The effect of lifetime on learning and forgetting in a supply chain inventory model with a service level constraint
تأثیر زندگی در یادگیری و فراموشی در مدل موجودی زنجیره تأمین با محدودیت سطح خدمات-2021
A supply chain inventory model for decaying goods has been established in this paper, assuming that the decaying items have a maximum life period. Uncertain Lead time is assumed taking into account the effect of learning-forgetting on ordering costs. Constraint on the quality of service is incorporated. Mathematical formulation has been performed for both the manufacturer and the supplier. Numerical examples are given to explain the proposed problem of seeking optimal recovery policies. Finally, a sensitivity analysis was carried out to study the influence of differences in various parameters.© 2021 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the 1st International Conference on Computations in Materials and Applied Engineering – 2021.
Keywords: Supply chain | Service level constraint | Learning and forgetting
Novel Four-Layered Software Defined 5G Architecture for AI-based Load Balancing and QoS Provisioning
نرم افزار جدید چهار لایه معماری 5G تعریف شده برای تعادل بار مبتنی بر هوش مصنوعی و تأمین کیفیت QoS -2020
Software defined 5G network (SD-5G) is an evolving networking technology. The integration of SDN and 5G brings scalability, and efficiency. However, Quality of Service (QoS) provision is still challenging in SD-5G due to improper load balancing, traffic unawareness and so on. To overwhelm these issues this paper designs a novel load balancing scheme using Artificial Intelligence (AI) techniques. Firstly, novel fourlayered SD-5G network is designed with user plane, smart data plane, load balancing plane, and distributed control plane. In the context to 5G, the data transmission rate must satisfy the QoS constraints based on the traffic type such as text, audio, video etc. Thus, the data from the user plane is classified by Smart Traffic Analyzer in the data plane. For traffic analysis, Enriched Neuro-Fuzzy (ENF) classifier is proposed. In the load balancing plane, Primary Load balancer and Secondary Load Balancer are deployed. This plane is responsible for balancing the load among controllers. For controller load balancing, switch migration is presented. Overloaded controller is predicted by Entropy function. Then decision for migration is made by Fitness-based Reinforcement Learning (F-RL) algorithm. Finally, the four-layered SD-5G network is modeled in the NS-3.26. The observations shows that the proposed work improves the SD-5G network in terms of Loss Rate, Packet Delivery Rate, Delay, and round trip time.
Keywords: QoS | software defined 5G network | Artificial intelligence | distributed control plane
Blockchain, AI and Smart Grids: The Three Musketeers to a Decentralized EV Charging Infrastructure
بلاکچین ،هوش مصنوعی و شبکه های هوشمند: سه تفنگدار به زیرساخت شارژ EV غیرمتمرکز-2020
The proliferation of Internet of Things (IoT) has brought an array of different services, from smart health-care, to smart transportation, all the way to smart cities. For a truly connected environment, different sectors need to collaborate. One use case of such overlap is between smart grids and Intelligent Transportation System (ITS) giving rise to Electric Vehicles and their charging infrastructure. Being such a lucrative opportunity for investors and the research community, many efforts have been made toward providing the end-user with an extraordinary Quality of Service (QoS). However, given the current protocols and deployment of the Electric Vehicle (EV) charging infrastructure, some key challenges still need to be addressed. In particular, we identify two main EV challenges: (1) vulnerable charging stations and EVs, and (2) non-optimal charging schedules. With these issues in mind, we evaluate the integration of Blockchain and AI with the EV charging infrastructure. Specifically, we discuss the current AI and Blockchain charging solutions available in the market. In addition, we propose a couple of use cases where both technologies complement each other for a secure, efficient and decentralized charging ecosystem. This article serves as starting point for stakeholders and policymakers to help identify potential directions and implementations of better charging systems for EVs.
Mining and Utilization of Special Information for Archives Management Based on 5G Network and Internet of Things
استخراج و استفاده از اطلاعات ویژه برای مدیریت بایگانی بر اساس شبکه 5G و اینترنت اشیا-2020
5G technology is currently in the process of demographic data, data mining, the next-generation mobile networks are considered to be one of the main factors. Through research and data analysis, are expected to overcome the complexity of these networks, and it will be possible to carry out dynamic management and business operations. It is a trade item in that category, which is a particular file. Data collection chosen field of study is the core part. These files are considered to know how it organize their files and save them for future posterity. Finally, deal with digitized archive material; these traditional archives sought to highlight the problems faced by the digital age. Issues related to critical skills of a digitized archive of documents as extended support for mobile telephone networks, and can be considered the next generation of ultra-fast 5G network technology. 5G network includes all kinds of advanced technology, to provide excellent service. Therefore, new architecture and applications of new technology service management solutions should be advised to resolve reliability issues and ensure data transmission capacity, high data rates, and Quality of services (QoS). Cloud computing, networking, as well as software-defined network technology are some of the core networks 5G. Cloud-based service, providing flexible and efficient solutions for information and communication technologies by reducing the cost of the investment and management of information technology infrastructure. In terms of functionality are decoupled control and data planes to support programmability, flexibility and adaptability in a changing network architecture promising architecture.
