potential for savings in CO2 emission and grid connection capacity. It shows similar benefits when using BITES compared to using battery storage, without the need for hardware investment. Given the ubiquity of buildings and the recent advances in building control systems, BITES offers great potential as an additional source of flexibility to the low-carbon energy systems of the future.

Softwarization has been deemed as a key feature of 5G networking in the sense that the support of network functions migrates from traditional hardware-based solutions to software based ones. While the main rationale of 5G softwarization is to achieve high degree of flexibility/ programmability as well as reduction of total cost of ownership (TCO), it remains an interesting but significant issue on how to strike a desirable balance between system openness and necessary standardization in the context of 5G. The aim of this article is to systematically survey relevant enabling technologies, platforms and tools for 5G softwarization, together with ongoing standardization activities at relevant SDOs (Standards Developing Organizations). Based on these, we aim to shed light on the future evolution of 5G technologies in terms of softwarization versus standardization requirements and options.

The need to improve power system performance, enhance reliability, and reduce environmental effects, as well as advances in communication infrastructures, have led to demand response (DR) becoming an essential part of smart grid operation. DR can provide power system operators with a range of flexible resources through different schemes. From the operational decision-making viewpoint, in practice, each scheme can affect the system performance differently. Therefore, categorizing different DR schemes based on their potential impacts on the power grid, operational targets, and economic incentives can embed a pragmatic and practical perspective into the selection approach. In order to provide such insights, this paper presents an extensive review of DR programs. A goal-oriented classification based on the type of market, reliability, power flexibility and the participants’ economic motivation is proposed for DR programs. The benefits and barriers based on new classes are presented. Every involved party, including the power system operator and participants, can utilize the proposed classification to select an appropriate plan in the DR-related ancillary service ecosystem. The various enabling technologies and practical strategies for the application of DR schemes in various sectors are reviewed. Following this, changes in the procedure of DR schemes in the smart community concept are studied. Finally, the direction of future research and development in DR is discussed and analyzed.

A conventional systematic satisfiability logic suffers from a nonflexible logical structure that leads to a lack of interpretation. To resolve this problem, the advantage of introducing nonsystematic satisfiability logic is important to improve the flexibility of the logical structure. This paper proposes Random 3 Satisfiability (RAN3SAT) with three types of logical combinations ( $k = 1, 3, k =2, 3$ , and $k =1$ , 2, 3) to report the behaviors of multiple logical structures. The different types of RAN3SAT enforced with Discrete Hopfield Neural Network (DHNN) are included with benchmark searching techniques, such as Exhaustive Search algorithm. Additionally, to strengthen and certify the behavior of the proposed model, we extensively conducted several performance evaluation metrics with a specific number of neurons. In particular, the experimental results revealed that RAN3SAT was able to be implemented in DHNN, and each logical combination has its characteristics. Nonetheless, RAN3SAT provides more neuron variations in the whole solution space. The proposed model can also be applied in real-world applications such as the logic mining approach since RAN3SAT consists of various logic combinations that behave as input language to transform raw data into informative output.