Xiangpei Hu

An Approach to Context-Aware Mobile Chinese Language Learning for Foreign Students

In recent years, there are a large number of foreign students coming to learn Chinese language. However due to the characters of Chinese language, it is usually difficult for them to learn. In order to provide a suitable mobile environment to learn Chinese language for foreign students, this paper presents an approach to Context-aware Mobile Chinese Language Learning (CAMCLL) for foreign students. The idea of CAMCLL approach is, to provide service guide for foreign students when they are out of school, and moreover the form of service will be provided as suitable sentences towards achieving the real world Chinese language practice. The architecture of CAMCLL is introduced. A context model including four contexts (time, location, activity, and learner level) is proposed. The ontology-based matching with the combination of rule-based matching is a special feature in building matching mechanism. The approach discussed will enhance the Chinese language learning efficiency and effects for foreign students.

An Approach to the Real-Time Risk Analysis for Hazardous Material Transportation

Real-time risk analysis is the critical point of monitoring, warning and re-routing HazardousMaterial Transportation (HMT). In this paper, we propose a real-time risk analysis approach based on Internet of Things (IoT), which dynamically evaluates the risk at discrete time pointsduring the whole transportation process.Probabilities of accidents are determined by both statistic accident data and real-time monitoring data collected by IoT techniques. To determine the potential consequences of accidents, Areal Location of Hazardous Atmospheres (ALOHA) is used to simulate the accident scenarios and estimate the influence areas. The population exposed can be calculated with the interface of Intelligent Traffic System (ITS). Furthermore, our approach provides a more accurate way to dynamically evaluate the risk of HMT.

SMS-based Mobile Recommendation System for Campus Recruitment in China

This paper presents a SMS-based recommendation system for campus recruitment in China, which can help college placement office to match the companies and students with higher precision and lower cost. We are mainly focusing on profile matching and preference-list-based two-sided matching for further recommendation. With regard to profile matching, three kinds of matching methods (i.e., semantic matching, tree-based knowledge matching and SMS-based query matching) are integrated according to representations of attributes of students and companies, and then the profile similarity degree is acquired. Another focus is to provide two-sided matching from the perspective of central bureau (college placement office). Based on profile similarity degree, the preference lists of companies and students are calculated, which serves as the input of two-sided matching. With the loop matching triggered by the information of SMS-based query interaction, the matching results would be further optimized and provide more effective guidance for recommendation. The new system embedding SMS-based interaction can raise the matching degree, shorten recruiting period and reduce cost. Furthermore, this recommendation service not only is applicable in the field of campus recruitment, but also can provide a framework for the field of mobile business with the extension to other domains such as hospital-intern and college-student matching and recommendation.

A Context-based Support System of Mobile Chinese Learning for Foreigners in China☆

Abstract Taking advantage of the improving Mobile Web technologies, mobile language-learning systems plays an important role for self-learning, especially in the case of working people. Nevertheless, existing systems do not support learners in studying or practicing language materials related with the current context. As it is known, the context related with surroundings can deepen the impressions and naturally explain the meanings of the sentences in a learners memory, which leads to an easy and casual language-learning experience. To achieve this goal, a context-based support system of mobile Chinese learning for foreigners in China is proposed by this research. The system can provide a learner with Chinese-learning materials related with what he is doing or what he is going to do. Besides, it also can generally adapt to learners’ profiles like many existing personalized e-learning systems. In the system, a context-based ontology is investigated to represent the knowledge that is used to support the process of matching context with suitable language-learning materials, and inference rules in the matching process are developed by Semantic Web Rule Language (SWRL). Four steps of the matching process are realized based on the knowledge representation method and the rules. The implementation of the prototype system shows that it can support the mobile Chinese-learning process by providing a learner with suitable learning materials related with the contexts of the task he/she is doing or going to do, his/her profile and mobile device, so that it can greatly improve the effect and efficiency of learning.

A Heuristic Algorithm and a System for Vehicle Routing with Multiple Destinations in Embedded Equipment

A heuristic algorithm appropriate for vehicle routing problem with multiple destinations in embedded equipment is presented. The algorithm is applied to the local routing and global routing respectively. Based on the consideration of road conditions and special direction roads (one-way street, banning of turn in crossing, etc.), a routing system adopting the algorithm is developed in embedded equipment ARM9. Experimental results from the routing system prove the efficiency of the presented heuristic algorithm in embedded equipment.

