Journal of Intelligent & Fuzzy Systems - Volume 41, issue 6

Authors: Zheng, Qinghe | Yang, Mingqiang | Wang, Deqiang | Tian, Xinyu | Su, Huake

Article Type: Research Article

Abstract: Throughout the wireless communication network planning process, efficient signal reception power estimation is of great significance for accurate 5 G network deployment. The wireless propagation model predicts the radio wave propagation characteristics within the target communication coverage area, making it possible to estimate cell coverage, inter-cell network interference, and communication rates, etc. In this paper, we develop a series of features by considering various factors in the signal transmission process, including the shadow coefficient, absorption coefficient in test area and base station area, distance attenuation coefficient, density, azimuth angle, relative height and ground feature index coefficient. Then we design a …quantile regression neural network to predict reference signal receiving power (RSRP) by feeding the above features. The network structure is specially constructed to be generalized on various complex real environments. To prove the effectiveness of proposed features and deep learning model, extensive comparative ablation experiments are applied. Finally, we have achieved the precision rate (PR), recall rate (RR), and inadequate coverage recognition rate (PCRR) of 84.3%, 78.4%, and 81.2% on the public dataset, respectively. The comparison with a series of state-of-the-art machine learning methods illustrates the superiority of the proposed method. Show more

Keywords: RSRP prediction, neural network, correlation analysis, shadowing effect, information entropy

DOI: 10.3233/JIFS-202430

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 6067-6078, 2021

Authors: Revathy, K. | Thenmozhi, K. | Praveenkumar, Padmapriya | Amirtharajanr, Rengarajan

Article Type: Research Article

Abstract: In Today’s pandemic situation, ‘Spectrum accessing and smart usage’ is the sacred Mantra uttered by every individual citizen in the world. Work from home for techies, online classes for students, games for kids, webinar for teaching fraternity etc., are going almost on indoor coverage without any limit in pace because of the smart spectrum coverage by the network service providers. This paper provides an add-on facility to the existing wireless infrastructure to provide a better user experience in this highly regrettable routine. In this paper, a cognitive domain unused spectrum holes are efficiently handled by (i) adaptive spectrum management technique; …(ii) Fuzzy Inference System based spectrum administration and (iii) Hybrid Cognitive Femtocell approaches based on the user demand and their applications. The proposed integrated cognitive femtocell and Fuzzy-based approach reduces the indoor coverage problems and enhances the throughput of the macrocell users by allowing adaptive spectrum management based on the demand, thereby eliminating spectrum underlay and overlay problems during critical conditions. In cognitive femtocell networks, the access points are prepared and installed with Cognitive Radio which can determine spectrum dynamically by macrocells and nearby Femto Access Points. It adjusts its radiating parameters to evade the macrocells’ interferences and the neighbouring femtocells, thereby maximising the spectrum band’s overall utility. Show more

Keywords: Spectrum, cognitive, fuzzy, femtocell

DOI: 10.3233/JIFS-202540

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 6079-6088, 2021

Authors: Mi, Xiao | Wang, Xue-ping

Article Type: Research Article

Abstract: This paper investigates minimal solutions of fuzzy relation inequalities with addition-min composition. It first shows the conditions that an element is a minimal solution of the inequalities, and presents the conditions that the inequalities have a unique minimal solution. It then proves that every solution of the inequalities has a minimal one and proposes an algorithm to searching for a minimal solution with computational complexity O  (n 2 ) where n is the number of unknown variables of the inequalities. This paper finally describes all minimal solutions of the inequalities.

Keywords: Fuzzy relation inequality, addition-min composition, minimal solution, algorithm

DOI: 10.3233/JIFS-202590

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 6089-6095, 2021

Authors: Shi, Jiawen | Li, Hong | Wang, Chiyu | Pang, Zhicheng | Zhou, Jiale

Article Type: Research Article

Abstract: Short text matching is one of the fundamental technologies in natural language processing. In previous studies, most of the text matching networks are initially designed for English text. The common approach to applying them to Chinese is segmenting each sentence into words, and then taking these words as input. However, this method often results in word segmentation errors. Chinese short text matching faces the challenges of constructing effective features and understanding the semantic relationship between two sentences. In this work, we propose a novel lexicon-based pseudo-siamese model (CL2 N), which can fully mine the information expressed in Chinese text. Instead of …utilizing a character-sequence or a single word-sequence, CL2 N augments the text representation with multi-granularity information in characters and lexicons. Additionally, it integrates sentence-level features through single-sentence features as well as interactive features. Experimental studies on two Chinese text matching datasets show that our model has better performance than the state-of-the-art short text matching models, and the proposed method can solve the error propagation problem of Chinese word segmentation. Particularly, the incorporation of single-sentence features and interactive features allows the network to capture the contextual semantics and co-attentive lexical information, which contributes to our best result. Show more

