[신간의 별자리x] 우리/미술, 그리고 ‘슬픔의 박물관’

The organization of behavior: A neuropsychological theory

An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article.

The Self-Organizing Map

When building a unified vision system or gradually adding new capabilities to a system, the usual assumption is that training data for all tasks is always available. However, as the number of tasks grows, storing and retraining on such data becomes infeasible. A new problem arises where we add new capabilities to a Convolutional Neural Network (CNN), but the training data for its existing capabilities are unavailable. We propose our Learning without Forgetting method, which uses only new task data to train the network while preserving the original capabilities. Our method performs favorably compared to commonly used feature extraction and fine-tuning adaption techniques and performs similarly to multitask learning that uses original task data we assume unavailable. A more surprising observation is that Learning without Forgetting may be able to replace fine-tuning with similar old and new task datasets for improved new task performance.

Learning without Forgetting

On the truck dispatching problem

Recent breakthrough in wireless energy transfer technology has enabled wireless sensor networks (WSNs) to operate with zero-downtime through the use of mobile energy chargers (MCs), that periodically replenish the energy supply of the sensor nodes. Due to the limited battery capacity of the MCs, a significant number of MCs and charging depots are required to guarantee perpetual operations in large scale networks. Existing methods for reducing the number of MCs and charging depots treat the charging tour planning and depot positioning problems separately even though they are inter-dependent. This paper is the first to jointly consider charging tour planning and MC depot positioning for large-scale WSNs. The proposed method solves the problem through the following three stages: charging tour planning, candidate depot identification and reduction, and depot deployment and charging tour assignment. The proposed charging scheme also considers the association between the MC charging cycle and the operational lifetime of the sensor nodes, in order to maximize the energy efficiency of the MCs. This overcomes the limitations of existing approaches, wherein MCs with small battery capacity ends up charging sensor nodes more frequently than necessary, while MCs with large battery capacity return to the depots to replenish themselves before they have fully transferred their energy to the sensor nodes. Compared with existing approaches, the proposed method leads to an average reduction in the number of MCs by 64%, and an average increase of 19.7 times on the ratio of total charging time over total traveling time.

Joint Charging Tour Planning and Depot Positioning for Wireless Sensor Networks Using Mobile Chargers

Accurate travel-time prediction of public transport is essential for reliable journey planning in urban transportation systems. However, existing studies on bus travel-/arrival-time prediction often focus only on improving the prediction accuracy of a single bus trip. This is inadequate in modern public transportation systems, where a bus journey usually consists of multiple bus trips. In this paper, we investigate the problem of travel-time prediction for bus journeys that takes into account a passenger’s riding time on multiple bus trips, and also his/her waiting time at transfer points (interchange stations or bus stops). A novel framework is proposed to separately predict the riding and waiting time of a given journey from multiple datasets (i.e., historical bus trajectories, bus route, and road network), and combining the results to form the final travel-time prediction. We empirically determine the impact factors of bus riding times and develop a long short-term memory model that can accurately predict the riding time of each segment of the bus lines/routes. We also demonstrate that the waiting time at transfer points significantly impacts the total journey travel time, and estimating the waiting time is non-trivial as we cannot assume a fixed distribution waiting time. In order to accurately predict the waiting time, we introduce a novel interval-based historical average method that can efficiently address the correlation and sensitivity issues in waiting time prediction. Experiments on real-world data show that the proposed method notably outperforms six baseline approaches for all the scenarios considered.

Travel-Time Prediction of Bus Journey With Multiple Bus Trips

Pedestrian trajectory prediction is essential for collision avoidance in autonomous driving and robot navigation. However, predicting a pedestrian's trajectory in crowded environments is non-trivial as it is influenced by other pedestrians' motion and static structures that are present in the scene. Such human-human and human-space interactions lead to non-linearities in the trajectories. In this paper, we present a new spatio-temporal graph based Long Short-Term Memory (LSTM) network for predicting pedestrian trajectory in crowded environments, which takes into account the interaction with static (physical objects) and dynamic (other pedestrians) elements in the scene. Our results are based on two widely-used datasets to demonstrate that the proposed method outperforms the state-of-the-art approaches in human trajectory prediction. In particular, our method leads to a reduction in Average Displacement Error (ADE) and Final Displacement Error (FDE) of up to 55% and 61% respectively over state-of-the-art approaches.

Situation-Aware Pedestrian Trajectory Prediction with Spatio-Temporal Attention Model.

Otsu's global automatic image thresholding method has been widely employed in various real-time applications. In this paper, a novel architecture for the BCVC (Between Class Variance Computation) of Otsu's method is presented to meet these high-speed requirements. The proposed implementation employs a binary Logarithmic Conversion Unit (LCU) to eliminate the complex divisions and multiplications in the Otsu's procedure. Implementations on the FPGA (Field Programmable Gate Array) platform show that our method achieves a computation speed-up of about 2.75 times by occupying only 1/6/sup th/ of the FPGA slices required by one that relies on the direct implementation.

Siew-Kei Lam

Papers

Joint Charging Tour Planning and Depot Positioning for Wireless Sensor Networks Using Mobile Chargers

Travel-Time Prediction of Bus Journey With Multiple Bus Trips

Situation-Aware Pedestrian Trajectory Prediction with Spatio-Temporal Attention Model.

Implementing Otsu's thresholding process using area-time efficient logarithmic approximation unit

Learning heterogeneous traffic patterns for travel time prediction of bus journeys