Search Tree Generation for the Exception Handling of E-Commerce Delivery Process

A business process management system (BPMS) offers the facility to define new processes or update the existing processes. However, exceptional or non-routine tasks require the intervention of domain experts or generation of the situation specific resolution process. This paper assumes that sufficient amount of business process exception handling cases are stored in the process repository. Since the retrieval of the best exception handling process requires good understanding about the exceptional situation, context awareness is an important issue. To facilitate the representation of the exceptional situation and to enable the selection of the best exception handling process, we adopted the `situation variable' and `decision variable' construct. A case example for exception handling in the e-commerce delivery process is provided to illustrate how the proposed construct works. We applied the C5.0 algorithm to build the optimum search tree

Integration between WSNs and Internet based on Address Internetworking for Web Services

There has been an increasing interest in wireless sensor networks as a new technology to realize ubiquitous computing, and demands for internetworking technology between the wireless sensor networks and the Internet which is based on IP address. For this purpose, this paper proposes and implements the internetworking scheme which assigns IP addresses to the sensor nodes and internetworks based on the gateway-based integration for internetworking between the wireless sensor networks and the Internet. That is, the proposed scheme makes the access to the wireless sensor networks be serviced as like the Web service with internetworking Internet IP address and ZigBee address which is allocated to the sensor node in wireless sensor networks. For validating the proposed scheme, we made experiments using Berkeley TinyOS, Mica Motes, dual protocol stack based on ZigBee and IP, and showed the service result using browser (IE) and IPv6 address based on DNS

A novel framework for photovoltaic energy optimization based on supply–demand constraints

Introduction: Distributed power supply has increasingly taken over as the energy industry’s primary development direction as a result of the advancement of new energy technology and energy connectivity technology. In order to build isolated island microgrids, such as villages, islands, and remote mountainous places, the distributed power supply design is frequently employed. Due to government subsidies and declining capital costs, the configured capacity of new energy resources like solar and wind energy has been substantially rising in recent years. However, the new energy sources might lead to a number of significant operational problems, including over-voltage and ongoing swings in the price of power. Additionally, the economic advantages availed by electricity consumers may be impacted by the change in electricity costs and the unpredictability of the output power of renewable energy sources.Methods: This paper proposes a novel framework for enhancing renewable energy management and reducing the investment constraint of energy storage. First, the energy storage incentive is determined through a bi-level game method. Then, the net incentive of each element is maximized by deploying a master–slave approach. Finally, a reward and punishment strategy is employed to optimize the energy storage in the cluster.Results: Simulation results show that the proposed framework has better performance under different operating conditions.Discussion: The energy storage operators and numerous energy storage users can implement master–slave game-based energy storage pricing and capacity optimization techniques to help each party make the best choices possible and realize the multi-subject interests of energy storage leasing supply and demand win–win conditions

Prospective submodule topologies for MMC-BESS and its control analysis with HBSM

Battery energy storage systems and multilevel converters are the most essential constituents of modern medium voltage networks. In this regard, the modular multilevel converter offers numerous advantages over other multilevel converters. The key feature of modular multilevel converter is its capability to integrate small battery packs in a split manner, given the opportunity to submodules to operate at considerably low voltages. In this paper, we focus on study of potential SMs for modular multilevel converter based battery energy storage system while, keeping in view the inconsistency of secondary batteries. Although, selecting a submodule for modular multilevel converter based battery energy storage system, the state of charge control complexity is a key concern, which increases as the voltage levels increase. This study suggests that the half-bridge, clamped single, and full-bridge submodules are the most suitable submodules for modular multilevel converter based battery energy storage system since, they provide simplest state of charge control due to integration of one battery pack along with other advantages among all 24 submodule topologies. Depending on submodules analysis, the modular multilevel converter based battery energy storage system based on half-bridge submodules is investigated by splitting it into AC and DC equivalent circuits to acquire the AC and DC side power controls along with an state of charge control. Subsequently, to validate different control modes, a downscaled laboratory prototype has been developed

Design and performance evaluation of a novel metamaterial broadband THz filter for 6G applications

