A 5 GHz LC-VCO frequency synthesizer for unlicensed band of WiMAX

This paper presents a low power and low phase noise CMOS integer-N frequency synthesizer based on the charge-pump Phase Locked Loop (PLL) topology. The frequency synthesizer can be used for IEEE 802.16 unlicensed band of WiMAX (World Interoperability for Microwave Access). The operation frequency of the proposed design is ranged from 5.13 to 5.22 GHz. The proposed Voltage-Controlled Oscillator (VCO) achieves low power consumption and low phase noise. The high speed divider is implemented by an optimal extended true single phase clock (E-TSPC) prescaler. It can achieve higher operating frequency and lower power consumption. A new frequency divider is also proposed to eliminate the hardware overhead of the S counter in the conventional programmable divider. The proposed frequency synthesizer consists of a phase-frequency detector (PFD), a charge pump, a low-pass loop filter, a VCO, and a frequency divider. The simulated phase noise of the proposed VCO is −121.6 dBc/Hz at 1 MHz offset from the carrier frequency. The proposed frequency synthesizer consumes 13.1 mW. The chip with an area of 1.048 × 1.076 mm² is fabricated in a TSMC 0.18 μm CMOS 1P6M technology process.     

Robust spatial flood vulnerability assessment for Han River using fuzzy TOPSIS with α-cut level set

This study aims to improve the general flood vulnerability approach using fuzzy TOPSIS based on α-cut level sets which can reduce the uncertainty inherent in even fuzzy multi-criteria decision making process. Since fuzzy TOPSIS leads to a crisp closeness for each alternative, it is frequently argued that fuzzy weights and fuzzy ratings should be in fuzzy relative closeness. Therefore, this study used a modified α-cut level set based fuzzy TOPSIS to develop a spatial flood vulnerability approach for Han River in Korea, considering various uncertainties in weights derivation and crisp data aggregation. Two results from fuzzy TOPSIS and modified fuzzy TOPSIS were compared. Some regions which showed no or small ranking changes have their centro-symmetric distributions, while other regions whose rankings varied dynamically, have biased (anti-symmetric) distributions. It can be concluded that α-cut level set based fuzzy TOPSIS produce more robust prioritization since more uncertainties can be considered. This method can be applied to robust spatial vulnerability or decision making in water resources management.     

Exploiting temporal information in Web search

Time plays important roles in Web search, because most Web pages contain temporal information and a lot of Web queries are time-related. How to integrate temporal information in Web search engines has been a research focus in recent years. However, traditional search engines have little support in processing temporal-textual Web queries. Aiming at solving this problem, in this paper, we concentrate on the extraction of the focused time for Web pages, which refers to the most appropriate time associated with Web pages, and then we used focused time to improve the search efficiency for time-sensitive queries. In particular, three critical issues are deeply studied in this paper. The first issue is to extract implicit temporal expressions from Web pages. The second one is to determine the focused time among all the extracted temporal information, and the last issue is to integrate focused time into a search engine. For the first issue, we propose a new dynamic approach to resolve the implicit temporal expressions in Web pages. For the second issue, we present a score model to determine the focused time for Web pages. Our score model takes into account both the frequency of temporal information in Web pages and the containment relationship among temporal information. For the third issue, we combine the textual similarity and the temporal similarity between queries and documents in the ranking process. To evaluate the effectiveness and efficiency of the proposed approaches, we build a prototype system called Time-Aware Search Engine (TASE). TASE is able to extract both the explicit and implicit temporal expressions for Web pages, and calculate the relevant score between Web pages and each temporal expression, and re-rank search results based on the temporal-textual relevance between Web pages and queries. Finally, we conduct experiments on real data sets. The results show that our approach has high accuracy in resolving implicit temporal expressions and extracting focused time, and has better ranking effectiveness for time-sensitive Web queries than its competitor algorithms.     

Personalized identification of abdominal wall hernia meshes on computed tomography

An abdominal wall hernia is a protrusion of the intestine through an opening or area of weakness in the abdominal wall. Correct pre-operative identification of abdominal wall hernia meshes could help surgeons adjust the surgical plan to meet the expected difficulty and morbidity of operating through or removing the previous mesh. First, we present herein for the first time the application of image analysis for automated identification of hernia meshes. Second, we discuss the novel development of a new entropy-based image texture feature using geostatistics and indicator kriging. Third, we seek to enhance the hernia mesh identification by combining the new texture feature with the gray-level co-occurrence matrix feature of the image. The two features can characterize complementary information of anatomic details of the abdominal hernia wall and its mesh on computed tomography. Experimental results have demonstrated the effectiveness of the proposed study. The new computational tool has potential for personalized mesh identification which can assist surgeons in the diagnosis and repair of complex abdominal wall hernias.     

Nonlinear multiobjective model-predictive control scheme for wastewater treatment process

A nonlinear multiobjective model-predictive control (NMMPC) scheme, consisting of self-organizing radial basis function (SORBF) neural network prediction and multiobjective gradient optimization, is proposed for wastewater treatment process (WWTP) in this paper. The proposed NMMPC comprises a SORBF neural network identifier and a multiple objectives controller via the multi-gradient method (MGM). The SORBF neural network with concurrent structure and parameter learning is developed as a model identifier for approximating on-line the states of WWTP. Then, this NMMPC optimizes the multiple objectives under different operating functions, where all the objectives are minimized simultaneously. The solution of optimal control is based on the MGM which can shorten the solution time. Moreover, the stability and control performance of the closed-loop control system are well studied. Numerical simulations reveal that the proposed control strategy gives satisfactory tracking and disturbance rejection performance for WWTP. Experimental results show the efficacy of the proposed method.     

