Artificial Intelligence and Robotics Commons^™

Hydrological Drought Forecasting Using A Deep Transformer Model, Amobichukwu C. Amanambu, Joann Mossa, Yin-Hsuen Chen

University Administration Publications

Hydrological drought forecasting is essential for effective water resource management planning. Innovations in computer science and artificial intelligence (AI) have been incorporated into Earth science research domains to improve predictive performance for water resource planning and disaster management. Forecasting of future hydrological drought can assist with mitigation strategies for various stakeholders. This study uses the transformer deep learning model to forecast hydrological drought, with a benchmark comparison with the long short-term memory (LSTM) model. These models were applied to the Apalachicola River, Florida, with two gauging stations located at Chattahoochee and Blountstown. Daily stage-height data from the period 1928–2022 were …

The Road To A Human-Centred Digital Society: Opportunities, Challenges And Responsibilities For Humans In The Age Of Machines, David De Cremer, Devesh Narayanan, Andreas Deppeler, Mahak Nagpal, Jack Mcguire

Research Collection Lee Kong Chian School Of Business

The growing adoption of intelligent technologies has brought us to a crossroad. The creators of intelligent technologies are acquiring the power to influence a wide variety of outcomes that are important to human end-users. In doing so, those same intelligent technologies are being used to undermine and even actively harm the interests of those same end-users. In the absence of a recalibration, we are almost certainly headed down a path wherein intelligent technologies will primarily serve the interests of developers and owners of technology rather than humankind at large. In an attempt to push for such a recalibration, we present …

Photovoltaic Cells For Energy Harvesting And Indoor Positioning, Hamada Rizk, Dong Ma, Mahbub Hassan, Moustafa Youssef

Research Collection School Of Computing and Information Systems

We propose SoLoc, a lightweight probabilistic fingerprinting-based technique for energy-free device-free indoor localization. The system harnesses photovoltaic currents harvested by the photovoltaic cells in smart environments for simultaneously powering digital devices and user positioning. The basic principle is that the location of the human interferes with the lighting received by the photovoltaic cells, thus producing a location fingerprint on the generated photocurrents. To ensure resilience to noisy measurements, SoLoc constructs probability distributions as a photovoltaic fingerprint at each location. Then, we employ a probabilistic graphical model for estimating the user location in the continuous space. Results show that SoLoc can …

Recipegen++: An Automated Trigger Action Programs Generator, Imam Nur Bani Yusuf, Diyanah Abdul Jamal, Lingxiao Jiang, David Lo

Research Collection School Of Computing and Information Systems

Trigger Action Programs (TAPs) are event-driven rules that allow users to automate smart-devices and internet services. Users can write TAPs by specifying triggers and actions from a set of predefined channels and functions. Despite its simplicity, composing TAPs can still be challenging for users due to the enormous search space of available triggers and actions. The growing popularity of TAPs is followed by the increasing number of supported devices and services, resulting in a huge number of possible combinations between triggers and actions. Motivated by such a fact, we improve our prior work and propose RecipeGen++, a deep-learning-based approach that …

Artificial Intelligence For Natural Disaster Management, Guansong Pang

Research Collection School Of Computing and Information Systems

Artificial intelligence (AI) can leverage massive amount of diverse types of data, such as geospatial data, social media data, and wireless network sensor data, to enhance our understanding of natural disasters, their forecasting and detection, and humanitarian assistance in natural disaster management (NDM). Due to this potential, different communities have been dedicating enormous efforts to the development and/or adoption of AI technologies for NDM. This article provides an overview of these efforts and discusses major challenges and opportunities in this topic.

The Eu's Capacity To Lead The Transatlantic Alliance In Ai Regulation, Varun Roy, Vignesh Sreedhar

Claremont-UC Undergraduate Research Conference on the European Union

In the face of Chinese advances in AI in terms of technological prowess and influence, there has been a call for collaboration between the EU and the US to create a foundation for AI governance based on shared democratic beliefs. This paper maps out the EU, US, and Chinese approaches to AI development and regulation as we analyze the capacity of the US and EU to establish international standards for AI regulation through channels such as the TTC. As the EU rolled out a proportionate and risk-based approach to ensure stricter regulation for high-risk AI technologies, it laid the foundation …

Development Of A Smartphone Application As An Asset To Pavement Management Engineers, Smartp3m, Damien Stephens

Electrical Engineering Theses

An application specific multi-platform smartphone application can utilize on-board accelerometer, gyroscope, and GPS sensors, along with software derived signals from the same sensors, to sample vibrational and geolocation datasets to capture pavement distresses such as potholes when mounted in a standardized configuration in a vehicle. Several observations were made with regard to the signals obtained from the accelerometer, gyroscope, and GPS sensors, and it was determined that the raw sensor outputs are capable of sampling statistically significant datasets which can be used to distinguish pavement distress from normal driving conditions. Furthermore, an approximate sensor noise margin is established, and a …

The Human Element In The Era Of Digitalization And Automation Of Ports : A Case Study Of South Africa, Lucky Njabulo Sithole

World Maritime University Dissertations

No abstract provided.

