This paper investigates an energy-efficient transmission scheme in cognitive radio networks, where primary users (PUs) may reoccupy the spectrum when secondary users (SUs) is transmitting data. We aim to maximize the energy efficiency by jointly optimizing the transmission power, the fusion rule threshold and the sensing/transmission durations. Firstly, it is derived that for a given fusion rule threshold, the objective function is unimodal while only one optimization parameter varies. Furthermore, we provide the corresponding closed-form expressions of the optimal data transmission duration and transmission power. Finally, the globally unimodal property is proven, and hence, the globally optimal point can be easily found with the proposed algorithm based on the alternating direction method (ADM). Numerical simulation results show that our proposed scheme is much better than the existing ones. similar to [6], the optimization of sensing duration, with cooperative spectrum sensing based on k-out-of-N fusion rule, is considered to maximize energy efficiency by using golden section search, while transmission power is given a fixed valueHowever, all above research has not considered the optimization of the data transmission duration. If the transmission duration is too short, the throughput will be hard to improve, otherwise, it will result in more energy consumption for data transmission. Besides, the primary user's (PU's) activity is not investigated that transforms from OFF state to ON state during secondary user's (SU's) data transmission. Traditionally, the throughput of secondary system is simply measured without regard to the effective transmission duration, in this case where PUs randomly reoccupy the spectrum when SU transmits data. In addition, the longer transmission time may increase the risk of coming back of PU. It is noteworthy that the Cognitive radio (CR) is regarded as a promising technology significance of optimizing transmission time is unassailable to effectively resolve the contradiction between the low utiliza-in view of its contributions to the balance between throughput tion of the rare spectrum resource and increasing demanding and energy efciency in CRNs. Both work [9] and [10] take of wireless communication applications [1]. Recently, much PUs' state transformation into account. In [9], the maximum attention has been paid on energy efficiency in cognitive radio channel efficiency of SUs are designed by utilizing statisnetworks (CRNs) [2]-[7] and [9]-[11] which is vital to trade off tical information of the licensed channel occupancy. Based the capacity and energy consumption for the battery-powered on the PU's traffic pattern, collision-throughput problem is wireless devices.formulated to maximize the throughput in [10]. Work [9] and In our previous work [4] and [5], energy-efficiency mea-[10] have not concerned the energy and capacity tradeoff. sured with the "throughput per Joule" metric in CRNs are energy efficient scheme is investigated by jointly optimizing studied. Work [4] proposes ...
As the market demand for environmentally friendly synthetic leather products has increased, water-based synthetic leather manufacturing technology and product performance have made great progress. Along with the explosive growth of coffee grounds generated by urban consumers in their daily lives, research on the sustainable reuse of coffee grounds has gradually become a trend in the field. This study discusses the method of preparing environmentally friendly water-based synthetic leather that reuses coffee grounds and is assessed by standardized physical tests for friction color fastness, Martindale abrasion resistance, breathability and moisture permeability, softness, and peel strength. The results have indicated that sustainable coffee-ground synthetic leather fully meets the performance of aqueous synthetic leather for apparel and luggage, with even some performance indicators exceeding existing aqueous synthetic leather, which is an innovative and sustainable product that can be applied to the apparel industry in the future. Its development and application in the textile field will provide research ideas with the transformation of environmental problems into new opportunities.
In order to achieve low-cost, high-efficiency customized footwear design and service to meet the individual needs of customers, quickly producing various types of shoe last based on different needs has become the key to customization. According to the moulding characteristics of the longitudinal profile of the shoe last, the key characteristic parameters and morphogenesis logic are found. Combined with the parameterized nonlinear thinking feature, the parameter logic is constructed. Rhino’s parametric design plug-in Grasshopper is used to develop the shoe last design process. The automatic moulding process of the sectional view, combined with the parametric design of the shoe last and the cross-section of the shoe last, can form a quick custom shoe last method. A professional and rapid design method is formed to enhance the design and production efficiency of customized shoe lasts.
With the further development of technology, providing technical support for the forecast and development of fashion design trends through artificial intelligence has gradually become a reality. In order to enable users to obtain more accurate design trend materials in a shorter time, this paper presents a ‘searching by image’ model of clothing trends based on the ResNet50 model. Through the targeted collection of data and the rational construction of algorithms, the system can retrieve and output more image materials with relevant design features for the existing image materials for the development of design trends as well as the development and design of styles. By comparing the system with Google’s and Taobao’s ‘searching by image’ system, it is concluded that this system has high efficiency, high accuracy and high correlation.
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