The IoT-based heart disease monitoring system for pervasive healthcare service

In China, most of heart attack results in death before the patients get any treatment. Because the traditional healthcare mode is passive, by which patients call the healthcare service by themselves. Consequently, they usually fail to call the service if they are unconscious when the heart disease attacks. The Internet of Things (IoT) techniques have overwhelming superiority in solving the problem of heart diseases patients care as they can change the service mode into a pervasive way, and trigger the healthcare service based on patients physical status rather than their feelings. In order to realize the pervasive healthcare service, a remote monitoring system is essential. In this paper, we proposed a pervasive monitoring system that can send patients physical signs to remote medical applications in real time. The system is mainly composed of two parts: the data acquisition part and the data transmission part. The monitoring scheme (monitoring parameters and frequency for each parameter) is the key point of the data acquisition part, and we designed it based on interviews to medical experts. Multiple physical signs (blood pressure, ECG, SpO2, heart rate, pulse rate, blood fat and blood glucose) as well as an environmental indicator (patients location) are designed to be sampled at different rates continuously. Four data transmission modes are presented taking patients risk, medical analysis needs, demands for communication and computing resources into consideration. Finally, a sample prototype is implemented to present an overview of the system.

Optimal policies of a two-echelon serial inventory system with general limited capacities

We study optimal policies of capacitated two-echelon serial inventory systems under periodic review. For a system with smaller downstream capacity, we fully characterise the optimal policy as a further modified echelon base stock policy using an intuitive backward induction. The key lies in the magnitude relation between the initial upstream stock level and the downstream capacity. For a system with smaller upstream capacity, we demonstrate that the optimal policy is of a more complex structure where there can be at most four/five target levels up to which the upstream/downstream echelon tries to produce/order. The numbers of levels and their values depend on the length of remaining horizons and the amount of initial upstream inventories. We also specify these potential target levels and then suggest a way to simplify the search of optimal solutions.

A Mobile Service Application Platform with Chinese Short Message Based on Fuzzy Ontology for Employment of University Graduates

Employment of university graduates is important to the overall development of a country. Lack of an effective communication channel between labor supply and demand is one of the most important reasons that lead to the declining employment rate of university graduates. Aiming at this problem, a mobile service application platform is established in this paper. In order to handle uncertainty of employment information and knowledge, the platform adopts a fuzzy ontology model which is capable of fuzzy semantic retrieval and can select appropriate candidates from graduates for a job. Short messages generated from the mobile service application platform will be sent directly to the candidates who are selected by the fuzzy ontology model and likely to be employed by companies. Application of the platform shows that the fuzzy ontology model facilitates the semantic information retrieval and the platform increase the efficiency and accuracy of employment.

Tactical Production and Distribution Planning in Urban Logistics under Vehicle Operational Restrictions

Abstract Significant uncertainty associated with Chinese urban logistics, caused by random vehicle operational restrictions due to severe weather (e.g., smog) in addiction to normal traffic variation makes the tactical production and distribution planning decisions quite challenging. In this paper, we propose a two-stage stochastic integer programming model for an optimal production distribution capacity planning problem under the aforementioned uncertainties. We aim to minimize both procurement spending and the expected operational cost under logistic uncertainty. Given the computational burden of solving the resultant stochastic integer program for real-world instances, we develop an improved stochastic branch-and-bound (SBB) algorithm embedding with Tabu search method. We conduct the numerical study to verify the superiority of the proposed algorithm. We also offer managerial insights to practitioners and policy recommendations to municipal governments based on the numerical study results.

An Ontology-based Model for Typical-context Awareness in the Oil Products Distribution System

During the oil products distribution process, disruptions are diverse and can occur at different distribution states and the distribution state changes constantly, which makes it hard to capture the distribution context in real time. In this paper, an ontology-based model is developed, which consists of the disruption ontology, the distribution-state ontology and the SWRL rules. The model can capture the typical-context in real time and judge whether the original distribution plan is disrupted by the disruption under the current context, which is in favor of taking measures to respond to the disruption in real time.