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Research at the nexus of maritime management and operational performance suggests that lean management may benefit from sociotechnical systems theory (STS) in terms of long-term business performance. The purpose of this study is to examine the extent to which lean management practices can improve operational performance. This study aims to assess the relationships between lean policy, lean practices, operational performance, and business performance in the context of container shipping. The examined data are acquired from 212 respondents in Taiwan, and a structural equation model is employed to evaluate the hypotheses. The results show that lean policy and practice positively impact both lean social practices and lean technical practices. Lean social practices and lean technical practices are positively related to operational performance. Operational performance is associated with business performance. In particular, lean policy has a direct relationship with operational performance. Based on lean policy is easily able to directly improve its lean social practices and lean technical practices, proving lean policy can strongly push organizations towards further enhanced performance. Although it will be ups and downs along the way in terms of business performance, improving long-term business value and cost advantage is very importance. Thus, this article contributes to a more nuanced understanding of the effect of lean practices and discusses practical implications for container shipping companies.

As the population worldwide continues to age and the percentage of elderly people continues to increase, falls have been become the second leading cause of death from accidental or unintentional injuries. Although many imaging sensing devices have been used to detect falls for elderly people, most involve using the Internet to transfer images taken by a camera to a large back-end server, which performs the necessary calculations; however, algorithm limitations and computational complexity may cause bottlenecks in image outflow, and the image transfer can result in privacy problems. To address these problems, in this paper, an artificial intelligence (AI) fall detection method is proposed that operates using an edge computing architecture, called the pose estimation-based fall detection methodology (PEFDM), which is based on a human body posture recognition technique. The proposed PEFDM can effectively reduce the computational load, runs smoothly on mainstream edge computing systems and possesses artificial intelligence computing capabilities. By using edge computing, the privacy and upload bandwidth issues caused by image outflow can be eliminated. Experiments with real humans show that the PEFDM can detect falls for elderly people with a recognition accuracy of up to 98.1%.

This article proposes a methodology for diagnosing faults in oil-immersed power transformers that considers correlation as a random variable and models the power transformer diagnosis problem as a hypothesis testing problem. Based on conventional estimation and detection theory, a novel diagnosis methodology for oil-immersed power transformer faults is developed by minimizing the maintenance cost of an oil-immersed power transformer. Unlike previous work, this is the first work to consider the optimization of the maintenance cost. Moreover, the proposed methodology is verified with a benchmark test based on 950 data sets of real historic records, and the results show that the accuracy of failure detection with this approach can reach 92.85% while the maintenance cost is minimized. Finally, the experimental results indicate that the proposed methodology displays promising performance and can be used as a tool for the diagnosis of incipient faults in oil-immersed power transformers.

This paper proposes an AIoT based smart mini marine fish tank system, which provides consumers feeding a more convenient way to enhance the comfort of seawater aquatic life and chances of survival. The proposed system can automatically adjust many related parameters such as marine fish water quality, water temperature, oxygen content in water, feeding, changing the water, and real-time water monitoring. Moreover, real-time monitoring of water status can identify abnormal biological illness, resolved in favor, and take care of the difficult marine fish pets care problems.

Most individuals involved in traffic accidents receive assistance from drivers, passengers, or other people. However, when a traffic accident occurs in a sparsely populated area or the driver is the only person in the vehicle and the crash results in loss of consciousness, no one will be available to send a distress message to the proper authorities within the golden window for medical treatment. Considering these issues, a method for detecting high-speed head-on and single-vehicle collisions, analyzing the situation, and raising an alarm is needed. To address such issues, this paper proposes a deep learning-based Internet of Vehicles (IoV) system called DeepCrash, which includes an in-vehicle infotainment (IVI) telematics platform with a vehicle self-collision detection sensor and a front camera, a cloud-based deep learning server, and a cloud-based management platform. When a head-on or single-vehicle collision is detected, accident detection information is uploaded to the cloud-based database server for self-collision vehicle accident recognition, and a related emergency notification is provided. The experimental results show that the accuracy of traffic collision detection can reach 96% and that the average response time for emergency-related announcements is approximately 7 s.

According to the hospital's old adult wards' clinical needs, we propose a deep learning edge computing-based two-stage early bed-exit warning and bedside fall notification system for aged patients with a high risk of falls. The proposed system can effectively help the hospital reduce bed fall incidence and increase bed-exit and bedside fall notification rates. As a result, the rate of injury from falling bed-exit can be reduced.

