An Introduction to Complex Systems Science and Its ApplicationsRead the full article
Complexity publishes original research and review articles across a broad range of disciplines with the purpose of reporting important advances in the scientific study of complex systems.
Chief Editor, Prof Sayama, is currently researching complex dynamical networks, human and social dynamics, artificial life, and interactive systems while working at Binghamton University, State University of New York.
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Forecasting Volatility of Stock Index: Deep Learning Model with Likelihood-Based Loss Function
Volatility is widely used in different financial areas, and forecasting the volatility of financial assets can be valuable. In this paper, we use deep neural network (DNN) and long short-term memory (LSTM) model to forecast the volatility of stock index. Most related research studies use distance loss function to train the machine learning models, and they gain two disadvantages. The first one is that they introduce errors when using estimated volatility to be the forecasting target, and the second one is that their models cannot be compared to econometric models fairly. To solve these two problems, we further introduce a likelihood-based loss function to train the deep learning models and test all the models by the likelihood of the test sample. The results show that our deep learning models with likelihood-based loss function can forecast volatility more precisely than the econometric model and the deep learning models with distance loss function, and the LSTM model is the better one in the two deep learning models with likelihood-based loss function.
Research on Deviation Detection of Belt Conveyor Based on Inspection Robot and Deep Learning
The deviation of the conveyor belt is a common failure that affects the safe operation of the belt conveyor. In this paper, a deviation detection method of the belt conveyor based on inspection robot and deep learning is proposed to detect the deviation at its any position. Firstly, the inspection robot captures the image and the region of interest (ROI) containing the conveyor belt edge and the exposed idler is extracted by the optimized MobileNet SSD (OM-SSD). Secondly, Hough line transform algorithm is used to detect the conveyor belt edge, and an elliptical arc detection algorithm based on template matching is proposed to detect the idler outer edge. Finally, a geometric correction algorithm based on homography transformation is proposed to correct the coordinates of the detected edge points, and the deviation degree (DD) of the conveyor belt is estimated based on the corrected coordinates. The experimental results show that the proposed method can detect the deviation of the conveyor belt continuously with an RMSE of 3.7 mm, an MAE of 4.4 mm, and an average time consumption of 135.5 ms. It improves the monitoring range, detection accuracy, reliability, robustness, and real-time performance of the deviation detection of the belt conveyor.
A Fuzzy Clustering Logic Life Loss Risk Evaluation Model for Dam-Break Floods
A dam is a complex and important water-retaining structure. Once the dam is broken, the flood will cause immeasurable damage to the lives and properties of the downstream people, so it is particularly important to have the dam risk management. Since the dam-break flood is a severe-consequence low-frequency event, the corresponding fatalities caused by it are difficult to estimate due to the lack of relevant data and poor data continuity. This paper analyzes the direct and indirect factors affecting the risk of life loss in dam failures and studies the characteristics, distribution rules, and membership functions of each factor. An adaptive differential evolution method is constructed through an optimization of the mutation factors and cross factors of the differential evolution method. This proposed evaluation method also combines with the fuzzy clustering iterative method that is capable of evaluating the similarity of life loss in dam accidents. The effectiveness of the proposed method is verified by 16 dam-break case studies.
An Integral Sliding Mode Control of Uncertain Chaotic Systems via Disturbance Observer
This paper proposes an integral sliding mode control (ISMC) method of a class of uncertain chaotic systems with saturation inputs. Firstly, fuzzy logic system (FLS) is used to estimate the unknown nonlinear function. Then, a disturbance observer is constructed to estimate a compound disturbance, which contains the external disturbance, the error of saturation input and control output, and the fuzzy estimation error. Subsequently, a proposed integral sliding mode controller can ensure that all signals of the closed-loop system are ultimately bounded, and based on the dynamic system of the integral sliding mode variable itself, the ultimate bound of the tracking error can be estimated. Simulation results show that the proposed ISMC method is more effective than the traditional ISMC method.
Content-Enhanced Network Embedding for Academic Collaborator Recommendation
It is meaningful for a researcher to find some proper collaborators in complex academic tasks. Academic collaborator recommendation models are always based on the network embedding of academic collaborator networks. Most of them focus on the network structure, text information, and the combination of them. The latent semantic relationships exist according to the text information of nodes in the academic collaborator network. However, these relationships are often ignored, which implies the similarity of the researchers. How to capture the latent semantic relationships among researchers in the academic collaborator network is a challenge. In this paper, we propose a content-enhanced network embedding model for academic collaborator recommendation, namely, CNEacR. We build a content-enhanced academic collaborator network based on the weighted text representation of each researcher. The content-enhanced academic collaborator network contains intrinsic collaboration relationships and latent semantic relationships. Firstly, the weighted text representation of each researcher is obtained according to its text information. Secondly, a content-enhanced academic collaborator network is built via the similarity of the weighted text representation of researchers and intrinsic collaboration relationships. Thirdly, each researcher is represented as a latent vector using network representation learning. Finally, top- similar researchers are recommended for each target researcher. Experiment results on the real-world datasets show that CNEacR achieves better performance than academic collaborator recommendation baselines.
Multiobjective Optimization of Large-Scale EVs Charging Path Planning and Charging Pricing Strategy for Charging Station
With the increasing number of electric vehicles (EVs), the charging demand of EVs has brought many new research hotspots, i.e., charging path planning and charging pricing strategy of the charging stations. In this paper, an integrated framework is proposed for multiobjective EV path planning with varied charging pricing strategies, considering the driving distance, total time consumption, energy consumption, charging fee such factors, while the charging pricing strategy is designed based on the objectives of maximizing the total revenues of the charging stations and balancing the profits of the charging stations. First, the energy consumption model of EVs, the M/M/S queuing model of charging stations, and the charging model of charging piles are established. A novel charging path planning algorithm is proposed based on bidirectional Martins’ algorithm, which can assist EV users to select charging stations and plan charging paths. Then, a particle swarm optimization (PSO) algorithm is applied to solve the optimal solution of charging station pricing designation. Finally, the method proposed in the paper is simulated on the street map of Shenzhen to verify the efficacy of the multiobjective charging path planning for EVs and the feasibility of the charging pricing strategy.