持续发展是唯一出路

讨论了人类、环境与发展的关系，指出不适当的发展模式已使三者之间形成恶性循环，必须及时地改变发展模式。列举了中国面临的严重环境问题，指出只有走可持续发展的道路才是唯一出路。并提出为实施可持续发展应注意的６个主要问题。     

Adaptive model predictive inventory controller for multiproduct warehouse system

An inventory control system has been developed for a distribution system consisting of a single multiproduct warehouse serving a set of customers and purchasing products from multiple vendors. Purchase orders requesting multiple products are delivered to the warehouse in a process referred to as joint replenishment. The receipt of customer orders by the warehouse proceeds in order intervals and in order quantities that are subject to random fluctuations. The objective of warehouse operation is to minimize the total cost while maintaining inventory levels within the warehouse capacity by adjusting the purchase order intervals and quantities. An adaptive model predictive control algorithm is developed using a periodic square wave model to represent the material flows. The adaptive concept incorporates a stabilized minimum variance control-type input calculation coupled with input/output stream parameter predictions. The boundedness of the control output under the suggested algorithm is mathematically proven under the assumption that disturbances in the orders are bounded. The effectiveness of the scheme was demonstrated using simulations.     

Interaction‐Feature Enhanced Multiuser Model Learning for a Home Environment Using Ambient Sensors

Activity recognition (AR) is a key enabler for a context‐aware smart home since knowing what the residents’ current activities helps a smart home provide more desirable services. This is why AR is often used in assistive technologies for cognitively impaired people to evaluate their abilities to undertake activities of daily living. In a real‐life scenario, multiple‐resident AR has been considered as a very challenging problem, primarily due to the complexity of data association. In addition, most prior research has not considered the potential interpersonal interactions among residents to simplify complexity, especially in an environment monitored by ambient sensors. In this study, we propose two types of multiuser activity models, both of which are derived from an interaction‐feature enhanced multiuser model learning framework. These two models consider interpersonal interactions and data association for multiuser AR using ambient sensors. We then compare their performance with the other two baseline models with or without consideration of data association and interpersonal interactions. The experimental results show that the derived models outperform other baseline classifiers. Therefore, the proposed approach can increase the opportunities for providing context‐aware services for a multiresident smart home.     

Domain Adaptation for Structured Regression

Discriminative regression models have proved effective for many vision applications (here we focus on 3D full-body and head pose estimation from image and depth data). However, dataset bias is common and is able to significantly degrade the performance of a trained model on target test sets. As we show, covariate shift, a form of unsupervised domain adaptation (USDA), can be used to address certain biases in this setting, but is unable to deal with more severe structural biases in the data. We propose an effective and efficient semi-supervised domain adaptation (SSDA) approach for addressing such more severe biases in the data. Proposed SSDA is a generalization of USDA, that is able to effectively leverage labeled data in the target domain when available. Our method amounts to projecting input features into a higher dimensional space (by construction well suited for domain adaptation) and estimating weights for the training samples based on the ratio of test and train marginals in that space. The resulting augmented weighted samples can then be used to learn a model of choice, alleviating the problems of bias in the data; as an example, we introduce SSDA twin Gaussian process regression (SSDA-TGP) model. With this model we also address the issue of data sharing, where we are able to leverage samples from certain activities (e.g., walking, jogging) to improve predictive performance on very different activities (e.g., boxing). In addition, we analyze the relationship between domain similarity and effectiveness of proposed USDA versus SSDA methods. Moreover, we propose a computationally efficient alternative to TGP (Bo and Sminchisescu 2010), and it’s variants, called the direct TGP. We show that our model outperforms a number of baselines, on two public datasets: HumanEva and ETH Face Pose Range Image Dataset. We can also achieve 8–15 times speedup in computation time, over the traditional formulation of TGP, using the proposed direct formulation, with little to no loss in performance.     