Adaptive Multi-Scale Place Cell Representations And Replay For Spatial Navigation And Learning In Autonomous Robots, Pablo Scleidorovich

USF Tampa Graduate Theses and Dissertations

Place cells are one of the most widely studied neurons thought to play a vital role in spatial cognition. Extensive studies show that their activity in the rodent hippocampus is highly correlated with the animal’s spatial location, forming “place fields” of smaller sizes near the dorsal pole and larger sizes near the ventral pole. Despite advances, it is yet unclear how this multi-scale representation enables navigation in complex environments.

In this dissertation, we analyze the place cell representation from a computational point of view, evaluating how multi-scale place fields impact navigation in large and cluttered environments. The objectives are to …

Bounded Confidence: How Ai Could Exacerbate Social Media’S Homophily Problem, Dylan Weber, Scott Atran, Rich Davis

New England Journal of Public Policy

The advent of the Internet was heralded as a revolutionary development in the democratization of information. It has emerged, however, that online discourse on social media tends to narrow the information landscape of its users. This dynamic is driven by the propensity of the network structure of social media to tend toward homophily; users strongly prefer to interact with content and other users that are similar to them. We review the considerable evidence for the ubiquity of homophily in social media, discuss some possible mechanisms for this phenomenon, and present some observed and hypothesized effects. We also discuss how the …

Review On Ecological Construction Of Domestic High-Performance Parallel Application Software In Post Moore Era, Chunye Gong, Jie Liu, Weimin Bao, Dongmei Pan, Xinbiao Gan, Shengguo Li, Xuguang Chen, Tiaojie Xiao, Bo Yang, Ruibo Wang

Journal of System Simulation

Abstract: Domestic high performance computing (HPC) system is world-leading and the system chip architectures are in varied forms. The system operation relied on National Supercomputing Center has a good development trend. Several technical key points of domestic high-performance parallel application software are word-leading and the application supporting environment is developing fast. But industrial software and team building are facing huge challenges. In post Moore era, based on the progress of human civilization, it is necessary to promote the ecological development of parallel application software, and from the viewpoint of software products the industrial software must be aim foreign commercial software …

Research On Improved Feature Pyramid Algorithm Integrating Border Supervision Strategy, Hong Sun, Yuelan Ling, Yuxiang Zhang

Journal of System Simulation

Abstract: Aiming at the inaccurate boundary division in semantic segmentation and the existence of multi-scale targets, an improved feature pyramid algorithm fused with boundary supervision strategies is proposed. By fusing the boundary supervision strategy and the improved feature pyramid algorithm, the problems of inaccurate boundary division and the existence of multi-scale targets are sloved respectively, and an attention mechanism is added in the upsampling process to further improve the segmentation effect. The experimental results show that the algorithm can reach 58.69% and 78.59% MIOU (mean intersection over union) indicators on the Camvid and PASCAL VOC2012 data sets respectively, and has …

Real-Time Scheduling Method For Railway Passenger Station Operations Based On Digital Twin, Bisheng He, Peng Chen, Hongxiang Zhang, Gongyuan Lu, Chunhui Zhang

Journal of System Simulation

Abstract: To improve the operation scheduling of railway passenger stations and reduce the train delays, digital twin technology is used to establish a railway passenger station operation model. Through real-time data acquisition, based on the operation time prediction of random forest method, operation simulation and decision-making, a real-time scheduling method of railway passenger station operations based on digital twin is proposed, and applied in a real railway passenger station. The experimental results show that the method can effectively forecast and simulate the actual operation. Three kinds of digital twin scheduling rules have been used, which can reduce the delay time …

A Quantum Krill Herd Fusion Algorithm And Its Application, Zengxi Feng, Jintong Zhao, Shiyan Li, Yalong Yang, Haiyue Chen, Cong Zhang