In this paper, we propose a deep learning-based intelligent calorie management system, named iBuffet, in which deep learning and IoT technologies are integrated for calorie management. The proposed iBuffet system comprises intelligent buffet table modules, a mobile device application, and a cloud-based platform. The purpose of the proposed iBuffet system is to improve the eating health of institutional personnel. By recording daily meal records and management to improve the health of institutional personnel. Moreover, the related information will be sent to the cloud-based platform to calculate a calorie value, allowing users to manage their health.

This paper proposes a fall risk prediction system, which adopts two cameras and an AI server to obtain the images of the 3D human skeleton torso activities. We use the Hidden Markov model (HMM) for the movement changes of the joint points of the human skeleton torso in 3D space to evaluate the probability of falling of subjects. The experimental results show that the proposed system can efficiently predict the risk of falling for consumers with unbalanced gait.

This paper proposes a deep learning-based intelligent anti-collision system, which can avoid an accident caused by the improper opening of the car door. The proposed system consists of an intelligent rearview mirror, an on-board diagnostics-II (OBD-II) bridge, and an in-vehicle infotainment (IVI) platform. The proposed system can recognize the rear approaching motorcycles and prevent the car from opening the car door improperly to avoid a collision accident between a motorcycle and the car door.

A deep learning-based intelligent scalp diagnosis system will be demonstrated. This live demonstration system is composed of a scalp detector, a mobile device app, an AI computing server, and a cloud-based service platform. During the live demonstration, this system can inference five scalp symptoms based on deep-learning technology for helping the treatment of hair salon or therapy services to achieve the purpose of scalp healthcare.

This paper proposes a wearable devices-based home sports recording system, which is composed of wearable devices, a mobile device app, and a cloud-based management platform. The proposed system can analyze five regular sports behaviors (walking, jogging (running), hula hoops, sit-ups, and push-up) taken by the sportsperson during a day. These five regular sports behaviors can be converted into calorie consumption so that the sportsmen can further understand how much exercise they need to reach the target of calories burned in a day.

This paper proposes an intelligent maintenance and repair informing system for solar power generation equipment, which the machine-learning techniques adopted to improve the time for manually judging the abnormal operation of the equipment. Thereby the maintenance efficiency of the equipment can be enhanced to reduce solar power generation losses. Based on the current environmental sensing data and power generation data of equipment, the proposed system infers whether the status of the solar power generation of each equipment is abnormal and its possible causes. Moreover, the maintenance and repair informing procedures can be processed and decided within 15 minutes.

This paper proposes a mobile device-based hairy scalp diagnosis system, which adopts deep learning techniques. The proposed system is composed of a mobile device, a cloud-based AI training server, and a cloud-based database. Moreover, the proposed system can detect and diagnose four common hairy scalp symptoms: dandruff, folliculitis, hair loss, and oily hair. As a result, the experimental results showed that the recognition accuracy of the proposed system could achieve up to 95.59%.

This paper proposes an artificial intelligence (AI) edge computing-based wearable assistive device for assisting zebra-crossing walking of the visually impaired people. The proposed assistive device is composed of a pair of smart glasses, a walking cane, and a waist-mounted box. When the visually impaired pedestrian prepares to cross the zebra-crossing, thus the visually impaired pedestrian will receive the current traffic light signal message. The visually impaired guidance voice service will be provided via Bluetooth (BT). Moreover, when the walking offset occurs on the zebra-crossing, then the visually impaired pedestrian will be reminded through the voice prompt of the earphone.

In this paper, we propose a 3D limb trajectory tracking system, named BodyTracker, which is based on deep learning techniques for assisting rehabilitation. The proposed BodyTracker is composed of two limbs recognition camera and a deep learning training server. As a result, the proposed system can provide medical personnel with an effective way to assess the effectiveness of rehabilitation for stroke patients.

This paper proposes an intelligent liquid waste barrels monitoring system, named iCAP system, which is based on the Internet of things (IoT) technologies for detecting abnormal opening and dumping of the liquid waste barrels. The proposed system is composed of an inbound/outbound areas management system, intelligent screw caps (named iCAP), and an online cloud-based platform. The inbound/outbound areas management system can provide inbound/outbound areas status management for the liquid waste barrels during transportation. If the intelligent screw cap mounted on the liquid barrel is unscrewed or closed during outdoor transportation, the related messages of such a situation will be uploaded to the proposed online could platform via the SigFox low power wide area networks (LPWAN) wireless communication technology to provide an effectively management and monitoring of liquid waste container transportation. As a result, the proposed system can avoid the problem of environmental pollution, which is caused by illegal waste dumping in the waste treatment process.

