Sheng-Yuan Chien

An Intelligent Energy Management Mechanism for Electric Vehicles

ABSTRACT Electric vehicles (EVs) have become increasingly popular all over the world in recent years. Many countries have been offering reward policies and facilitating the establishment of EV charging stations and battery exchange stations to encourage use of these vehicles by the public. However, in terms of electricity demand, the rapid establishment of EV charging stations and battery exchange stations may lead to significant increases in peak loads, the contracted capacities, and basic electricity charges. In this work, an intelligent EV energy management mechanism is proposed to make use of scheduling systems for the charging stations in order to determine when to store electricity in batteries according to the real-time electricity price and the recharging requirements of EVs. Meanwhile, a recharging suggestion module is presented in this work for locating the most suitable charging station or battery exchange station for an EV according to the available information on hand. When an EV cannot reach any charging station because it is running out of electric power, a mobile CV management module is used to assist the EV to find a suitable mobile CV for recharging. Notably, a well-known machine learning technique, multiobjective particle swarm optimization, was employed in this work to assist in solving the multiobjective optimization problems during the design of an energy management mechanism. The experimental results show that the proposed mechanism can balance the loading of battery charging and exchange stations, and lower the load peak to keep electricity cost down. Meanwhile, the recharging suggestion module can decrease the driving distance of EVs for finding the charging stations, as well as decreasing the waiting time wasted while charging. The mobile CV management module, for its part, can effectively prevent EVs from becoming stranded on the road because they have run out of electricity.

A group intelligence-based asynchronous argumentation learning-assistance platform

Structured argumentation support environments have been built and used in scientific discourse in the literature. However, to the best our knowledge, there is no research work in the literature examining whether student’s knowledge has grown during learning activities with asynchronous argumentation. In this work, an intelligent computer-supported collaborative argumentation-based learning platform that detects whether the learners address the expected discussion issues is proposed. After each learner presents an argument, a term weighting method is adopted to derive input parameters of a one-class support vector machines classifier which determines if the learners’ arguments are related to the discussion topics. Notably, a peer review mechanism is established to improve the quality of the classifier. Besides, a feedback module is used to issue feedback messages to the learners if the learners have gone off on a tangent. The experimental results revealed that the students were benefited by the proposed learning-assistance platform.

A self-adaptive joint bandwidth allocation scheme for heterogeneous wireless networks

Our approach coordinately manages radio resources with multiple radio access technologies in an optimum way. A mobility prediction module and a remaining bandwidth estimation module are established at each BS as shown in the lower part of the above figure. The global radio resource manager located at the upper part of this figure consists of a bandwidth utilization optimization module. A mobile host (MH) collects required parameters and the latest MHs state is immediately updated at the BS the MH resides in when a MHs state changes. The mobility prediction module will check with a pre-built lookup table to determine whether the handoff will occur. If the handoff is predicted to occur, the MH sends out a bandwidth requirement to a nearest BS, and the remaining bandwidth estimation module in the target BS will be activated to determine the amount of remaining bandwidth that can be used by the handoffs. In case some BS finds that its bandwidth is going to be inadequate, a bandwidth adjustment coordinator located at the BS will request the common bandwidth utilization optimization module at its upper level to reallocate the bandwidth for the BSs/APs of different RATs. The results of the bandwidth reallocation will then be sent back to each BS to reassign the bandwidth to the MHs. A mobility prediction module is proposed to predict the mobility pattern of mobile host.A lookup table records the probability for the possible handoffs moving into the coverage of the BS.Bandwidth is estimated for the possible arriving MHs outside the coverage of the BS.The optimization of utilization and fairness for the bandwidth allocation are addressed.Hybrid Genetic Algorithm is employed to achieve real-time computation requirement. Recently, people have been able to connect with different types of networks anytime, anywhere using advanced network technologies. In order to properly distribute wireless network resources among different clients, this work proposed a user mobility prediction algorithm, which takes the coverage of different kinds of base stations, and the volatile mobility of pedestrians, vehicles, and mass transportation, into consideration. In addition, a novel bandwidth utilization optimization technique is proposed in the algorithm to allocate bandwidth more efficiently. The Hybrid Genetic Algorithm, which combines the Genetic Algorithm and local searches to improve the frequency of finding a Pareto set, is used to realize the optimization problem as well. Compared with our previous work and the other four methods in the literature, the simulation results show that our proposed work can achieve desirable performance by network utilization, throughput, and QoS quality in the heterogeneous wireless networks.

An Adaptive Live Streaming Mechanism for Heterogeneous Vehicular Networks

With the popularity of vehicular networks, how to maintain high quality of the seamless live streaming service is a great challenge. In this work, an adaptive seamless live streaming dissemination system for heterogeneous vehicular networks to tackle this challenging issue is presented. First, differential service is presented in this work to ensure that paid users can satisfy the live steaming service. Based on users’ service-level agreement, an adaptive bandwidth allocation policy is proposed in this work to attain seamless handoff. In addition, because vehicles enter into the areas of hotspots, we also present a mechanism not only to prevent the insufficient bandwidth in advance, but also to make sure the paid users have higher priority than free users to obtain the seamless streaming service. When an unavoidable congestion occurs, we compress the streaming videos with a Region of Interest approach based on the content and characters of the video in order to maintain the service quality of paid users and reduce the required bandwidth for the streaming services. A series of simulation results show that our mechanism achieves better performance in terms of bandwidth utilization, packet loss ratio, and blocking probability. The capability of self-adaption in volatile real-time vehicular environment assists in the effectiveness and practicability of our proposed approach.

An Intelligent Infrared LED-Based Prosthesis Training System

In the past, the utilization of limb prostheses has improved the daily life of both amputees and patients with movement disorders. However, prior to achieving this improvement, a leg amputee must undertake a series of training sessions while wearing a limb prosthesis whereby the training results determine whether a patient will be able to use the limb prosthesis correctly in her/his daily life. Limb prostheses vendors therefore desire to offer the leg amputee a complete and well-organized training procedure, but they often fail to do so owing to factors related to limited human resource support and the financial constraints of the amputee. This work proposes a prosthesis training system that amputees can borrow or buy from the limb prostheses vendors to enable independent training at home. In this prosthesis training system, 3D positioning information is obtained via infrared LEDs. Four features are extracted and fed into a classifier to determine the actual conditions for the leg-amputee during training. Experimental results exhibited the effectiveness and practicality of the proposed prosthesis training system.