Terahertz (THz) radiation, which has applications in the imaging of objects, non-destructive testing, satellite communication, medical diagnostics, and biosensing, has generated a great deal of attention due to its remarkable properties. This paper proposes a novel broadband filter for THz applications. The main idea is to overcome the insertion loss and bandwidth issues by modeling a frequency-domain finite difference method and guided-mode resonance (GMR). The optimal design scheme of the wideband pass filter based on the circular resonant ring is discussed by comparing the transmission parameters under various parameters. This scheme overcomes the restriction of the narrow passband bandwidth of the prior THz filters and achieves approximately 3 dB bandwidth of 0.54 THz. The proposed THz filter paper also has the advantages of a straightforward structure, low processing costs, and ease of conformal with other structures, and it can be used for stealth fighters, new communication technology, and precise instruments. In addition, when compared to existing models, the suggested filter offers higher 3 dB BW operation, increased transmittance, low insertion loss, and stable performance at various oblique angles

A Context-Aware Location Recommendation System for Tourists Using Hierarchical LSTM Model

The significance of contextual data has been recognized by analysts and specialists in numerous disciplines such as customization, data recovery, ubiquitous and versatile processing, information mining, and management. While a generous research has just been performed in the zone of recommender frameworks, by far most of the existing approaches center on prescribing the most relevant items to customers. It usually neglects extra-contextual information, for example time, area, climate or the popularity of different locations. Therefore, we proposed a deep long-short term memory (LSTM) based context-enriched hierarchical model. This proposed model had two levels of hierarchy and each level comprised of a deep LSTM network. In each level, the task of the LSTM was different. At the first level, LSTM learned from user travel history and predicted the next location probabilities. A contextual learning unit was active between these two levels. This unit extracted maximum possible contexts related to a location, the user and its environment such as weather, climate and risks. This unit also estimated other effective parameters such as the popularity of a location. To avoid feature congestion, XGBoost was used to rank feature importance. The features with no importance were discarded. At the second level, another LSTM framework was used to learn these contextual features embedded with location probabilities and resulted into top ranked places. The performance of the proposed approach was elevated with an accuracy of 97.2%, followed by gated recurrent unit (GRU) (96.4%) and then Bidirectional LSTM (94.2%). We also performed experiments to find the optimal size of travel history for effective recommendations

Topic Predictions and Optimized Recommendation Mechanism Based on Integrated Topic Modeling and Deep Neural Networks in Crowdfunding Platforms

The accelerated growth rate of internet users and its applications, primarily e-business, has accustomed people to write their comments and reviews about the product they received. These reviews are remarkably competent to shape customers’ decisions. However, in crowdfunding, where investors finance innovative ideas in exchange for some rewards or products, the comments of investors are often ignored. These comments can play a markedly significant role in helping crowdfunding platforms to battle against the bitter challenge of fraudulent activities. We take advantage of the language modeling techniques and aim to merge them with neural networks to identify some hidden discussion patterns in the comments. Our objective is to design a language modeling based neural network architecture, where Recurrent Neural Networks (RNN) Long Short-Term Memory (LSTM) is used to predict discussion trends, i.e., either towards scam or non-scam. LSTM layers are fed with latent topic distribution learned from the pre-trained Latent Dirichlet Allocation (LDA) model. In order to optimize the recommendations, we used Particle Swarm Optimization (PSO) as a baseline algorithm. This module helps investors find secure projects to invest in (with the highest chances of delivery) within their preferred categories. We used prediction accuracy, an optimal number of identified topics, and the number of epochs, as metrics of performance evaluation for the proposed approach. We compared our results with simple Neural Networks (NNs) and NN-LDA based on these performance metrics. The strengths of both integrated models suggest that the proposed model can play a substantial role in a better understanding of crowdfunding comments

Ultra-Short-Term Continuous Time Series Prediction of Blockchain-Based Cryptocurrency Using LSTM in the Big Data Era

This study uses the API of Upbit, one of Korea’s cryptocurrency exchanges, to predict continuous time series for a limited period and cryptocurrencies using LSTM, a machine learning technique. The trading (buying and selling) point algorithm presented in this study was used to conduct experimental research on efficient profit creation for cryptocurrency investment. Several related studies have shown the results of time series prediction for long-term forecasts, such as a week or several months. Still, they have not attempted to make an ultra-short-term prediction in units of one minute. This paper attempts such a 1 min prediction. This is an experiment to create efficient profits by setting efficient trading (buying and selling) points using machine learning techniques and repeating these operations by an algorithm. Applying it to cryptocurrency shows the possibility of time series prediction