An Ambient Intelligence-Based Human Behavior Monitoring Framework for Ubiquitous Environments

Thakur, Nirmalya; Han, Chia Y.

doi:10.3390/info12020081

Cited by 69 publications

(31 citation statements)

References 35 publications

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“…Two model types were used: unbalanced and balanced performance bonus models. In the unbalanced model, we removed the constraints on the performance bonuses described in Equations ( 23)- (25). Then, we used some simple examples to assess the increase in cost caused by adding the balancing constraint to the proposed model.…”

Section: Computational Resultsmentioning

confidence: 99%

An Auction Bidding Approach to Balance Performance Bonuses in Vehicle Routing Problems with Time Windows

Chen

Ying

et al. 2021

Sustainability

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In the field of operations research, the vehicle routing problem with time windows (VRPTW) has been widely studied because it is extensively used in practical applications. Real-life situations discussed in the relevant research include time windows and vehicle capabilities. Among the constraints in a VRPTW, the practical consideration of the fairness of drivers’ performance bonuses has seldom been discussed in the literature. However, the shortest routes and balanced performance bonuses for all sales drivers are usually in conflict. To balance the bonuses awarded to all drivers, an auction bidding approach was developed to address this practical consideration. The fairness of performance bonuses was considered in the proposed mathematical model. The nearest urgent candidate heuristic used in the auction bidding approach determined the auction price of the sales drivers. The proposed algorithm both achieved a performance bonus balance and planned the shortest route for each driver. To evaluate the performance of the auction bidding approach, several test instances were generated based on VRPTW benchmark data instances. This study also involved sensitivity and scenario analyses to assess the effect of the algorithm’s parameters on the solutions. The results show that the proposed approach efficiently obtained the optimal routes and satisfied the practical concerns in the VRPTW.

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Section: Computational Resultsmentioning

confidence: 99%

An Auction Bidding Approach to Balance Performance Bonuses in Vehicle Routing Problems with Time Windows

Chen

Ying

et al. 2021

Sustainability

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“…However, the algorithm currently being studied in this paper should only be used when the current controller cluster resources meet the current demand, and it does not consider the failure analysis and the current shortage of resources when the cluster demand is met. Therefore, in the future, according to [40], the use of intelligent algorithms combined with heterogeneous Internet of Things devices based on the situational awareness of multi-domain test network environment will be an important topic for research. Additionally, load balancing by dynamically adding controllers will become the focus of future research.…”

Section: Discussionmentioning

confidence: 99%

Multi-Controller Load Balancing Algorithm for Test Network Based on IACO

Zhu

Cao

et al. 2021

Symmetry

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With the rapid increase of volume and complexity in the projectile flight test business, it is becoming increasingly important to improve the quality of the service and efficiency of multi-domain cooperative networks. The key for these improvements is to solve the problem of asymmetric load of multi-controllers in multi-domain networks. However, due to the current reality, it is difficult to meet the demands of future tests, and there is not guarantee of subnet multi-domain test load balancing. Most recent works have used a heuristic approach to seek the optimal dynamic migration path, but they may fall into the local optimum. This paper proposes an improved ant colony algorithm (IACO) that can transform the modeling of the mapping relationship between the switch and the controller into a traveling salesman problem by combining the ant colony algorithm and artificial fish swarm algorithm. The IACO not only ensures the load balancing of multi-controllers but also improves the reliability of the cluster. The simulation results show that compared to other algorithms such as traditional ant colony algorithms and distributed decision mechanisms, this IACO achieves better load balancing, improves the average throughput of multi-controller clusters, and effectively reduces the response time of controller request events.

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“…Li et al [24] proposed a relation extraction model based on dual attention-guided graph convolutional network, and they used the dual attention mechanism to capture rich context dependencies and achieved better performances. Thakur et al [25] proposed a model of entity and relation extraction in IoT. Liu et al [26] proposed a relation extraction method based on CRF and the syntactic analysis tree, and created a military knowledge graph.…”

Section: Related Workmentioning

confidence: 99%

MRE: A Military Relation Extraction Model Based on BiGRU and Multi-Head Attention

Jiang

Zhou

et al. 2021

Symmetry

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A great deal of operational information exists in the form of text. Therefore, extracting operational information from unstructured military text is of great significance for assisting command decision making and operations. Military relation extraction is one of the main tasks of military information extraction, which aims at identifying the relation between two named entities from unstructured military texts. However, the traditional methods of extracting military relations cannot easily resolve problems such as inadequate manual features and inaccurate Chinese word segmentation in military fields, failing to make full use of symmetrical entity relations in military texts. With our approach, based on the pre-trained language model, we present a Chinese military relation extraction method, which combines the bi-directional gate recurrent unit (BiGRU) and multi-head attention mechanism (MHATT). More specifically, the conceptual foundation of our method lies in constructing an embedding layer and combining word embedding with position embedding, based on the pre-trained language model; the output vectors of BiGRU neural networks are symmetrically spliced to learn the semantic features of context, and they fuse the multi-head attention mechanism to improve the ability of expressing semantic information. On the military text corpus that we have built, we conduct extensive experiments. We demonstrate the superiority of our method over the traditional non-attention model, attention model, and improved attention model, and the comprehensive evaluation value F1-score of the model is improved by about 4%.

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An Ambient Intelligence-Based Human Behavior Monitoring Framework for Ubiquitous Environments

Cited by 69 publications

References 35 publications

An Auction Bidding Approach to Balance Performance Bonuses in Vehicle Routing Problems with Time Windows

An Auction Bidding Approach to Balance Performance Bonuses in Vehicle Routing Problems with Time Windows

Multi-Controller Load Balancing Algorithm for Test Network Based on IACO

MRE: A Military Relation Extraction Model Based on BiGRU and Multi-Head Attention

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