A framework for SaaS software packages evaluation and selection with virtual team and BOCR of analytic network process

Software packages evaluation and selection is one of the most important activities encountered by software as a service (SaaS) users in the high performance networked computing environment, especially for the small or medium-sized enterprises. In this paper, we propose a framework for SaaS software packages evaluation and selection by combining the virtual team (VT) and the BOCR (benefits, opportunities, costs, and risks) of the analytic network process (ANP). Different from the traditional application of the BOCR model of ANP, the proposed VT-BOCR model attempts to solve the complex ANP model and overloaded pairwise comparisons by decomposing the tasks to four parts, and performed by benefits virtual team (B-VT), opportunities virtual team (O-VT), costs virtual team (C-VT), and risks virtual team (R-VT) separately. The interactive networked media on distributed environments not only makes the proposed framework possible without the limitations of time, space, and human resources, but also can take full advantage of the talent experts who are geographically dispersed. The proposed framework also shows great potentials for aiding practitioners and researchers concerned with the cloud services.     

Robustness of globally exponential stability of delayed neural networks in the presence of random disturbances

This paper analyzes the robustness of globally exponential stability of time-varying delayed neural networks (NNs) subjected to random disturbances. Given a globally exponentially stable neural network, and in the presence of noise, we quantify how much noise intensity that the delayed neural network can remain to be globally exponentially stable. We characterize the upper bounds of the noise intensity for the delayed NNs to sustain globally exponential stability. The upper bounds of parameter uncertainty intensity are characterized by using transcendental equation. A numerical example is provided to illustrate the theoretical result.     

Memristor-based chaotic neural networks for associative memory

In chaotic neural networks, the rich dynamic behaviors are generated from the contributions of spatio-temporal summation, continuous output function, and refractoriness. However, a large number of spatio-temporal summations in turn make the physical implementation of a chaotic neural network impractical. This paper proposes and investigates a memristor-based chaotic neural network model, which adequately utilizes the memristor with unique memory ability to realize the spatio-temporal summations in a simple way. Furthermore, the associative memory capabilities of the proposed memristor-based chaotic neural network have been demonstrated by conventional methods, including separation of superimposed pattern, many-to-many associations, and successive learning. Thanks to the nanometer scale size and automatic memory ability of the memristors, the proposed scheme is expected to greatly simplify the structure of chaotic neural network and promote the hardware implementation of chaotic neural networks.     

Learning high-dimensional correspondence via manifold learning and local approximation

The recent years have witnessed a surge of interests of learning high-dimensional correspondence, which is important for both machine learning and neural computation community. Manifold learning–based researches have been considered as one of the most promising directions. In this paper, by analyzing traditional methods, we summarized a new framework for high-dimensional correspondence learning. Within this framework, we also presented a new approach, Local Approximation Maximum Variance Unfolding. Compared with other machine learning–based methods, it could achieve higher accuracy. Besides, we also introduce how to use the proposed framework and methods in a concrete application, cross-system personalization (CSP). Promising experimental results on image alignment and CSP applications are proposed for demonstration.     

半刚性钢-混凝土组合节点受弯承载力的计算

在半刚性钢 -混凝土组合框架中 ,混凝土板中的纵向钢筋增加了节点的强度和刚度 ,但是 ,传统的设计方法往往忽略纵向钢筋的作用而采用简化的计算模型。因此 ,出于经济效益考虑 ,应该根据半刚性组合节点的实际受力性能建立更加准确的计算方法。这里详细介绍了考虑混凝土板纵向钢筋作用的半刚性组合节点受弯承载力的计算方法 ,计算结果表明 ,建议的公式与试验结果吻合良好 ,可供规范制定和工程应用参考。     

基于粒子群三次样条优化的图像边缘描述算法

针对手工选点生成的三次样条曲线无法准确地描述图像边缘的问题,以粒子群三次参数样条曲线优化算法为基础对现有的图像边缘算法进行了改进。在图像边缘上取若干点作为样条曲线的型值点,利用粒子群算法对这些型值点进行优化,使得到的样条曲线对图像边缘有很好的贴合。实验表明,该算法不仅能够快速拟合样条曲线,而且优化后的样条曲线形态能较好地贴合图像边缘轮廓。