Smart grid coordination in building HVAC systems: EMPC and the impact of forecasting

Energy consumption by heating, ventilation, and air conditioning (HVAC) systems exhibits a clear correlation with electricity prices. The method of economic model predictive control (EMPC) can be used in conjunction with thermal energy storage (TES) to time-shift power consumption away from periods of high demand to periods of low energy cost. Dynamic electricity pricing and weather condition forecasts can be readily incorporated within this methodology. Unfortunately, the receding horizon nature of this control strategy makes it very susceptible to the quality of the forecasts used. To this end, the development and implementation of several forecasting methods will be discussed. Finally, the EMPC performance of these methods will be assessed on a simple building example using active TES.     

An overview of aqueous-phase catalytic processes for production of hydrogen and alkanes in a biorefinery

In this overview we discuss how aqueous-phase catalytic processes can be used to convert biomass into hydrogen and alkanes ranging from C₁ to C₁₅. Hydrogen can be produced by aqueous-phase reforming (APR) of biomass-derived oxygenated hydrocarbons at low temperatures (423–538 K) in a single reactor over supported metal catalysts. Alkanes, ranging from C₁ to C₆ can be produced by aqueous-phase dehydration/hydrogenation (APD/H). This APD/H process involves a bi-functional pathway in which sorbitol (hydrogenated glucose) is repeatedly dehydrated by a solid acid (SiO₂–Al₂O₃) or a mineral acid (HCl) catalyst and then hydrogenated on a metal catalyst (Pt or Pd). Liquid alkanes ranging from C₇ to C₁₅ can be produced from carbohydrates by combining the dehydration/hydrogenation process with an upstream aldol condensation step to form C–C bonds. In this case, the dehydration/hydrogenation step takes place over a bi-functional catalyst (4 wt.% Pt/SiO₂–Al₂O₃) containing acid and metal sites in a specially designed four-phase reactor employing an aqueous inlet stream containing the large water-soluble organic reactant, a hexadecane alkane sweep stream, and a H₂ inlet gas stream. The aqueous organic reactant become more hydrophobic during dehydration/hydrogenation, and the hexadecane sweep stream removes these species from the catalyst as valuable products before they go on further to form coke.     

基于无线疲劳监测系统的铝合金材料疲劳损伤研究

铝合金材料具有重量轻、强度高等特点,是国民经济发展的重要基础材料,在诸多领域(如飞行器设计与制造、建筑装饰等)中占有十分重要的地位。然而,其抗疲劳性能较差,在交变荷载作用下容易产生疲劳裂纹和发生疲劳破坏。提出了一种基于无线疲劳监测系统的铝合金材料疲劳损伤研究方法,实时监测了铝合金构件的疲劳应变,并实时分析了构件的疲劳损伤状况,为研究铝合金材料的疲劳性能提供了可借鉴性的方法。     

Stability analysis and controller design of interval type-2 fuzzy systems with time delay

The type-2 fuzzy models can handle the system uncertainties directly based on the type-2 fuzzy sets. In this paper, the Takagi–Sugeno fuzzy model approach is extended to the stability analysis and controller design for interval type-2 (IT2) fuzzy systems with time-varying delay. Delay-dependent robust stability criteria are developed in terms of linear matrix inequalities by using the improvement technique of free-weighting matrices. Less conservative results are obtained by considering the information contained in the footprint of uncertainty. Finally, two simulation examples are presented to illustrate the effectiveness of the theoretical results. One is provided to show the merits of the proposed method, the other based on the continuous stirred tank reactor model is given to illustrate the design processes of IT2 fuzzy controller for a nonlinear system with parameter uncertainties.     

A fully autonomous kernel-based online learning neural network model and its application to building cooling load prediction

Building cooling load prediction is critical to the success of energy-saving measures. While many of the computational models currently available in the industry have been developed for this purpose, most require extensive computer resources and involve lengthy computational processes. Artificial neural networks (ANNs) have recently been adopted for prediction, and pioneering works have confirmed the feasibility of this approach. However, users are required to predetermine an ANN model’s parameters. This hinders the applicability of the ANN approach in actual engineering problems, as most engineers may be unfamiliar with soft computing. This paper proposes a fully autonomous kernel-based neural network (AKNN) model for noisy data regression prediction. No part of the model’s mechanism requires human intervention; rather, it self-organises its structure according to the training samples presented. Unlike the other existing autonomous models, the AKNN model is an online learning model. It is particularly suitable for online steps-ahead prediction. In this paper, we benchmark the AKNN model’s performance according to other ANN models. It is also successfully applied to predicting the cooling load of a commercial building in Hong Kong. The occupancy areas and concentration of carbon dioxide inside the building are successfully adopted to mimic the building’s internal cooling load. Training data was adopted from actual measurements taken inside the building. Its results show reasonable agreement with actual cooling loads.