Portfolio optimization problems are challenging as they contain different kinds of constrains and their complexity becomes very high when the number of assets grows. In this paper, we develop a dimension-decreasing pa...
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ISBN:
(纸本)9781450334884
Portfolio optimization problems are challenging as they contain different kinds of constrains and their complexity becomes very high when the number of assets grows. In this paper, we develop a dimension-decreasing particle swarm optimization (DDPSO) for solving multi-constrained portfolio optimization problems. DDPSO improves the efficiency of PSO for solving portfolio optimization problems with a lot of asset and it can easily handle the cardinality constraint in portfolio optimization. To improve search diversity, the dimension-decreasing method is coupled with the comprehensive learning particle swarm optimization (CLPSO) algorithm. The proposed method is tested on benchmark problems from the OR library. Experimental results show that the proposed algorithm performs well. Copyright is held by the owner/author(s).
Most intelligent diagnosis systems are developed for one or a few specific diseases, while medical specialists can diagnose all diseases of certain organ or tissue. Since it is often difficult to collect data of all d...
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This paper comes up with a SDN based On-Demand Routing Protocol, SVAO, which separates data forwarding layer and network control layer, as in SDN, to enhance the data transmission efficiency within VANETs. The Roadsid...
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This paper proposes an unobtrusive way to detect fatigue for drivers through grip forces on steering wheel. Simulated driving experiments are conducted in a refitted passenger car, during which grip forces of both han...
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We study the problem of leveraging the syntactic structure of text to enhance pre-trained models such as BERT and RoBERTa. Existing methods utilize syntax of text either in the pre-training stage or in the fine-tuning...
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Nowadays, depth cameras such Microsoft Kinect make it easier and cheaper for us to capture depth images. It becomes practical to use depth images for detection in consumer-grade products. In this paper, we propose a n...
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Industrial intelligent robots are treated as a measure of na- tional scientific level and technology innovation, and also the important symbol of high-level manufacturing, while service intelligent robots can directly...
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Industrial intelligent robots are treated as a measure of na- tional scientific level and technology innovation, and also the important symbol of high-level manufacturing, while service intelligent robots can directly affect people＇ s daily lives. The development of artificial robots in different areas is at- tracting much attention around the world. This article re- views the current situation and development of Chinese and international intelligent robot markets including industrial ro- bots and service robots. The intelligent robot technology and the classification of robots are also discussed. Finally, appli- cations of intelligent robots in various fields are concluded and the development trends and outlook of intelligent robots are explored.
Multiobjective particle swarm optimization based on decomposition (MOPSO/D) is an effective algorithm for multiobjective optimization problems (MOPs). This paper proposes a parallel version of MOPSO/D algorithm using ...
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ISBN:
(纸本)9781479975617
Multiobjective particle swarm optimization based on decomposition (MOPSO/D) is an effective algorithm for multiobjective optimization problems (MOPs). This paper proposes a parallel version of MOPSO/D algorithm using both message passing interface (MPI) and OpenMP, which is abbreviated as MO-MOPSO/D. It adopts an island model and divides the whole population into several subspecies. Based on the hybrid of distributed and shared-memory programming models, the proposed algorithm can fully use the processing power of today's multicore processors and even a cluster. The experimental results show that MO-MOPSO/D can achieve speedups of 2× on a personal computer equipped with a dual-core four-thread CPU. In terms of the quality of solutions, it can perform similarly to the serial MOPSO/D but greatly outperform NSGA-II. An additional experiment is done on a cluster, and the results show the speedup is not obvious for small-scale MOPs and it is more suitable for solving highly complex problems.
Detecting anomaly in images is challenging due to the high dimension nature of image data. While the previous learning-based anomaly detection approaches can detect a particular type of anomaly precisely, they often f...
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ISBN:
(数字)9781728113319
ISBN:
(纸本)9781728113326
Detecting anomaly in images is challenging due to the high dimension nature of image data. While the previous learning-based anomaly detection approaches can detect a particular type of anomaly precisely, they often fail in detecting multiple types of abnormal samples *** identify the two specific types of anomalies that can be precisely detected by either compress-based or reconstruction-based anomaly detection approaches, named global anomaly and local anomaly. We then propose Glad, an anomaly detector that can precisely detect both of them at the same time. Glad adopts a joint approach combining the density estimation and auto-encoder. Firstly, it designs a multimodal density estimation model to derive the latent representation probability for identifying the global anomaly. Then, it uses structural similarity to measure the reconstruction loss for characterizing local anomaly. Finally, both anomalies can be diagnosed according to the joint density of latent representation and reconstruction loss. Experimental results on public benchmark datasets demonstrate that Glad outperforms the state-of-the-art methods significantly.
PLSA(Probabilistic Latent Semantic Analysis) is a popular topic modeling technique for exploring document collections. Due to the increasing prevalence of large datasets, there is a need to improve the scalab.lity of ...
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