Keywords: Quality of services (QoS) | Internet of things (IoT) | Programmability | Flexibility | 5G network
Machine to machine performance evaluation of grid-integrated electric vehicles by using various scheduling algorithms
ارزیابی عملکرد ماشین به ماشین از وسایل نقلیه برقی شبکه یکپارچه با استفاده از الگوریتم های مختلف برنامه ریزی-2020
For smart cities, electric vehicles (EVs) are promisingly considered as a striving industry due to its pollution-less behaviours and easy-to-maintain characteristics. A seamless management system is necessary to manage the energy between EV and various parties participating in the grid operation. To facilitate the energy system in a distributed and coordinated way, a machine-to-machine (M2M) system can be considered as the key component in future intelligent transportation systems. Due to the ubiquitous range and data speed, a fourth-generation (4G) cellular-based long-term evaluation (LTE) system inspires us to select it as a potential carrier for M2M communication. However, various simulation and analytical modelling end up with the conclusion that the maximum 250 EVs can be connected under an LTE base station. These limitations or scalability limits may result in a terrible mix-up in future smart cities for over dense roads. In this paper, we measured various M2M quality of services performance for exceeding the number of EVs by using three popular algorithms (proportional fair scheduling, modified largest weighted delay first scheduling and exponential scheduling). The result shows that the proportional fair scheduler has the highest packet loss ratio (PLR) and delay time as compared to other two schedulers.
Keywords: DLS | Electric vehicle | Energy management system | EXP | M2M communication | M-LWDF | PF | PLR
Trustworthy AI in the Age of Pervasive Computing and Big Data
هوش مصنوعی قابل اعتماد در عصر محاسبات فراگیر و داده های بزرگ-2020
The era of pervasive computing has resulted in countless devices that continuously monitor users and their environment, generating an abundance of user behavioural data. Such data may support improving the quality of service, but may also lead to adverse usages such as surveillance and advertisement. In parallel, Artificial Intelligence (AI) systems are being applied to sensitive fields such as healthcare, justice, or human resources, raising multiple concerns on the trustworthiness of such systems. Trust in AI systems is thus intrinsically linked to ethics, including the ethics of algorithms, the ethics of data, or the ethics of practice. In this paper, we formalise the requirements of trustworthy AI systems through an ethics perspective. We specifically focus on the aspects that can be integrated into the design and development of AI systems. After discussing the state of research and the remaining challenges, we show how a concrete use-case in smart cities can benefit from these methods.
Index Terms: Artificial Intelligence | Pervasive Computing | Ethics | Data Fusion | Transparency | Privacy | Fairness | Accountability | Federated Learning
Does quality stimulate customer satisfaction where perceived value mediates and the usage of social media moderates?
آیا کیفیت در جایی که ارزش ادراک شده واسطه و استفاده از رسانه های متوسط است رضایت مشتری را تحریک می کند؟-2020
Customer is considered as the king in the world of business. The issue of customer satisfaction in electronics home appliances has received greater attention from academics and practitioners. In other words, customer satisfaction is a vital consideration in marketing. With the development of technology, new and innovative electronic home appliances are available in the market. Customers purchase and use the costly electronic home appliances where the satisfaction issue is an important concern. In Bangladesh, working families ﬁnd the electronic home appliance very necessary. Companies offer state-of- the-art appliances for customers household works. Therefore, the study intends to investigate the effect of product quality (PQ), quality of service (SQ) and perceived value on customer satisfaction (CS). In addition, this study also seeks this relationship shaped by customers perceived value (CPV) as a key mechanism and interacted by social media usage. A total of 300 households were selected on a judgmental basis from Dhaka city in Bangladesh using a structured questionnaire. Collected data were CB-SEM (AMOS-v24) and SPSS. The ﬁndings showed PQ and SQ have positive effects on CS; SQ affects, but PQ does not affect CPV. CPV has a mixing mediating effect on SQ and CS relationship and PQ and CS relationship. Importantly, the positive impact of PQ, SQ and CPV is greater on customers who exhibit higher social media use. The conceptual framework was buttressed by EDT theory. The study contributed to contextual and theoretical knowledge in regards to home appliances. The practicing managers can collect an insight of customer satisfaction for their business.
