真空冷冻干燥装置制冷系统的设计

基于真空冷冻干燥装置的推广应用,讨论了该装置制冷量的确定方法,给出了常见冻干食品的蒸发温度为－３５～－４０℃,可满足其降温要求,介绍了该装置近阶段优先选择的制冷剂为Ｒ７１７、Ｒ２２,优先选择的制冷机为单、双机压缩冷凝制冷机组,或带经济器的螺杆式低温压缩机组,为进一步工程应用提供设计依据     

关于热风式横流圆筒烘干机几个问题的讨论

提出了热风式横流圆筒烘干机在附属设备及生产工艺上存在的几个主要问题关分析了产生的原因,为该机的设计制造,推广使用技术改造提供一定有益的帮助。     

Combined planning and scheduling in a divergent production system with co-production: A case study in the lumber industry

Many research initiatives carried out in production management consider process planning and operations scheduling as two separate and sequential functions. However, in certain contexts, the two functions must be better integrated. This is the case in divergent production systems with co-production (i.e. production of different products at the same time from a single product input) when alternative production processes are available. This paper studies such a context and focuses on the case of drying and finishing operations in a softwood lumber facility. The situation is addressed using a single model that simultaneously performs process planning and scheduling. We evaluate two alternative formulations. The first one is based on mixed integer programming (MIP) and the second on constraint programming (CP). We also propose a search procedure to improve the performance of the CP approach. Both approaches are compared with respect to their capacity to generate good solutions in short computation time.     

Social evaluations of embodied agents and avatars

The purpose of this study was to examine social evaluations (i.e., perceptions of empathy and positivity) following peoples’ interactions with digital human representations. Female research participants engaged in a 3-min interaction while immersed in a 3-D immersive virtual environment with a “peer counselor.” Participants were led to believe that the peer counselor was either an embodied agent (i.e., computer algorithm) or an avatar (i.e., another person). During the interaction, the peer counselor either smiled or not. As predicted, a digitally-rendered smile was found to affect participants’ social evaluations. However, these effects were moderated by participants’ beliefs about their interaction partner. Specifically, smiles enhanced social evaluations of embodied agents but degraded them for avatars. Although these results are consistent with other findings concerning the communicative realism of embodied agents and avatars they uniquely demonstrate that people’s beliefs alone, rather than actual differences in virtual representations, can impact social evaluations.     

An agent-based signal processing in-node environment for real-time human activity monitoring based on wireless body sensor networks

Nowadays wireless body sensor networks (WBSNs) have great potential to enable a broad variety of assisted living applications such as human biophysical/biochemical control and activity monitoring for health care, e-fitness, emergency detection, emotional recognition for social networking, security, and highly interactive games. It is therefore important to define design methodologies and programming frameworks which enable rapid prototyping of WBSN applications. Several effective application development frameworks have been already proposed for WBSNs designed for TinyOS-based sensor platforms, e.g. CodeBlue, SPINE, and Titan. In this paper we present an application of MAPS, an agent framework for wireless sensor networks based on the Java-programmable Sun SPOT sensor platform, for the development of a real-time WBSN-based system for human activity monitoring. The agent-oriented programming abstractions provided by MAPS allow effective and rapid prototyping of the sensor-side software. In particular, the architecture of the developed system is a typical star-based WBSN composed of a coordinator node and two sensor nodes located respectively on the waist and the thigh of the monitored assisted living. The coordinator relies on a JADE-based enhancement of the SPINE coordinator and allows configuring sensors, receiving their data, and recognizing pre-defined human activities. On the other hand, each sensor node runs a MAPS-based agent that performs sensing of the 3-axial accelerometer sensor, computation of significant features on the acquired data, feature aggregation and transmission to the coordinator. The experimentation phase of the prototype, which allows evaluating the obtainable monitoring performances and activity recognition accuracy, is described. Moreover, a comparison of the monitoring system based on MAPS, AFME and SPINE in terms of programming effectiveness and system performances is discussed.     

Intelligent assistance for teachers in collaborative e-learning environments

Collaborative learning environments provide a set of tools for students acting in groups to interact and accomplish an assigned task. In this kind of systems, students are free to express and communicate with each other, which usually lead to collaboration and communication problems that may require the intervention of a teacher. In this article, we introduce an intelligent agent approach to assist teachers through monitoring participations made by students within a collaborative distance learning environment, detecting conflictive situations in which a teacher’s intervention may be necessary. High precision rates achieved on conflict detection scenarios suggest great potential for the application of the proposed rule-based approach for providing personalized assistance to teachers during the development of group works.     

Simulated environments with animated agents: effects on visual attention,emotion, performance,and perception

This research assessed how emotive animated agents in a simulation‐based training affect the performance outcomes and perceptions of the individuals interacting in real time with the training application. A total of 56 participants consented to complete the study. The material for this investigation included a nursing simulation in which participants interacted with three animated agents. The results of this investigation indicated that both experienced and novice participants focused more visual attention time on the body of the animated agent than the other defined areas of interest in the simulated environment. The results also indicated that novice participants conveyed more neutral facial expressions during the interaction with the animated agents than experience participants. The results of the simulation performance scores indicated that novice participants achieved higher simulation performance scores on the simulation task than experienced participants. Lastly, the results of the agent persona instrument showed that experienced and novice participants perceived the animated agents as facilitators of learning, credible, human‐like and engaging.     

Reliable representations for association rules

Association rule mining has contributed to many advances in the area of knowledge discovery. However, the quality of the discovered association rules is a big concern and has drawn more and more attention recently. One problem with the quality of the discovered association rules is the huge size of the extracted rule set. Often for a dataset, a huge number of rules can be extracted, but many of them can be redundant to other rules and thus useless in practice. Mining non-redundant rules is a promising approach to solve this problem. In this paper, we first propose a definition for redundancy, then propose a concise representation, called a Reliable basis, for representing non-redundant association rules. The Reliable basis contains a set of non-redundant rules which are derived using frequent closed itemsets and their generators instead of using frequent itemsets that are usually used by traditional association rule mining approaches. An important contribution of this paper is that we propose to use the certainty factor as the criterion to measure the strength of the discovered association rules. Using this criterion, we can ensure the elimination of as many redundant rules as possible without reducing the inference capacity of the remaining extracted non-redundant rules. We prove that the redundancy elimination, based on the proposed Reliable basis, does not reduce the strength of belief in the extracted rules. We also prove that all association rules, their supports and confidences, can be retrieved from the Reliable basis without accessing the dataset. Therefore the Reliable basis is a lossless representation of association rules. Experimental results show that the proposed Reliable basis can significantly reduce the number of extracted rules. We also conduct experiments on the application of association rules to the area of product recommendation. The experimental results show that the non-redundant association rules extracted using the proposed method retain the same inference capacity as the entire rule set. This result indicates that using non-redundant rules only is sufficient to solve real problems needless using the entire rule set.