Keywords: Short text matching, Chinese text, semantic relationship, pseudo-siamese model, multi-granularity information

DOI: 10.3233/JIFS-202592

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 6097-6109, 2021

Authors: Tilli, Tuomo | Espinosa-Leal, Leonardo

Article Type: Research Article

Abstract: Online advertisements are bought through a mechanism called real-time bidding (RTB). In RTB, the ads are auctioned in real-time on every webpage load. The ad auctions can be of two types: second-price or first-price auctions. In second-price auctions, the bidder with the highest bid wins the auction, but they only pay the second-highest bid. This paper focuses on first-price auctions, where the buyer pays the amount that they bid. This research evaluates how multi-armed bandit strategies optimize the bid size in a commercial demand-side platform (DSP) that buys inventory through ad exchanges. First, we analyze seven multi-armed bandit algorithms on …two different offline real datasets gathered from real second-price auctions. Then, we test and compare the performance of three algorithms in a production environment. Our results show that real data from second-price auctions can be used successfully to model first-price auctions. Moreover, we found that the trained multi-armed bandit algorithms reduce the bidding costs considerably compared to the baseline (naïve approach) on average 29%and optimize the whole budget by slightly reducing the win rate (on average 7.7%). Our findings, tested in a real scenario, show a clear and substantial economic benefit for ad buyers using DSPs. Show more

Keywords: Bid shading, bid optimization, multi-armed bandits, reinforcement learning

DOI: 10.3233/JIFS-202665

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 6111-6125, 2021

Authors: Surekha, S. | Zia Ur Rahman, Md.

Article Type: Research Article

Abstract: In medical telemetry networks, cognitive radio technology is mostly used to avoid licensed spectrum underutilization and by providing access to unlicensed spectrum users without causing interference to primary users, this concept is widely used in development of smart hospitals and smart cities. In medical telemetry networks frequency spectrum concept is used for providing treatment to patients who are far away from hospitals. In cognitive radios, spectrum sensing concept is used in which energy detection method is mostly used because it is simple to implement. While measuring health care environments using cognitive radios probability detection, false alarm probability and threshold parameters …are calculated. In this paper for identifying spectrum holes in spectrum sensing using energy detection, distributed diffusion non-negative least mean square algorithm is proposed. It gives better results compared to energy detection concept alone in terms of probability detection converged earlier. If number of nodes are increasing probability detection is decreased from one and move towards left and its SNR is around 1.5-2 dB with proposed method. Hence simulation results give better results in terms of sensing ability while measuring patient condition. Show more

Keywords: Cognitive sensors, energy detection, spectrum holes, spectrum sensing, medical telemetry

DOI: 10.3233/JIFS-202673

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 6127-6136, 2021

Authors: Xiong, Pingping | Xiao, Lushuang | Liu, Yuchun | Yang, Zhuo | Zhou, Yifan | Cao, Shuren

Article Type: Research Article

Abstract: Faced with serious growing global warming problem, it is important to predict carbon emissions. As there are a lot of factors affecting carbon emissions, a novel multi-variable grey model (GM(1,N) model) based on linear time-varying parameters discrete grey model (TDGM(1,N)) has been established. In this model, linear time-varying function is introduced into the traditional model, and dynamic optimization of fixed parameters which can only be used for static analysis is carried out. In order to prove the applicability and effectiveness of the model, this paper compared the model with the traditional model and simulated the carbon emissions of Anhui Province …from 2005 to 2015. Carbon emissions in the next two years are also predicted. The results show that the TDGM(1,N) model has better simulation effect and higher prediction accuracy than the traditional GM(1,N) model and the multiple regression model(MRM) in practical application of carbon emissions prediction. In addition, the novel model of this paper is also used to predict the carbon emissions in 2018–2020 of Anhui Province. Show more