This paper proposes a wireless communication scheme, named AudioNet, which is based on the audio frequency (AF) domain modulation/demodulation techniques for wireless personal area networks (WPAN). The proposed AudioNet includes a transmitter and a receiver. The frequency modulation scheme of the transmitter adopts frequency-shift keying (FSK). The Goertzel's algorithm is used to implement the FSK demodulation in the receiver. As a result, the experimental results showed that the proposed AudioNet can be transmitted the text string messages over audio at 32bps.

This paper proposes an in-vehicle safety aided system, which adopts DC power-line communication (DC PLC) as a main transmission channel. The proposed system is composed of a voice-to-text (VTT) module, two embedded computer modules, two DC PLC transceiver modules, a text and video message display module, and a distance measurement module. Moreover, the proposed system is successfully installed and tested in a real vehicle. The experimental results showed that the proposed system can have a data of distance to the adjacent rear vehicle, and display front view video with alerting messages for drivers of rear vehicles. As a result, the proposed system can be applied to alert the drivers of rear vehicles to achieve the purpose of road safety.

This paper proposes a deep learning based wearable medicines recognition system for visually impaired people. The proposed system is composed of a pair of wearable smart glasses, a wearable waist-mounted drug pills recognition device, a mobile device application, and a cloud-based management platform. The proposed system uses deep learning technology to identify drug pills to avoid taking wrong drugs. The experimental results show that the accuracy of the proposed system has reached up to 90% that can really be achieved the purpose of correct medication for visually impaired people.

Recently, the popularity of automated and unmanned restaurants has increased. Due to the absence of staff, there is no direct perception of the customers' impressions in order to find out what their experiences with the restaurant concept are like. For this purpose, this paper presents a rating system based on facial expression recognition with pre-trained convolutional neural network (CNN) models. It is composed of an Android mobile application, a web server, and a pre-trained AI-server. Both the food and the environment are supposed to be rated. Currently, three expressions (satisfied, neutral and disappointed) are provided by the scoring system.

In this paper, we propose an intelligent assistance system for visually impaired/blind people, which is composed of wearable smart glasses, an intelligent walking stick, mobile devices application, and on-line information platform. When visually impaired/blind people wear the proposed smart glasses and holding the proposed intelligent walking stick, thus the obstacles can be detected. If a visually impaired/blind person is fall down, then the related information (GPS, fall down, etc.) will be recorded and uploaded to the on-line information platform. Related information can also be viewed by the proposed mobile devices application.

Today, there is an increase in the number of elderly people who want to start using computers and the Internet. However, elderly people are hindered by limited education, limited knowledge of digital information, words they do not know, and not knowing how to use the computer keyboard and mouse. The setbacks and frustration they face in learning to use the computer causes them to feel computer anxiety. This paper proposes a computer-learning-assisted system equipped with wrist-wearable devices to help elderly computer learners as they perform computer learning tasks. This system, named the WristEyesystem, can discern and analyze learners’ attitudes, reactions, and behaviors as they participate in computer literacy classes. In the WristEyesystem, a kinematic sensor attached to a student’s wrist can detect differences in wrist orientation and vertical acceleration and determine which learning computer operations are in process, i.e., directing the mouse, hitting the keyboard, idle, and random undirected movement of the mouse. Moreover, a remote backend server receives the detected signal from the wearable unit by a wireless sensor network and then analyzes the corresponding computer learning effectiveness to produce results in graphic and score form to an instructor who can use this information to better tailor his lessons and activities to the needs of the learners.

This paper proposes an intelligent scalp inspection and diagnosis system based on the deep learning techniques for caring hair scalp health. The proposed system can automatically recognize the status of the user's scalp. Moreover, we can continuously increase in the number of samples to enhance the accuracy rate by adopting deep learning techniques. The proposed system consists of a scalp detector, an app running on a tablet, and a cloud management platform. The scalp detector will be connected with the tablet via Wi-Fi wireless network. Thus, a scalp photo can be captured via the proposed scalp detector. The scalp photo will be recognized by scalp detector, and the recognized result of the scalp will also be sent and displayed to the tablet. As a result, we can get the quantitative data on the scalp, including bacteria, allergies, dandruff, grease, and hair loss. Moreover. The experimental results showed that the accuracy can be achieved 90.909%.

This paper proposes a deep learning based Internet of vehicles system, which consists of an in-vehicle infotainment telematics platform with collisions detection sensors, a cloud based deep learning training server, and a web-based service platform. The proposed system implements deep learning based techniques to achieve traffic accident collisions detection and to provide related emergency announcement. As a result, the experimental result showed that the accuracy of traffic accident collisions detection can be achieved up to 96% and the average response time for related emergency announcement is approximately 7 seconds.