Keywords: Customer satisfaction | Social media usage | Customer perceived value | Quality of service | SEM-AMOS | Electronic home appliances | Moderated mediation | Bangladesh | Tourism | Information science | Business | Technology management | Management | Marketing | Consumer attitude | Research and development | Psychology
An analysis on the unified theory of acceptance and use of technology theory (UTAUT): Acceptance of electronic document management system (EDMS)
تجزیه و تحلیل نظریه واحد پذیرش و استفاده از تئوری فناوری (UTAUT): پذیرش سیستم مدیریت اسناد الکترونیکی (EDMS)-2020
Public Institutions need information systems that facilitate management of generated documents during business processes on a digital platform. Development of information and communication technologies facilitated the transfer of documents to digital platforms which caused the emergence of Electronic Document Management System (EDMS). Institutions are utilizing EDMS in order to keep records securely and improve business processes. EDMS have many beneﬁts such as improvement of efﬁciency and productivity, reduction of errors, increase in quality of service and reduction of costs. On the other hand, while EDMS offers many beneﬁts to its users, it also has made it imperative to adopt the new technological system. For this reason, it becomes essential to understand the factors that affect the intention of use of EDMS. This study researches the factors that affect the adoption and use of EDMS in Bartın University by using the uniﬁed theory of acceptance and use of technology (UTAUT). In this research, data was analyzed by using R software program and Structural Equation Modelling (SEM). Based on the ﬁndings, 61% of the intention of use of EDMS has been explained by performance expectancy and social inﬂuence factors with in the proposed model. Empirical ﬁndings suggest that the factors of performance expectancy and social inﬂuence has a positive effect on the intention of use but of effort expectancy factor does not have a positive effect.
Keywords: EDMS | UTAUT | Technology acceptance | Structural equation modelling
Internet of energy-based demand response management scheme for smart homes and PHEVs using SVM
اینترنت برنامه پاسخگویی به تقاضای مبتنی بر انرژی برای خانه های هوشمند و PHEV با استفاده از SVM-2020
The usage of information and communication technology (ICT) in the power sector has led to the emergence of smart grid (SG). The connected loads in SG are able to communicate their consumption data to the grid using ICT and thus forming a large Internet of Energy (IoE) network. However, various issues such as–increasing demand–supply gap, grid instability, and deteriorating quality of service persist in this network which degrade its performance. These issues can be handled in an efficient way by managing the demand response (DR) of different types of loads. For this purpose, cloud computing can be leveraged to gather the data generated in IoE network and perform analytics to manage DR. Working in this direction, a novel scheme to handle the DR of smart homes (SHs) and plug-in hybrid electric vehicles (PHEVs) is presented in this paper. The proposed scheme is based on analyzing the demand of these users at the cloud server for flattening the overall load profile of grid. This scheme is divided into two hierarchical stages which work as follows. In the first stage, the residential and PHEV users are identified whose demands can be regulated. This task is achieved with the help of a binary-class support vector machine (SVM) which uses Gaussian kernel function to classify these users. In the next stage, the load in SHs is curtailed on the basis of a pre-defined rule-base after analyzing the consumption data of various devices; whereas PHEVs are managed by controlling their charging rates. The efficacy of proposed scheme has been tested on PJM benchmark data and Open Energy Information dataset. The simulation results prove that the proposed scheme is effective in maintaining the overall load profile of SG by managing the DR of SHs and PHEV users.
Keywords: Data analytics | Demand response | Plug-in hybrid electric vehicles | Smart grid | Smart homes | Support vector machine
Reinforcement learning in sustainable energy and electric systems: a survey
یادگیری تقویتی در سیستم های انرژی پایدار و الکتریکی: یک نظرسنجی-2020
The dynamic nature of sustainable energy and electric systems can vary significantly along with the en- vironment and load change, and they represent the features of multivariate, high complexity and uncer- tainty of the nonlinear system. Moreover, the integration of intermittent renewable energy sources and energy consumption behaviours of households introduce more uncertainty into sustainable energy and electric systems. The operation, control and decision-making in such an environment definitely require increasing intelligence and flexibility in the control and optimization to ensure the quality of service of sustainable energy and electric systems. Reinforcement learning is a wide class of optimal control strate- gies that uses estimating value functions from experience, simulation, or search to learn in highly dy- namic, stochastic environment. The interactive context enables reinforcement learning to develop strong learning ability and high adaptability. Reinforcement learning does not require the use of the model of system dynamics, which makes it suitable for sustainable energy and electric systems with complex non- linearity and uncertainty. The use of reinforcement learning in sustainable energy and electric systems will certainly change the traditional energy utilization mode and bring more intelligence into the system. In this survey, an overview of reinforcement learning, the demand for reinforcement learning in sustain- able energy and electric systems, reinforcement learning applications in sustainable energy and electric systems, and future challenges and opportunities will be explicitly addressed.
Keywords: Reinforcement learning | Sustainable energy and electric systems | Deep reinforcement learning | Power system | Integrated energy system