Journal of System Simulation

Abstract: Aiming at the defects of krill herd algorithm and quantum evolutionary algorithm, a quantum krill herd fusion algorithm (QKH) is proposed. The algorithm uses double-chain real numbers to encode the krill position, which can speed up the convergence speed, and avoids the randomness and complexity of quantum observations. The dynamically adjusted quantum krill herd rotation phase update strategy improves the convergence accuracy, and the efficiency of determining the quantum rotation phase. The introduction of an improved quantum full interference crossover strategy can prevent the fusion algorithm from falling into a local optimum, and can improve the optimization efficienal. The …

Control Of Quadruped Robot Based On Impedance And Virtual Model, Chikun Gong, Xunwei Wu, Lipeng Yuan

Journal of System Simulation

Abstract: In order to improve the motion stability of quadruped robot, a control method based on impedance and virtual model is proposed. The force-based impedance control method is used to control the leg swing phase to realize the more accurate trajectory tracking and leg compliance control. The virtual model control method is used to control the support phase to realize the attitude control of the robot body and the stable walking of the quadruped robot. Combined with the lateral stride strategy and the yaw angle control strategy based on virtual model, a robot anti-lateral impact control method is proposed, which …

Adaptive Crowd Evacuation Simulation Model Based On Bounded Rationality Constraints, Liqiang Zhao, Mengqian Guo, Shuixiong Tang, Jinjin Tang

Journal of System Simulation

Abstract: To effectively improve the accuracy of evacuation simulation of a crowded environment, an adaptive crowd evacuation simulation model based on social force model and bounded rationality constraints is proposed. The desired direction and desired speed of the self-driving force that affects the pedestrian movement in the traditional social force model is improved. The adaptive calculation is used in the optimization of direction and speed of pedestrian in an obstacle avoidance situation. The rational route decision mechanism is proposed to describe the route selection behavior of pedestrians in a congested state more accurately. The results show that the proposed model …

Research On Modeling And Simulation Of Optimization Deployment For Cooperative Localization By Multiple Detection Sensors In Complex Environment, Gongguo Xu, Libing Cai, Peibing Du, Yu Liu

Journal of System Simulation

Abstract: Aiming at the difficulty of accurate cooperative localization by multi-sensor network in complex environment, an optimization deployment method is proposed. The GDOP evaluation index of target positioning accuracy is constructed based on PCRLB. The influence of undulating terrain, clutter jamming and illumination on sensor detection and positioning ability in complex environment is analyzed. An optimization deployment model of multi-sensor cooperative location is built and the intelligent optimization algorithm is used to quickly solve the model. Simulation results show that the proposed method can effectively improve the cooperative localization ability of multi-sensor network and can guide the multi-sensor cooperative …

Tactical Maneuver Strategy Learning From Land Wargame Replay Based On Convolutional Neural Network, Jiale Xu, Haidong Zhang, Donghai Zhao, Wancheng Ni

Journal of System Simulation

Abstract: Aiming at collecting the high valuable knowledge of action decisions in "man-in-the-loop" wargame's replay data, a method of using convolutional neural network to learn the tactical maneuver strategy model from the replay data of wargame is proposed. In this method, the tactical maneuver strategy is modeled as a classification problem of making a good choice from the target candidate locations under the influence of current situation. The key factors affecting commander's decision-making are summarized, and the basic situation features are defined, which are composed of seven attributes such as "maneuverability range and observation range". The feature dataset with positive …

Weighted Local Complexity Invariance For Time Series Classification, Yitong Li, Xiaotao Liu, Jing Liu, Kai Wu

Journal of System Simulation

Abstract: Aiming at the misclassification of existing algorithms for long or unevenly distributed time series, the local complexity information is extracted and weighted local complexity-invariant distance (WLCID) is proposed, which includes the local complexity representation model and the weighted global complexity integration model. Sliding window is used to split up time series, and combined with the complexity-invariant distance, the local complexity information can be extracted. As to the class representation model, the integration weights are quantified with the normalized cumulative between-class distance, with the perspective that the subsequence contributes more greatly with larger between-class distance. Compared with other …

A K-Modes Clustering Method Based On Maximal Information Coefficient Data Preprocessing, Mingmei Li, Chenglin Wen, Shaolin Hu

Journal of System Simulation

Abstract: The existing k-modes clustering method ignores the weak correlation of variable attributes, which often results in poor clustering performance in practical applications. A new k-modes clustering method that includes the weak correlation of attributes is proposed. Maximum information coefficient (MIC) is introduced to measure the correlation of variable attributes in the data set. The obtained MIC value is merged with the original distance to establish a new measurement method containing weak attribute correlation information to enhance the completeness of related information of variable attributes, and a more refined k-modes clustering method is established. Three different data sets are used …