In order to effectively manage the information exchange between telematics equipment and various terminal equipment in a vehicle. This paper proposes a platform, which uses an in-vehicle infotainment (IVI) system as a heterogeneous networks interconnection gateway. The proposed platform integrates the power line communication (PLC), the visible light communication (VLC), the CAN Bus, and the other related heterogeneous networks. Hence, the platform can be used as a heterogeneous networks coordinator, which is a key role of the intelligent transportation system applications. As a result, with the proposed platform, a flexible network can be organized to achieve vehicle-to-vehicle (V2V) communication, and therefore to provide traffic safety related application services.

Today, we are forward to meeting an older people society in the world. The elderly people have become a high risk of dementia or depression. In recent years, with the rapid development of internet of things (IoT) techniques, it has become a feasible solution to build a system that combines IoT and cloud techniques for detecting and preventing the elderly dementia or depression. This paper proposes an IoT-based elderly behavioral difference warning system for early depression and dementia warning. The proposed system is composed of wearable smart glasses, a BLE-based indoor trilateration position, and a cloud-based service platform. As a result, the proposed system can not only reduce human and medical costs, but also improve the cure rate of depression or delay the deterioration of dementia.

Driving safety is the most important issue for drivers all the time. In general, so far there is no direct communication channel between two vehicles. Sometimes a driver may alert the front car driver by pressing horn or flashing headlight. However, the car driver cannot convey any message to the vehicle behind him. In this paper, we propose a mechanism to provide passing text messages to the rear car driver through a display system. By integrating distance measurement, voice-to-text (VTT) and display modules. The mechanism is implemented and therefore can have the driving safety aided function.

In this paper, we propose an intelligent motorcycle helmet, called /-Helmet, which integrates IR sensors with an image sensor and adopts the image recognition methodology to recognize rear big vehicles. Two detection modes (day/night) are designed for the purpose of the image recognition accuracy. Experimental results showed that the proposed /-Helmet can successfully be achieved image recognition of the license plate for rear big truck/bus. The recognition accuracy rate achieves about 70% (night) to 75% (day). Therefore, the proposed /-Helmet can real-time provide related intimations for avoiding rear big truck/bus collisions.

This paper proposes a Fast Fourier Transform (FFT)-based monitoring system which is applied to measure, monitor, and analyze the vibrations of the high-speed spindle. The piezoelectric sensor based accelerometer can real-time measure the signals of the vibrations when the high-speed spindle is vibrated. These signals of the vibrations are sent to a cloud-based platform via wireless communication techniques. The meaningful characteristic of vibrations are captured by the FFT technique. Moreover, a database is built as sample patterns (all kinds of damage and normal operations), according to these related characteristic. Therefore, it can assist us to recognize normal/abnormal vibrations. The behaviors of abnormal vibration occurred in the high-speed spindle can also be predicted for early maintenance.

This paper proposes an in-vehicle power line communication (PLC) system which provides an in-vehicle communication for the video and vehicle messages transmission as a reliable alternative networking medium. The proposed in-vehicle PLC system adopts an existing in-vehicle 12-V battery power system, which means we don't need an external DC power supply for in-vehicle communication equipment to achieve the purpose of video and vehicle message transmission for driving safety.

In this paper, we propose a wireless battery charging system which the power transfer position can be automatically moved to the optimal charging placement. The proposed charging system is based on the electromagnetic inductive power transfer technique which is a short distance power transfer system. As a result, we have successfully finished the implementation of the proposed wireless battery charging system. The experimental results showed that this work can achieve the purpose of the optimal charging placement with a shortest power transfer distance.

This paper proposes a wireless router based on Zigbee networks for home automation systems applications, which is capable of smart devices monitoring, controlling, and enabling. When a smart device is joined the domain of the home automation system, the system will automatically require the related basic information of this device. Then this smart device can be monitored and controlled. Moreover, the user can plan automatically inter-enabling devices. As a result, it can increase devices interconnectability and reduce the design complexity of the console UI.

This paper proposes a cargo management system, named i-Logistics, which consists of an electronic tape (e-tape), a QR code scan APP, an in-vehicle infotainment system (a vehicular telematics device), and a cloud-based platform. The dedicated Polyvinylidene fluoride (PVDF) sensors are mounted on the electronic tape for monitoring cargos. The QR code scan APP is used to burn a matching dedicated ID from QR code of Cargo, to electronic tape via BLE. The related messages of the dedicated ID and cargo status (such as broken and loss) are also sent to in-vehicle infotainment (IVI) system by BLE. Furthermore, the abnormal notified messages will be real-time recorded and uploaded to the cloud-based platform via 3G/4G networks when aberrant status of cargos is occurred. As a result, the proposed system can efficiently help to understanding the status of cargos and giving necessary assistance, during truck transportation process.