Keywords: Linear time-varying parameters, grey system theory, multi-variable model, carbon emissions, forecasting

DOI: 10.3233/JIFS-202711

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 6137-6148, 2021

Authors: Pan, Xingguang | Wang, Lin | Huang, Chengquan | Wang, Shitong | Chen, Haiqing

Article Type: Research Article

Abstract: In feature weighted fuzzy c-means algorithms, there exist two challenges when the feature weighting techniques are used to improve their performances. On one hand, if the values of feature weights are learnt in advance, and then fixed in the process of clustering, the learnt weights might be lack of flexibility and might not fully reflect their relevance. On the other hand, if the feature weights are adaptively adjusted during the clustering process, the algorithms maybe suffer from bad initialization and lead to incorrect feature weight assignment, thus the performance of the algorithms may degrade the in some conditions. In order …to ease these problems, a novel weighted fuzzy c-means based on feature weight learning (FWL-FWCM) is proposed. It is a hybrid of fuzzy weighted c-means (FWCM) algorithm with Improved FWCM (IFWCM) algorithm. FWL-FWCM algorithm first learns feature weights as priori knowledge from the data in advance by minimizing the feature evaluation function using the gradient descent technique, then iteratively optimizes the clustering objective function which integrates the within weighted cluster dispersion with a term of the discrepancy between the weights and the priori knowledge. Experiments conducted on an artificial dataset and real datasets demonstrate the proposed approach outperforms the-state-of-the-art feature weight clustering methods. The convergence property of FWL-FWCM is also presented. Show more

Keywords: Feature weight learning, fuzzy c-means, priori knowledge, gradient descent

DOI: 10.3233/JIFS-202779

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 6149-6167, 2021

Authors: Yanjun, Xiao | Yameng, Zhang | Nan, Gao | Kai, Peng | Wei, Zhou | Weiling, Liu

Article Type: Research Article

Abstract: In the current industrial production process, waste heat of low quality is seriously wasted. In order to effectively recover low-quality waste heat, the research group developed a small energy conversion device –Roots power machine. On this basis, the research group designed a low-quality waste heat efficient utilization system with the equipment as the core and successfully applied it to low-quality waste heat recovery. However, in the actual operation process, the system can not run stably due to the occasional fluctuation of the input gas source. In view of this, after the study of waste heat recovery system, the fluctuation of …gas source can be controlled by different grades according to the degree of change. Fuzzy rules also divide variables into different grades to solve problems, and fuzzy control can convert continuous changes of airflow into discrete changes, which greatly reduces the complexity of the control system. Therefore, the research group proposed a control strategy based on fuzzy PID. The simulation results show that the adjustment time of fuzzy PID is within 7 s, and the adjustment effect is obviously better than that of traditional PID. The experimental results show that the speed deviation under the condition of air source fluctuation is within the speed fluctuation rate (±5%), and the speed deviation under the condition of sudden disturbance load is within the steady speed adjustment rate (±3.5%), both of which meet the requirements of indexes. Therefore, the fuzzy PID control strategy can further improve the stability of output speed, reduce airflow pulsation, and provide the possibility for grid-connected power generation. Show more

Keywords: Efficient utilization, fuzzy PID, low-quality waste heat, roots power machine, variable coupling

DOI: 10.3233/JIFS-202784

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 6169-6179, 2021

Authors: Zhao, Yun-Tao | Gan, Lei | Li, Wei-Gang | Liu, Ao

Article Type: Research Article

Abstract: The path planning of traditional spot welding mostly uses manual teaching method. Here, a new model of path planning is established from two aspects of welding length and welding time. Then a multi-objective grey wolf optimization algorithm with density estimation (DeMOGWO) is proposed to solve multi-object discrete problems. The algorithm improves the coding method and operation rules, and sets the density estimation mechanism in the environment update. By comparing with other five algorithms on the benchmark problem, the simulation results show that DeMOGWO is competitive which takes into account both diversity and convergence. Finally, the DeMOGWO algorithm is used to …solve the model established of path planning. The Pareto solution obtained can be used to guide the welding sequence of body-in-white(BIW) workpieces. Show more

Keywords: Spot welding robot, multi-objective grey wolf optimization algorithm, density estimation, path planning

DOI: 10.3233/JIFS-202810

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 6181-6189, 2021